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The Mediating Role of Student Engagement in the Relationship between Teacher and Digital Support and Learner Satisfaction in Blended Learning Environments at Higher Education

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Title: The Mediating Role of Student Engagement in the Relationship between Teacher and Digital Support and Learner Satisfaction in Blended Learning Environments at Higher Education
Language: English
Authors: Xiao-Feng Kenan Kok (ORCID 0000-0002-8679-7641), Ching Yee Pua, Shermain Puah, Oran Zane Devilly, Peng Cheng Wang, Eric Chern-Pin Chua
Source: British Educational Research Journal. 2025 51(3):1313-1341.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 29
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Mediation Theory, Learner Engagement, Teacher Student Relationship, Electronic Learning, Student Satisfaction, Blended Learning, Higher Education, College Students, College Faculty, COVID-19, Pandemics, Foreign Countries, Professional Autonomy, Digital Literacy, Technology Uses in Education
Geographic Terms: Singapore
DOI: 10.1002/berj.4123
ISSN: 0141-1926
1469-3518
Abstract: Given the emergence of blended learning as the dominant mode of learning at university in a post-COVID-19 world, the need to examine students' perceptions of blended learning is increasingly becoming more important. This study examined the mediating role of student engagement in the relationship between the types of support (i.e., teacher, digital) and learner satisfaction in blended learning environments. A sample of 674 Year 1 and Year 2 students from a public university in Singapore participated in this study. Structural equation modelling showed that (1) teacher autonomy and digital relatedness support predicted agentic engagement, (2) digital competence and relatedness support predicted emotional engagement, (3) emotional engagement predicted all learner satisfaction facets except for learner-instructor interaction and (4) agentic engagement predicted all learner satisfaction facets except for learner-technology interaction. Of the four dimensions of student engagement, only emotional and agentic engagement mediated the relationships between various dimensions of support and learner satisfaction. Overall, these findings highlight the importance of emotionally engaging students and imbuing a sense of agency in them to enhance the relationships between the types of support and learner satisfaction.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1475462
Database: ERIC
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  Value: <anid>AN0186252682;bed01jun.25;2025Jul01.05:03;v2.2.500</anid> <title id="AN0186252682-1">The mediating role of student engagement in the relationship between teacher and digital support and learner satisfaction in blended learning environments at higher education </title> <p>Given the emergence of blended learning as the dominant mode of learning at university in a post‐COVID‐19 world, the need to examine students' perceptions of blended learning is increasingly becoming more important. This study examined the mediating role of student engagement in the relationship between the types of support (i.e., teacher, digital) and learner satisfaction in blended learning environments. A sample of 674 Year 1 and Year 2 students from a public university in Singapore participated in this study. Structural equation modelling showed that (<reflink idref="bib1" id="ref1">1</reflink>) teacher autonomy and digital relatedness support predicted agentic engagement, (<reflink idref="bib2" id="ref2">2</reflink>) digital competence and relatedness support predicted emotional engagement, (<reflink idref="bib3" id="ref3">3</reflink>) emotional engagement predicted all learner satisfaction facets except for learner–instructor interaction and (<reflink idref="bib4" id="ref4">4</reflink>) agentic engagement predicted all learner satisfaction facets except for learner–technology interaction. Of the four dimensions of student engagement, only emotional and agentic engagement mediated the relationships between various dimensions of support and learner satisfaction. Overall, these findings highlight the importance of emotionally engaging students and imbuing a sense of agency in them to enhance the relationships between the types of support and learner satisfaction.</p> <p>Keywords: blended learning; digital support; higher education; learner satisfaction; student engagement; teacher support</p> <p>Key insights What is the main issue that the paper addresses?This paper uses structural equation modelling to explore how student engagement mediates the relationship between the types of support (i.e., teacher, digital) and learner satisfaction in higher education blended learning environments. The study aims to provide insights into the interplay between teacher and digital support, student engagement and learner satisfaction. What are the main insights that the paper provides?This study provides empirical support for the hypothesised structural model that illustrates the relationships between teacher and digital support, student engagement and learner satisfaction. The findings indicate that only emotional and agentic engagement served as mediators in the relationship between different dimensions of support and learner satisfaction.</p> <hd id="AN0186252682-2">INTRODUCTION</hd> <p>Following the emergence of the COVID‐19 pandemic in 2020, blended learning has become the dominant mode of university education, leading to new research on specific pedagogical adaptations (Syska & Pritchard, [<reflink idref="bib78" id="ref5">78</reflink>]). These studies have explored the use of immersive technologies (Colreavy‐Donelly et al., [<reflink idref="bib31" id="ref6">31</reflink>]), the development of critical thinking skills (Sudirman et al., [<reflink idref="bib77" id="ref7">77</reflink>]), approaches to sustainability (Chen, [<reflink idref="bib20" id="ref8">20</reflink>]; Do Lago et al., [<reflink idref="bib33" id="ref9">33</reflink>]) and strategies for enhancing student engagement (Buckley et al., [<reflink idref="bib14" id="ref10">14</reflink>]). With the transition to blended learning emerging as the 'new normal' for many higher education institutions, this shift is expected to accelerate in the coming years (Bruggeman et al., [<reflink idref="bib13" id="ref11">13</reflink>]). While much has been written on blended learning, a clear consensus on its definition remains elusive (Buhl‐Wiggers et al., [<reflink idref="bib15" id="ref12">15</reflink>]). Despite that, blended learning is increasingly being described as the intentional integration of online and traditional face‐to‐face class activities, where part of the face‐to‐face instruction is replaced by online interactions in a pedagogically meaningful way (Dziuban et al., [<reflink idref="bib36" id="ref13">36</reflink>]).</p> <p>Blended learning affords opportunities for improving the efficiency of education, transforming education and enhancing student learning outcomes (Buhl‐Wiggers et al., [<reflink idref="bib15" id="ref14">15</reflink>]). In terms of efficiency, blended learning offers more cost‐effective, time‐efficient, sustainable and scalable delivery methods, such as the usage of technology to create instructional activities that were previously difficult to organise, hence optimising the teaching and learning process (Boelens et al., [<reflink idref="bib9" id="ref15">9</reflink>]; Spanjers et al., [<reflink idref="bib75" id="ref16">75</reflink>]). Where transformation is concerned, blended learning provides the opportunity for introducing new activities, processes or delivery methods. For instance, blended learning has the potential to engage industry partners more actively in student learning, creating a new triad of teacher, student and industry partnerships, rather than the traditional teacher–student dyad (Duffy & Ney, [<reflink idref="bib35" id="ref17">35</reflink>]). Experts from various industries can participate as guest lecturers, contribute to online discussions or mentor students remotely through virtual platforms, without the logistical constraints of in‐person sessions (Vaughan, [<reflink idref="bib85" id="ref18">85</reflink>]). Blended learning has also been shown to positively impact student learning outcomes, including attitude, performance and learning achievement across various countries (Cao, [<reflink idref="bib17" id="ref19">17</reflink>]). For example, students' attitudes and emotions towards blended education were found to significantly influence their satisfaction with learning activities and their perceived learning performance (Banihashem et al., [<reflink idref="bib6" id="ref20">6</reflink>]).</p> <p>Learner satisfaction with blended learning, however, requires greater independence, with students taking a more autonomous and active role in the learning (Broadbent & Poon, [<reflink idref="bib11" id="ref21">11</reflink>]). In other words, learners are expected to self‐regulate their learning and engage with the course material, their teachers and peers during blended learning. To facilitate self‐regulated learning in blended learning, it is therefore crucial to design an online (and face‐to‐face) environment that enhances students' sense of autonomy, competence and relatedness to improve the effectiveness of blended learning (Chiu, [<reflink idref="bib22" id="ref22">22</reflink>]). For the delivery of effective blended learning, it is important to foster strong student engagement, as student engagement is a prerequisite for successful learning (Lam et al., [<reflink idref="bib59" id="ref23">59</reflink>]). Student engagement has often been referred to as the 'holy grail of learning' (Sinatra et al., [<reflink idref="bib73" id="ref24">73</reflink>], p. 1) and has been described as a key objective of curriculum design (Halverson et al., [<reflink idref="bib48" id="ref25">48</reflink>]; Spring et al., [<reflink idref="bib76" id="ref26">76</reflink>]).</p> <p>Although various studies have examined the potential of blended learning to boost learner engagement (e.g., Halverson et al., [<reflink idref="bib47" id="ref27">47</reflink>]), more studies are required to understand how learners are engaged in blended contexts (Halverson & Graham, [<reflink idref="bib46" id="ref28">46</reflink>]). Furthermore, there is a scarcity of studies investigating the role of non‐teacher support, such as digital support, in promoting student engagement within blended learning environments (Chiu, [<reflink idref="bib22" id="ref29">22</reflink>]). Despite the acknowledged importance of teacher support, digital support and student engagement in blended learning, most existing research has been conducted in high school and middle school settings rather than in higher education (Chiu, [<reflink idref="bib22" id="ref30">22</reflink>]). The focus on student engagement research at the upper secondary level likely stems from its perceived importance in driving educational reforms and supporting key interventions (Fredricks et al., [<reflink idref="bib42" id="ref31">42</reflink>]). Unlike high and middle school settings, higher education environments present distinct challenges, such as a greater emphasis on self‐regulation, due to the more autonomous nature of university education (Broadbent & Poon, [<reflink idref="bib11" id="ref32">11</reflink>]). Additionally, there is a lack of research analysing the relationships between students' perceptions of blended learning platforms, their engagement experiences and their overall satisfaction in blended learning environments (Gao et al., [<reflink idref="bib44" id="ref33">44</reflink>]; Syska & Pritchard, [<reflink idref="bib78" id="ref34">78</reflink>]). To address these gaps, the present study aims to examine the relationships between teacher support, digital support, student engagement and learner satisfaction, focusing on undergraduate students at a Singapore‐based university. Through this study, we endeavour to offer valuable insights to enhance support systems, boost student engagement and improve learner satisfaction.</p> <hd id="AN0186252682-3">LITERATURE REVIEW</hd> <p></p> <hd id="AN0186252682-4">Teacher support and student engagement</hd> <p>Student engagement in blended learning may require teacher and digital support. Student engagement refers to students' active participation in effective educational practices and their dedication to educational goals and learning, which are crucial pathways to achieving educational outcomes such as academic achievement (Christenson et al., [<reflink idref="bib29" id="ref35">29</reflink>]). Conceptualised as a multidimensional concept, student engagement encompasses four dimensions, namely, behavioural, cognitive, emotional and agentic engagement (Chiu, [<reflink idref="bib22" id="ref36">22</reflink>]; Fredricks, [<reflink idref="bib40" id="ref37">40</reflink>]; Reeve, [<reflink idref="bib68" id="ref38">68</reflink>]). Although interrelated, these components are thought to be distinct (Christenson et al., [<reflink idref="bib29" id="ref39">29</reflink>]; Reeve, [<reflink idref="bib68" id="ref40">68</reflink>]). Behavioural engagement refers to students' active participation and involvement in learning activities both inside and outside the classroom, while emotional engagement involves students' affective responses to their peers, teachers, learning activities and the school environment, focusing on specific emotions like happiness, excitement, boredom and anxiety (Fredricks et al., [<reflink idref="bib41" id="ref41">41</reflink>]). Cognitive engagement is characterised by students' mental effort to complete tasks via a deep, self‐regulated and strategic approach to learning, as opposed to superficial methods (Chiu, [<reflink idref="bib22" id="ref42">22</reflink>]). In contrast, agentic engagement pertains to the proactive efforts to actively contribute to the learning and teaching process (Reeve, [<reflink idref="bib68" id="ref43">68</reflink>]). Student engagement in blended learning environments differs from traditional settings in that such environments afford more flexibility in participation, such as contributing to online discussions outside regular class hours, which are not possible in traditional classrooms (Halverson & Graham, [<reflink idref="bib46" id="ref44">46</reflink>]). Although blended learning offers flexibility, maintaining student engagement remains a persistent challenge (Boelens et al., [<reflink idref="bib9" id="ref45">9</reflink>]; Gao et al., [<reflink idref="bib44" id="ref46">44</reflink>]). The increased flexibility of time and space in blended learning environments expands the psychological and communication space, known as the transactional distance (Moore, [<reflink idref="bib63" id="ref47">63</reflink>]). However, as transactional distance grows, meaningful social interaction becomes more challenging, spontaneous engagement declines and self‐regulation becomes crucial for success. Hence, there are challenges in (a) determining effective strategies to facilitate meaningful interaction, (b) supporting students in their learning processes and (c) nurturing a sense of belonging within these environments (Boelens et al., [<reflink idref="bib9" id="ref48">9</reflink>]).</p> <p>To promote student engagement, teacher practices play an important role as they can encourage student autonomy, offer opportunities for student collaboration, provide feedback on student work, be interpersonally involved and use multimedia tools to enhance the learning experience (Cao, [<reflink idref="bib17" id="ref49">17</reflink>]; Hartnett, [<reflink idref="bib49" id="ref50">49</reflink>]). According to self‐determination theory (SDT), teaching practices are categorised into three dimensions: (<reflink idref="bib1" id="ref51">1</reflink>) autonomy support (autonomy); (<reflink idref="bib2" id="ref52">2</reflink>) structure (competence); and (<reflink idref="bib3" id="ref53">3</reflink>) involvement (relatedness) (Lietaert et al., [<reflink idref="bib61" id="ref54">61</reflink>]; Vansteenkiste et al., [<reflink idref="bib83" id="ref55">83</reflink>]; Vollet et al., [<reflink idref="bib86" id="ref56">86</reflink>]). These dimensions correspond to the three fundamental psychological needs of humans: autonomy, competence and relatedness (Ryan & Deci, [<reflink idref="bib71" id="ref57">71</reflink>]). SDT is a highly validated and comprehensive framework that offers valuable insights into motivation and human functioning and has important attributes that are vital in every learning environment (Ryan & Deci, [<reflink idref="bib71" id="ref58">71</reflink>]). Importantly, these basic psychological needs are inherent, essential, universal and psychological (Ryan & Deci, [<reflink idref="bib71" id="ref59">71</reflink>]; Vansteenkiste et al., [<reflink idref="bib82" id="ref60">82</reflink>]).</p> <p>The need for autonomy is characterised by the desire for choice and freedom in one's learning process, making students aware that they are in control of their educational journey (Ryan & Deci, [<reflink idref="bib71" id="ref61">71</reflink>]). Translating theory into practice, an SDT‐driven learning design involves teachers adopting a motivational teaching approach that values student input, thereby supporting students' basic need for autonomy (Ryan & Deci, [<reflink idref="bib71" id="ref62">71</reflink>]; Vansteenkiste et al., [<reflink idref="bib82" id="ref63">82</reflink>]). Autonomy can be fostered by offering choices, emphasising the value of learning materials, using encouraging language, encouraging and facilitating students to pursue their personal goals, posing questions that support autonomy, all tailored towards the students' behaviour (Assor et al., [<reflink idref="bib4" id="ref64">4</reflink>]; Reeve & Jang, [<reflink idref="bib69" id="ref65">69</reflink>]). With teachers' autonomy support, students demonstrate better concentration and time management (behavioural engagement; Vansteenkiste et al., [<reflink idref="bib84" id="ref66">84</reflink>]), enjoy their lessons more (emotional engagement; Skinner et al., [<reflink idref="bib74" id="ref67">74</reflink>]) and communicate their learning agendas more effectively with teachers (agentic engagement; Reeve, [<reflink idref="bib68" id="ref68">68</reflink>]). Additionally, autonomy affords students more freedom to select their learning goals, potentially leading to increased cognitive engagement (Bedenlier et al., [<reflink idref="bib7" id="ref69">7</reflink>]).</p> <p>The fundamental need for competence refers to feeling confident in one's abilities, which is closely linked to confidence in the outcomes of the learning process (Ryan & Deci, [<reflink idref="bib71" id="ref70">71</reflink>]). In providing structure or competence support, teachers can clearly communicate expectations regarding student behaviour (Lietaert et al., [<reflink idref="bib61" id="ref71">61</reflink>]) by providing supportive information, setting clear task expectations, offering positive and constructive feedback and occasionally surprising students with rewards (Chiu, [<reflink idref="bib22" id="ref72">22</reflink>]; Chiu & Hew, [<reflink idref="bib26" id="ref73">26</reflink>]; Ryan & Deci, [<reflink idref="bib71" id="ref74">71</reflink>]). Teachers can also employ a scaffolding approach when integrating technology into their teaching methods (Chiu & Lim, [<reflink idref="bib27" id="ref75">27</reflink>]). By developing a well‐structured learning environment, students can feel competent, efficient and challenged, motivating them to engage cognitively with the content (Skinner et al., [<reflink idref="bib74" id="ref76">74</reflink>]). When students perceive a structured learning environment, they develop a sense of mastery over the subject matter, become more motivated to participate in course activities, contributing to improved behavioural and emotional engagement (Reeve, [<reflink idref="bib68" id="ref77">68</reflink>]).</p> <p>Finally, the need for relatedness involves the desire to connect with others and feel a sense of belonging to a group or community (Ryan & Deci, [<reflink idref="bib71" id="ref78">71</reflink>]). It involves teachers offering emotional and motivational support, including pedagogical care, closeness, acceptance and assistance (Vollet et al., [<reflink idref="bib86" id="ref79">86</reflink>]). Behaviours such as warmth, affection and enjoyment are known to cultivate close and caring teacher–student relationships (Skinner et al., [<reflink idref="bib74" id="ref80">74</reflink>]). Teachers can also promote trust among students in collaborative learning settings and small‐group discussions (Alamri et al., [<reflink idref="bib1" id="ref81">1</reflink>]; Xie & Ke, [<reflink idref="bib87" id="ref82">87</reflink>]), making students feel welcomed, secure, capable and autonomous, leading to increase engagement and internalisation of their learning experiences (Reeve, [<reflink idref="bib68" id="ref83">68</reflink>]; Ryan & Deci, [<reflink idref="bib71" id="ref84">71</reflink>]). By meeting the need for relatedness, the strong teacher–student relationships encourage active participation (behavioural engagement), foster positive attitudes towards course activities (emotional engagement), boost confidence in tackling challenging tasks (cognitive engagement) and empower students to advocate for their learning needs (agentic engagement) (Reeve, [<reflink idref="bib68" id="ref85">68</reflink>]; Vollet et al., [<reflink idref="bib86" id="ref86">86</reflink>]).</p> <p>Overall, providing teacher support that encourages autonomy, competence and relatedness can foster student engagement. Research by Chiu ([<reflink idref="bib22" id="ref87">22</reflink>]) on grade 11 students indicates that teacher perceived support for autonomy, competence and relatedness is strongly associated with the four dimensions of student engagement (i.e., behavioural, cognitive, emotional, agentic), suggesting that teachers who satisfied these three basic psychological needs were more likely to foster student learning in blended environments. In more recent research examining the effect of teachers sharing learning analytics reports with university students on their basic psychological needs and satisfaction, Ameloot et al. ([<reflink idref="bib3" id="ref88">3</reflink>]) observed higher levels of relatedness and positive feelings of autonomy in both the experimental and control conditions. This suggests that learning analytics‐based feedback that is learner‐centred is particularly valuable for fostering interaction between students and teachers, emphasising the significance of autonomy and flexibility (Yilmaz & Yilmaz, [<reflink idref="bib89" id="ref89">89</reflink>]). In their systematic review, Yang et al. ([<reflink idref="bib88" id="ref90">88</reflink>]) found that teachers' autonomy support significantly enhances student engagement. Practically, this involves implementing effective strategies, such as collaborative rule‐setting, establishing procedures and defining interaction goals (Baker et al., [<reflink idref="bib5" id="ref91">5</reflink>]). Notably, fostering purposeful, interactive and supportive dialogue between teachers and students is essential for promoting engagement (Böheim et al., [<reflink idref="bib10" id="ref92">10</reflink>]). Given that the empirical evidence consistently reflects relations between teacher support and student engagement, we will hypothesise the following:</p> <hd id="AN0186252682-5">H1</hd> <p>Teacher support (i.e., autonomy, competence, relatedness) positively predicts student engagement (i.e., behavioural, cognitive, emotional, agentic).</p> <hd id="AN0186252682-6">Digital support and student engagement</hd> <p>Digital support refers to the creation of technological learning environments that cater to students' inherent needs (Chiu, [<reflink idref="bib22" id="ref93">22</reflink>]). It is about designing support using multiple modalities to spur students to actively engage with the content (Chiu & Churchill, [<reflink idref="bib25" id="ref94">25</reflink>]; Schnotz & Bannert, [<reflink idref="bib72" id="ref95">72</reflink>]), as the provision of a single modality is less stimulating and engaging. This entails students being given the freedom to independently select their own preferred learning method, thereby giving them a sense of agency and control over their own actions and learning processes.</p> <p>On the other hand, digital competence support considers the learner's expertise (Chiu et al., [<reflink idref="bib28" id="ref96">28</reflink>]; Kalyuga, [<reflink idref="bib53" id="ref97">53</reflink>]), where various instructional formats are designed to cater to different levels of student expertise and support diverse thinking skills in technological environments (Chiu, [<reflink idref="bib22" id="ref98">22</reflink>]). Scaffolding designs, like level‐up exercises, can provide students with clear expectations for lesson activities and offer flexible learning pathways in technological environments (Chiu, [<reflink idref="bib22" id="ref99">22</reflink>]).</p> <p>Digital relatedness support incorporates emotional design strategies, utilising engaging and captivating design elements to evoke learners' emotions and enhance learning in technological environments (Mayer & Estrella, [<reflink idref="bib62" id="ref100">62</reflink>]; Park et al., [<reflink idref="bib66" id="ref101">66</reflink>]). Through creating a pleasurable experience, emotional design can inspire students to invest greater effort in processing multimedia information (Knörzer et al., [<reflink idref="bib56" id="ref102">56</reflink>]; Mayer & Estrella, [<reflink idref="bib62" id="ref103">62</reflink>]).</p> <p>Few prior studies have investigated the relationship between digital support and student engagement. For example, in a study on grade 11 students, Chiu ([<reflink idref="bib22" id="ref104">22</reflink>]) found that implemented digital support significantly influenced the perceived autonomy, competence and relatedness support provided by the learning management system (LMS), leading to increased student engagement. This indicates that designed support structures effectively fulfilled students' intrinsic needs while engaging with resources and completing online tasks in blended learning. It should be noted, however, that perceived digital support from the LMS correlates differently with the four dimensions of student engagement (Chiu, [<reflink idref="bib22" id="ref105">22</reflink>]). Perceived digital autonomy support showed significant associations with agentic, behavioural and cognitive engagement. Meanwhile, perceived digital competence support correlated strongly with cognitive engagement, and relatedness support correlated with emotional engagement. This led Chiu ([<reflink idref="bib22" id="ref106">22</reflink>]) to suggest that digital autonomy support might hold greater importance compared to competence or relatedness support, a finding that was consistent with previous SDT‐related research that underscored autonomy support's central role in fostering intrinsic motivation in traditional classroom settings (Ruzek et al., [<reflink idref="bib70" id="ref107">70</reflink>]; Trenshaw et al., [<reflink idref="bib80" id="ref108">80</reflink>]).</p> <p>In another study on the effects of the three psychological needs in SDT on student engagement in online learning with 1201 grade 8 and 9 students during the COVID‐19 pandemic, Chiu ([<reflink idref="bib23" id="ref109">23</reflink>]) found that perceived digital relatedness support emerged as the primary predictor of agentic, behavioural and emotional engagement. Perceived digital competence support stood out as the most influential factor for cognitive engagement, while perceived digital autonomy support remained significant across all dimensions of student engagement, but not as prominently as relatedness or competence. These results diverged from traditional SDT studies, which typically prioritised autonomy support for fostering intrinsic motivation in learning (Ruzek et al., [<reflink idref="bib70" id="ref110">70</reflink>]; Trenshaw et al., [<reflink idref="bib80" id="ref111">80</reflink>]).</p> <p>In a systematic review of the extent to which digital technologies have influenced teaching and learning practices, Nkomo ([<reflink idref="bib65" id="ref112">65</reflink>]) highlighted how digital tools such as learning management systems and social media can influence students cognitively and emotionally. For example, emotional engagement can be promoted through chat platforms and discussion forums, helping students connect and foster a sense of community. In contrast, cognitive engagement can occur through problem‐based learning activities and self‐regulated learning, as students access resources at their own pace. However, effective engagement with digital platforms largely depends on how both students and instructors use these platforms (Nkomo, [<reflink idref="bib65" id="ref113">65</reflink>]).</p> <p>Despite the mixed findings on the influence of the types of digital support on student engagement, there was, nevertheless, evidence depicting the relations between both multidimensional constructs. As the studies by Chiu ([<reflink idref="bib22" id="ref114">22</reflink>], [<reflink idref="bib23" id="ref115">23</reflink>]) were conducted prior to the COVID‐19 pandemic and during the pandemic, respectively, it would be valuable to examine if the relationships between the various facets of digital support and student engagement are different or similar during the post‐pandemic era, as in the present study. Additionally, the paucity of studies examining the role of digital support on student engagement during blended learning warrants more of such studies, especially in the higher education setting (Chiu, [<reflink idref="bib22" id="ref116">22</reflink>]). Based on the above empirical evidence, we hypothesise the following:</p> <hd id="AN0186252682-7">H2</hd> <p>Digital support (i.e., autonomy, competence, relatedness) positively predicts student engagement (i.e., behavioural, cognitive, emotional, agentic).</p> <hd id="AN0186252682-8">Student engagement and learner satisfaction</hd> <p>Learner satisfaction in blended learning is the degree to which students perceive that their educational needs and expectations are fulfilled through a blend of online and face‐to‐face learning elements (Alqurashi, [<reflink idref="bib2" id="ref117">2</reflink>]). In terms of the relations between student engagement and learner satisfaction, studies have generally shown that greater student engagement has a positive impact on learner satisfaction. According to a study by Gao et al. ([<reflink idref="bib44" id="ref118">44</reflink>]) with 347 university students, playfulness and emotional engagement significantly enhanced satisfaction, suggesting that a stimulating learning platform fostered higher satisfaction among students. Interestingly, emotional engagement functioned as a mediator between certain factors (e.g., playfulness, usefulness, ease of use, interaction) and course satisfaction, implying that students' emotional responses are influenced by how they perceived of the learning platform. However, the impact of cognitive engagement on satisfaction was found to be insignificant, indicating that students are likely to derive greater satisfaction when they are emotionally and deeply engaged.</p> <p>In another study examining the role of course evaluation and digital platforms on students' online learning and satisfaction, Yousaf et al. ([<reflink idref="bib90" id="ref119">90</reflink>]) found significant relations between student engagement and satisfaction in their sample of 652 university students. Concurring with this finding, Fisher et al. ([<reflink idref="bib39" id="ref120">39</reflink>]), in a study of 348 university students, observed that both blended and flipped learning significantly enhanced students' perceptions of engagement, performance and satisfaction. Specifically, engagement with flipped learning directly contributed to feelings of satisfaction, even when perceptions of improved performance were not evident.</p> <p>In summary, the empirical evidence above suggests relations between student engagement and learner satisfaction, and that certain types of engagement (e.g., emotional engagement) can mediate the relationship between support provided during blended learning and learner satisfaction. However, few studies have examined the mediating role of the four dimensions of student engagement on the relationship between students' perceptions of blended learning platforms in terms of teacher and digital support, and their overall satisfaction with blended learning. In view of this research gap, we set out to examine these relationships and hypothesise the following:</p> <hd id="AN0186252682-9">H3</hd> <p>Student engagement (i.e., behavioural, cognitive, emotional, agentic) positively predicts learner satisfaction (i.e., learner–content interaction, learner–instructor interaction, learner–learner interaction, learner–technology interaction, general satisfaction).</p> <hd id="AN0186252682-10">H4.1</hd> <p>Student engagement (i.e., behavioural, cognitive, emotional, agentic) mediates the relationship between teacher support (i.e., autonomy, competence, relatedness) and learner satisfaction.</p> <hd id="AN0186252682-11">H4.2</hd> <p>Student engagement (i.e., behavioural, cognitive, emotional, agentic) mediates the relationship between digital support (i.e., autonomy, competence, relatedness) and learner satisfaction.</p> <hd id="AN0186252682-12">Task value, perceived workload and perceived difficulty</hd> <p>Task value refers to a student's assessment of how interesting, how important and how useful a task is (Pintrich et al., [<reflink idref="bib67" id="ref121">67</reflink>]). In the context of this study, the 'task' for students is their participation in their blended module. As task value concerns students' involvement in the module, it is logical to expect that this might influence students' engagement in the blended learning environment (Vanslambrouck et al., [<reflink idref="bib81" id="ref122">81</reflink>]). Hence, task value was included as a covariate in this study. Perceived workload is the sense of pressure or stress students feel due to the demands of the syllabus and assessments (Kyndt et al., [<reflink idref="bib57" id="ref123">57</reflink>]), while perceived difficulty is a subjective assessment of how challenging a task is, shaped by cognitive, motivational and emotional factors (Efklides, [<reflink idref="bib37" id="ref124">37</reflink>]). As these are relevant module‐related factors that could affect how students engage in the module, both perceived workload and difficulty were included as covariates in this study.</p> <hd id="AN0186252682-13">Proposed structural model</hd> <p>Based on these hypotheses generated from the above literature review, our proposed structural model is presented in Figure 1.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/BED/01jun25/berj4123-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="berj4123-fig-0001.jpg" title="1 Hypothesised structural model. For clarity purposes, the direct paths from teacher and digital support to learner satisfaction are not depicted. Gender, age, module type, task value, perceived level of difficulty and perceived workload (not depicted in the figure) are included as covariates in the model. All direct paths are hypothesised to be positive." /> </p> <p></p> <hd id="AN0186252682-15">THE PRESENT STUDY</hd> <p></p> <hd id="AN0186252682-16">Objectives of study</hd> <p>The present study aims to empirically assess and examine a hypothesised model in which teacher support (i.e., autonomy, competence, relatedness) and digital support (i.e., autonomy, competence, relatedness) are posited to predict student engagement (i.e., behavioural, cognitive, emotional, agentic), which, in turn, are hypothesised to predict learner satisfaction (i.e., learner–content interaction, learner–instructor interaction, learner–learner interaction, learner–technology interaction, general satisfaction) with undergraduate students taking various modules at a public university in Singapore.</p> <hd id="AN0186252682-17">Significance of study</hd> <p>Our study adds to the existing corpus of research pertaining to blended learning in the following ways. First, it adds to our understanding of the mechanisms through which teacher and digital support impacts student engagement, and student engagement influences learner satisfaction, hence enabling deeper insights to be gained into the interconnectedness of these constructs. Second, the insights derived from this study can potentially guide university educators or administrators in identifying aspects of student engagement that are crucial for the relationship between teacher and digital support, and learner satisfaction. Armed with these findings, university educators or administrators can develop interventions to enhance specific dimensions of student engagement that are vital for the blended learning context.</p> <hd id="AN0186252682-18">METHODS</hd> <p></p> <hd id="AN0186252682-19">Participants</hd> <p>The participants were 674 Year 1 and Year 2 undergraduate students from a Singapore‐based university, with an average age of 22.83 years (SD = 3.53). These students were conveniently sampled and recruited from nine modules across the Engineering and Health and Social Sciences clusters at our university. They comprised 353 males (52.4%) and 321 females (47.6%). A summary of the participant demographics is outlined in Table 1.</p> <p>1 TABLE Demographics of participants (n  = 674).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Frequency</th><th align="left">Percentage</th></tr></thead><tbody valign="top"><tr><td align="left">Gender</td><td align="left" /><td align="left" /></tr><tr><td align="left">Male</td><td align="char" char=".">353</td><td align="char" char=".">52.4</td></tr><tr><td align="left">Female</td><td align="char" char=".">321</td><td align="char" char=".">47.6</td></tr><tr><td align="left">Age</td><td align="left" /><td align="left" /></tr><tr><td align="left">19–21</td><td align="char" char=".">242</td><td align="char" char=".">35.9</td></tr><tr><td align="left">22–24</td><td align="char" char=".">330</td><td align="char" char=".">49.0</td></tr><tr><td align="left">25–27</td><td align="char" char=".">57</td><td align="char" char=".">8.4</td></tr><tr><td align="left">28+</td><td align="char" char=".">45</td><td align="char" char=".">6.7</td></tr><tr><td align="left">Year of study</td><td align="left" /><td align="left" /></tr><tr><td align="left">Year 1</td><td align="char" char=".">663</td><td align="char" char=".">98.4</td></tr><tr><td align="left">Year 2</td><td align="char" char=".">11</td><td align="char" char=".">1.6</td></tr><tr><td align="left">Modules</td><td align="left" /><td align="left" /></tr><tr><td align="left">Module 1</td><td align="char" char=".">74</td><td align="char" char=".">11.0</td></tr><tr><td align="left">Module 2</td><td align="char" char=".">97</td><td align="char" char=".">14.4</td></tr><tr><td align="left">Module 3</td><td align="char" char=".">134</td><td align="char" char=".">19.9</td></tr><tr><td align="left">Module 4</td><td align="char" char=".">65</td><td align="char" char=".">9.6</td></tr><tr><td align="left">Module 5</td><td align="char" char=".">19</td><td align="char" char=".">2.8</td></tr><tr><td align="left">Module 6</td><td align="char" char=".">18</td><td align="char" char=".">2.7</td></tr><tr><td align="left">Module 7</td><td align="char" char=".">17</td><td align="char" char=".">2.5</td></tr><tr><td align="left">Module 8</td><td align="char" char=".">128</td><td align="char" char=".">19.0</td></tr><tr><td align="left">Module 9</td><td align="char" char=".">122</td><td align="char" char=".">18.1</td></tr></tbody></table> </ephtml> </p> <hd id="AN0186252682-20">Research design</hd> <p>This study employed a cross‐sectional research design, collecting data on all variables at a single time point to analyse their interrelationships (Creswell, [<reflink idref="bib32" id="ref125">32</reflink>]). This approach was chosen to examine the temporal ordering of the latent variables (see Figure 1), as the statistical technique of structural equation modelling (SEM) allows the testing of whether the data fits the hypothesised temporal sequence. Although not as robust as longitudinal data (not feasible in our context), such a statistical method helps detect indirect effects and validate the ordering of constructs (Hayes et al., [<reflink idref="bib50" id="ref126">50</reflink>]).</p> <hd id="AN0186252682-21">Measures</hd> <p>The survey form administered in this study comprised two parts. The first contained items pertaining to participants' demographic information, including gender, year of birth, year of study, degree programme and module code and name. The second comprised sets of items measuring students' task value, perceived difficulty of the module, perceived workload of the module, perceived teacher support, perceived digital support, student engagement and learner satisfaction. These items were drawn from a variety of existing self‐report inventories and were adapted to measure the above variables in the context of higher education. Students responded to all items on a five‐point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Below are descriptions of the measures used in this study along with their Cronbach's alpha values from the present sample.</p> <hd id="AN0186252682-22">Task value</hd> <p>To measure the task value, Pintrich et al.'s ([<reflink idref="bib67" id="ref127">67</reflink>]) <emph>task value</emph> scale from the Motivated Strategies and Learning Questionnaire (MSLQ) was used. This scale includes six items measuring students' evaluation of how interesting, important and useful a particular task is (e.g., 'I think I will be able to use what I learn in this course in other courses'). However, two items were removed as they were deemed irrelevant or contained overlaps with other items (i.e., 'I like the subject matter of this course' and 'Understanding the subject matter of the course is very important to me'). The Cronbach's alpha of the resulting four‐item <emph>task value</emph> scale was 0.90.</p> <hd id="AN0186252682-23">Perceived difficulty and perceived workload</hd> <p>Perceived difficulty was measured using a single item adapted from Fulmer and Tulis ([<reflink idref="bib43" id="ref128">43</reflink>]) and Efklides ([<reflink idref="bib38" id="ref129">38</reflink>]) (i.e., 'Overall, I felt that the module was difficult'). Perceived workload, on the other hand, was evaluated via a single item adapted from Kyndt et al. ([<reflink idref="bib57" id="ref130">57</reflink>]) (i.e., 'I often feel overwhelmed with the amount of time pressure to complete the assignments and tasks in this module').</p> <hd id="AN0186252682-24">Perceived teacher and digital support</hd> <p>Two types of support were measured. Perceived teacher support concerns students' perceptions of autonomy, competence and relatedness, as facilitated by their teachers (Chiu, [<reflink idref="bib22" id="ref131">22</reflink>]). The first scale, <emph>perceived teacher autonomy support</emph>, evaluated the extent to which teachers provided learning choices, offered explanations when choices were limited and the avoidance of controlling language (<emph>α</emph> = 0.86, four items, e.g., 'My instructors encourage us to ask questions'). The second scale, <emph>perceived teacher competence support</emph>, measured the extent to which teachers provided effective guidance during lessons, established boundaries in learning activities, delivered competence‐relevant feedback and expressed confidence in their students' abilities (<emph>α</emph> = 0.84, four items, e.g., 'My instructors create well‐structured and organised course materials'). The third scale, <emph>perceived teacher relatedness support</emph>, gauged the extent to which teachers offered students emotional and motivational support, including pedagogical caring, involvement, closeness, acceptance and assistance (<emph>α</emph> = 0.88, four items, e.g., 'I am comfortable with approaching my instructors with questions or concerns').</p> <p>Perceived digital support refers to the students' perceptions of the design of technological learning environments, in terms of autonomy, competence and relatedness, to support their innate needs (Chiu, [<reflink idref="bib22" id="ref132">22</reflink>]). The first scale, <emph>perceived digital autonomy support</emph>, reflects students' perceptions of how the technological environment offers and recommends various digital resources for learning (<emph>α</emph> = 0.86, three items, e.g., 'I feel like I have freedom of choice in the digital tools I use for my learning'). The second scale, <emph>perceived digital competence support</emph>, refers to the digital environment providing well‐designed interactive learning materials and progressively challenging exercises in a cognitively demanding setting (<emph>α</emph> = 0.86, three items, e.g., 'I think I am pretty good at learning using digital tools'). Finally, the <emph>perceived digital relatedness support</emph> scale comprised items relating to personal and emotional design elements and communication features that foster a positive atmosphere (<emph>α</emph> = 0.89, three items, e.g., 'The digital tools make me feel more connected to my peers'). To clarify what digital tools refer to, we provided some examples of commonly used digital tools at our university (e.g., learning management system, Microsoft Teams, Zoom, Microsoft OneDrive).</p> <hd id="AN0186252682-25">Student engagement</hd> <p>Items used to make up student engagement were based on the student engagement instrument modified from Chiu ([<reflink idref="bib22" id="ref133">22</reflink>]). Student engagement, as measured in this study, contained four dimensions, namely, <emph>behavioural</emph> (<emph>α</emph> = 0.80, e.g., 'I make a conscious effort to do well in all of the learning activities'), <emph>cognitive</emph> (<emph>α</emph> = 0.79, e.g., 'I make a conscious effort to do well in all of the learning activities'), <emph>emotional</emph> (<emph>α</emph> = 0.88, e.g., 'I find blended learning rewarding') and <emph>agentic</emph> (<emph>α</emph> = 0.89, e.g., 'I am able to let my instructor know what I need and want'), each evaluated using three items.</p> <hd id="AN0186252682-26">Learner satisfaction</hd> <p>Learner satisfaction was measured using the Learner Satisfaction Survey (LSS; Chang, [<reflink idref="bib18" id="ref134">18</reflink>]; Torrado & Blanca, [<reflink idref="bib79" id="ref135">79</reflink>]), a shortened version of the Online Satisfaction Survey. The LSS consists of 18 modified self‐report items that measure four aspects of interaction (i.e., learner–content, learner–instructor, learner–learner and learner–technology), as well as general satisfaction. The <emph>learner–content interaction</emph> scale examines a learner's interaction with course contents, lessons, learning activities, learning objects, videos, websites and projects (<emph>α</emph> = 0.87, three items, e.g., 'The learning activities in this module have facilitated my learning'). The second scale, <emph>learner–instructor interaction</emph>, focuses on the two‐way communication between learner and instructor, crucial for content clarification and feedback exchange (<emph>α</emph> = 0.87, four items, e.g., 'In this module, the instructor fosters interactive two‐way communication between students and themselves'). Third, the <emph>learner–learner interaction</emph> scale measures the two‐way communication between a learner and other learners (<emph>α</emph> = 0.85, four items, e.g., 'This module has created a sense of community among students'). The <emph>learner–technology interaction</emph> scale assesses the learner's comfort and proficiency in their interactions with online environments (<emph>α</emph> = 0.85, three items, e.g., 'I enjoy working in online environments'). Finally, the <emph>general satisfaction</emph> scale concerns the overall satisfaction students felt with the module (<emph>α</emph> = 0.88, four items, e.g., 'I am very satisfied with this module').</p> <hd id="AN0186252682-27">Procedures</hd> <p>Prior to data collection, our study was approved by the university's institutional review board (IRB) for ethics clearance (IRB no. RECAS‐0144). We administered a cross‐sectional self‐report questionnaire to students at the end of the first trimester of the academic year (AY) 2023/2024, in November 2023. The survey was hosted on the online Qualtrics platform for students to conveniently access and complete via an online link or QR code. Students were given 15 minutes to complete the survey during curriculum time. Participation in the survey was voluntary and only completed participant responses for all items were used for this study. Incomplete responses were removed prior to data analysis.</p> <hd id="AN0186252682-28">Data analyses</hd> <p>The Statistical Package for Social Sciences (SPSS) version 27.0 (IBM Corp., [<reflink idref="bib52" id="ref136">52</reflink>]) was used to obtain the descriptive statistics (i.e., means, standard deviations, skewness, kurtosis) and internal reliabilities of the scales in the survey. On the other hand, multi‐group invariance tests, confirmatory factor analysis (CFA) and SEM were performed using Mplus 8.0 (Muthén & Muthén, [<reflink idref="bib64" id="ref137">64</reflink>]–2017). Multi‐group invariance tests were conducted to ascertain whether the scores derived from the operationalisation of the scales in the survey held the same meaning across the subgroup of gender (Byrne, [<reflink idref="bib16" id="ref138">16</reflink>]; Kline, [<reflink idref="bib54" id="ref139">54</reflink>]). To verify the structure of the observed variables measuring the latent factors, CFA was employed (Brown, [<reflink idref="bib12" id="ref140">12</reflink>]). Finally, SEM was used to evaluate the multivariate relationships between the covariates (i.e., gender, age, module type, task value, perceived level of difficulty and perceived workload), and the latent factors.</p> <p>We adhered to the cut‐off criteria recommended by Hu and Bentler ([<reflink idref="bib51" id="ref141">51</reflink>]) for evaluating the model fit of our measurement and structural models: (<reflink idref="bib1" id="ref142">1</reflink>) <emph>χ</emph><sups>2</sups>/df < 5; (<reflink idref="bib2" id="ref143">2</reflink>) comparative fit index (CFI) ≥ 0.90; (<reflink idref="bib3" id="ref144">3</reflink>) Tucker–Lewis index (TLI) ≥ 0.90; (<reflink idref="bib4" id="ref145">4</reflink>) root mean square error of approximation (RMSEA) ≤ 0.06; and (<reflink idref="bib5" id="ref146">5</reflink>) standardised root mean residual (SRMR) ≤ 0.08. Even though the chi‐square statistic is routinely reported as a model fit indicator, it is inflated by a large sample size and is commonly rejected (Bentler & Bonett, [<reflink idref="bib8" id="ref147">8</reflink>]). Regardless, we still report the chi‐square statistic because it is a common reporting ethic to use it alongside other fit measures (Kyriazos, [<reflink idref="bib58" id="ref148">58</reflink>]). Alpha levels were all set at <emph>p</emph> < 0.05.</p> <hd id="AN0186252682-29">RESULTS</hd> <p></p> <hd id="AN0186252682-30">Descriptive statistics and CFA correlations</hd> <p>Table 2 shows the means, standard deviations, skewness, kurtosis, Cronbach's alpha values and CFA factor loadings of each research variable. The means and standard deviations ranged from 3.53 to 4.15 and 0.56 to 1.03, respectively. Skewness and kurtosis values were used to check the normality of the variables. All variables met the skewness and kurtosis thresholds of −2 to +2 and − 7 to +7, respectively, for multivariate normal distribution (Byrne, [<reflink idref="bib16" id="ref149">16</reflink>]), hence the normality of the variables was considered appropriate for SEM analysis.</p> <p>2 TABLE Descriptive statistics, distributional properties, Cronbach's alpha coefficients and CFA factor loadings (n  = 674).</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Mean</th><th align="left">SD</th><th align="left">Skewness</th><th align="left">Kurtosis</th><th align="left"><italic>α</italic></th><th align="left">Mean of CFA factor loadings (range)</th></tr></thead><tbody valign="top"><tr><td align="left">Task value</td><td align="char" char=".">3.92</td><td align="char" char=".">0.74</td><td align="char" char=".">−0.78</td><td align="char" char=".">1.40</td><td align="left">0.90</td><td align="left">0.83 (0.80–0.86)</td></tr><tr><td align="left">Perceived difficulty</td><td align="char" char=".">3.53</td><td align="char" char=".">0.94</td><td align="char" char=".">−0.17</td><td align="char" char=".">−0.46</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Perceived workload</td><td align="char" char=".">3.62</td><td align="char" char=".">1.03</td><td align="char" char=".">−0.24</td><td align="char" char=".">−0.77</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Teacher support</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Teacher autonomy support</td><td align="char" char=".">4.01</td><td align="char" char=".">0.64</td><td align="char" char=".">−0.51</td><td align="char" char=".">0.83</td><td align="left">0.86</td><td align="left">0.77 (0.72–0.83)</td></tr><tr><td align="left">Teacher competence support</td><td align="char" char=".">3.95</td><td align="char" char=".">0.65</td><td align="char" char=".">−0.38</td><td align="char" char=".">0.62</td><td align="left">0.84</td><td align="left">0.76 (0.74–0.77)</td></tr><tr><td align="left">Teacher relatedness support</td><td align="char" char=".">4.15</td><td align="char" char=".">0.63</td><td align="char" char=".">−0.54</td><td align="char" char=".">0.78</td><td align="left">0.88</td><td align="left">0.82 (0.76–0.86)</td></tr><tr><td align="left">Digital support</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Digital autonomy support</td><td align="char" char=".">4.09</td><td align="char" char=".">0.64</td><td align="char" char=".">−0.63</td><td align="char" char=".">1.50</td><td align="left">0.86</td><td align="left">0.82 (0.77–0.86)</td></tr><tr><td align="left">Digital competence support</td><td align="char" char=".">3.89</td><td align="char" char=".">0.72</td><td align="char" char=".">−0.54</td><td align="char" char=".">0.91</td><td align="left">0.86</td><td align="left">0.83 (0.77–0.86)</td></tr><tr><td align="left">Digital relatedness support</td><td align="char" char=".">3.64</td><td align="char" char=".">0.87</td><td align="char" char=".">−0.40</td><td align="char" char=".">0.05</td><td align="left">0.89</td><td align="left">0.87 (0.80–0.90)</td></tr><tr><td align="left">Student engagement</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Behavioural engagement</td><td align="char" char=".">4.15</td><td align="char" char=".">0.56</td><td align="char" char=".">−0.44</td><td align="char" char=".">1.02</td><td align="left">0.80</td><td align="left">0.76 (0.73–0.81)</td></tr><tr><td align="left">Cognitive engagement</td><td align="char" char=".">4.03</td><td align="char" char=".">0.60</td><td align="char" char=".">−0.40</td><td align="char" char=".">1.09</td><td align="left">0.79</td><td align="left">0.75 (0.72–0.77)</td></tr><tr><td align="left">Emotional engagement</td><td align="char" char=".">3.82</td><td align="char" char=".">0.72</td><td align="char" char=".">−0.38</td><td align="char" char=".">0.43</td><td align="left">0.88</td><td align="left">0.84 (0.81–0.89)</td></tr><tr><td align="left">Agentic engagement</td><td align="char" char=".">3.70</td><td align="char" char=".">0.75</td><td align="char" char=".">−0.42</td><td align="char" char=".">0.60</td><td align="left">0.89</td><td align="left">0.86 (0.83–0.88)</td></tr><tr><td align="left">Learner satisfaction</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Learner–content interaction</td><td align="char" char=".">3.96</td><td align="char" char=".">0.63</td><td align="char" char=".">−0.83</td><td align="char" char=".">2.55</td><td align="left">0.87</td><td align="left">0.83 (0.80–0.86)</td></tr><tr><td align="left">Learner–instructor interaction</td><td align="char" char=".">4.00</td><td align="char" char=".">0.61</td><td align="char" char=".">−0.40</td><td align="char" char=".">0.61</td><td align="left">0.87</td><td align="left">0.79 (0.74–0.84)</td></tr><tr><td align="left">Learner–learner interaction</td><td align="char" char=".">3.98</td><td align="char" char=".">0.59</td><td align="char" char=".">−0.23</td><td align="char" char=".">0.57</td><td align="left">0.85</td><td align="left">0.78 (0.75–0.81)</td></tr><tr><td align="left">Learner–technology interaction</td><td align="char" char=".">3.83</td><td align="char" char=".">0.79</td><td align="char" char=".">−0.56</td><td align="char" char=".">0.49</td><td align="left">0.85</td><td align="left">0.81 (0.71–0.87)</td></tr><tr><td align="left">General satisfaction</td><td align="char" char=".">3.76</td><td align="char" char=".">0.76</td><td align="char" char=".">−0.57</td><td align="char" char=".">0.77</td><td align="left">0.88</td><td align="left">0.82 (0.68–0.89)</td></tr></tbody></table> </ephtml> </p> <p>Table 3 presents the results of the CFA correlation analysis. As indicated, the three dimensions of teacher support and digital support were positively and significantly correlated with all four dimensions of student engagement (<emph>p</emph> < 0.001). Similarly, the four dimensions of student engagement were also positively and significantly related to all five aspects of learner satisfaction (<emph>p</emph> < 0.001). The covariate of task value was positively and significantly correlated with all dimensions of teacher support, digital support, student engagement and learner satisfaction (<emph>p</emph> < 0.001). The remaining covariates of gender, age, perceived difficulty and perceived workload were only significantly correlated with some facets of the main study latent variables.</p> <p>3 TABLE CFA correlations among covariates, teacher support, digital support, student engagement and learner satisfaction.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Covariates</th><th align="left">Teacher support</th><th align="left">Digital support</th><th align="left">Student engagement</th><th align="left">Learner satisfaction</th></tr><tr><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th><th align="left">6</th><th align="left">7</th><th align="left">8</th><th align="left">9</th><th align="left">10</th><th align="left">11</th><th align="left">12</th><th align="left">13</th><th align="left">14</th><th align="left">15</th><th align="left">16</th><th align="left">17</th><th align="left">18</th><th align="left">19</th><th align="left">20</th></tr></thead><tbody valign="top"><tr><td align="left">Covariates</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">1. Gender</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">2. Age</td><td align="left">−0.33</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">3. Task value</td><td align="left">−0.08</td><td align="left">0.10</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">4. Perceived difficulty</td><td align="left">0.02</td><td align="left">−0.02</td><td align="left">−0.06</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">5. Perceived workload</td><td align="left">0.01</td><td align="left">0.01</td><td align="left">−0.12</td><td align="left">0.68</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Teacher support</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">6. Teacher autonomy support</td><td align="left">−0.11</td><td align="left">0.09</td><td align="left">0.65</td><td align="left">−0.09</td><td align="left">−0.09</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">7. Teacher competence support</td><td align="left">−0.14</td><td align="left">0.10</td><td align="left">0.59</td><td align="left">−0.10</td><td align="left">−0.09</td><td align="left">0.96</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">8. Teacher relatedness support</td><td align="left">−0.06</td><td align="left">0.04</td><td align="left">0.53</td><td align="left">−0.13</td><td align="left">−0.08</td><td align="left">0.85</td><td align="left">0.88</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Digital support</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">9. Digital autonomy support</td><td align="left">−0.02</td><td align="left">−0.03</td><td align="left">0.49</td><td align="left">−0.04</td><td align="left">−0.07</td><td align="left">0.54</td><td align="left">0.59</td><td align="left">0.55</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">10. Digital competence support</td><td align="left">−0.04</td><td align="left">0.03</td><td align="left">0.48</td><td align="left">−0.10</td><td align="left">−0.09</td><td align="left">0.53</td><td align="left">0.58</td><td align="left">0.49</td><td align="left">0.80</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">11. Digital relatedness support</td><td align="left">−0.09</td><td align="left">0.02</td><td align="left">0.43</td><td align="left">−0.09</td><td align="left">−0.04</td><td align="left">0.51</td><td align="left">0.61</td><td align="left">0.46</td><td align="left">0.59</td><td align="left">0.82</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Student engagement</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">12. Behavioural engagement</td><td align="left">−0.05</td><td align="left">0.08</td><td align="left">0.48</td><td align="left">0.07</td><td align="left">0.12</td><td align="left">0.50</td><td align="left">0.48</td><td align="left">0.48</td><td align="left">0.47</td><td align="left">0.45</td><td align="left">0.34</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">13. Cognitive engagement</td><td align="left">−0.09</td><td align="left">0.12</td><td align="left">0.56</td><td align="left">0.03</td><td align="left">0.08</td><td align="left">0.59</td><td align="left">0.54</td><td align="left">0.52</td><td align="left">0.47</td><td align="left">0.52</td><td align="left">0.45</td><td align="left">0.85</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">14. Emotional engagement</td><td align="left">−0.03</td><td align="left">0.09</td><td align="left">0.42</td><td align="left">0.01</td><td align="left">0.02</td><td align="left">0.48</td><td align="left">0.49</td><td align="left">0.44</td><td align="left">0.50</td><td align="left">0.65</td><td align="left">0.63</td><td align="left">0.54</td><td align="left">0.64</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">15. Agentic engagement</td><td align="left">−0.07</td><td align="left">0.06</td><td align="left">0.43</td><td align="left">−0.07</td><td align="left">−0.08</td><td align="left">0.67</td><td align="left">0.68</td><td align="left">0.58</td><td align="left">0.51</td><td align="left">0.57</td><td align="left">0.59</td><td align="left">0.52</td><td align="left">0.65</td><td align="left">0.69</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Learner satisfaction</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">16. Learner–content interaction</td><td align="left">−0.01</td><td align="left">0.02</td><td align="left">0.63</td><td align="left">0.02</td><td align="left">−0.01</td><td align="left">0.69</td><td align="left">0.67</td><td align="left">0.64</td><td align="left">0.62</td><td align="left">0.60</td><td align="left">0.51</td><td align="left">0.60</td><td align="left">0.68</td><td align="left">0.63</td><td align="left">0.70</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">17. Learner–instructor interaction</td><td align="left">−0.09</td><td align="left">0.05</td><td align="left">0.48</td><td align="left">−0.05</td><td align="left">−0.02</td><td align="left">0.82</td><td align="left">0.81</td><td align="left">0.82</td><td align="left">0.55</td><td align="left">0.52</td><td align="left">0.50</td><td align="left">0.57</td><td align="left">0.62</td><td align="left">0.54</td><td align="left">0.73</td><td align="left">0.79</td><td align="left">–</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">18. Learner–learner interaction</td><td align="left">−0.03</td><td align="left">0.05</td><td align="left">0.52</td><td align="left">−0.07</td><td align="left">−0.11</td><td align="left">0.71</td><td align="left">0.70</td><td align="left">0.66</td><td align="left">0.62</td><td align="left">0.55</td><td align="left">0.48</td><td align="left">0.54</td><td align="left">0.61</td><td align="left">0.58</td><td align="left">0.70</td><td align="left">0.86</td><td align="left">0.86</td><td align="left">–</td><td align="left" /><td align="left" /></tr><tr><td align="left">19. Learner–technology interaction</td><td align="left">−0.07</td><td align="left">0.03</td><td align="left">0.30</td><td align="left">−0.09</td><td align="left">−0.08</td><td align="left">0.43</td><td align="left">0.47</td><td align="left">0.37</td><td align="left">0.50</td><td align="left">0.70</td><td align="left">0.67</td><td align="left">0.43</td><td align="left">0.50</td><td align="left">0.68</td><td align="left">0.58</td><td align="left">0.56</td><td align="left">0.48</td><td align="left">0.57</td><td align="left">–</td><td align="left" /></tr><tr><td align="left">20. General satisfaction</td><td align="left">−0.06</td><td align="left">0.12</td><td align="left">0.68</td><td align="left">−0.21</td><td align="left">−0.23</td><td align="left">0.71</td><td align="left">0.71</td><td align="left">0.61</td><td align="left">0.60</td><td align="left">0.64</td><td align="left">0.62</td><td align="left">0.50</td><td align="left">0.61</td><td align="left">0.66</td><td align="left">0.70</td><td align="left">0.83</td><td align="left">0.71</td><td align="left">0.78</td><td align="left">0.63</td><td align="left">–</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>: Coefficients significant at <emph>p</emph> < 0.05 are italicised, at <emph>p</emph> < 0.01 underlined, at <emph>p</emph> < 0.001 in bold. Gender (1 = male, 2 = female), <emph>n</emph> = 674.</p> <hd id="AN0186252682-31">Measurement model</hd> <p>The measurement model was evaluated using CFA carried out at the individual item level across all the items and constructs to be included in the empirical model. Each construct was treated as a latent variable and the respective items as observed variables. To account for individuals that were nested within nine different modules in the sample, the 'stratification' command in Mplus was used. This approach improves the precision of estimates and ensures that specific subgroups (i.e., nine modules) are adequately represented (Muthén & Muthén, [<reflink idref="bib64" id="ref150">64</reflink>]–2017). The fit indices show that our data fit the overall CFA model well: <emph>χ</emph><sups>2</sups> = 2607.379, df = 1310, <emph>p</emph> < 0.001, CFI = 0.934, TLI = 0.925, RMSEA = 0.038, SRMR = 0.040. The mean factor loadings and corresponding ranges for all latent variables are contained in Table 2. All loadings were significant at <emph>p</emph> < 0.001. The Cronbach alpha values for all latent constructs were also found to be within the range of 0.79 to 0.90 (see Table 2), indicating appropriate to excellent internal consistencies (see George & Mallery, [<reflink idref="bib45" id="ref151">45</reflink>]). Additionally, we performed multi‐group measurement invariance tests for configural, metric and scalar invariance (Kline, [<reflink idref="bib54" id="ref152">54</reflink>]) across gender. The results from Table 4 show minor variations, yet consistent invariance across the three models when successive elements of the factor structure were held invariant for gender, based on criteria of lack of invariance (ΔCFI < 0.01 by Cheung & Rensvold, [<reflink idref="bib21" id="ref153">21</reflink>]; ΔRMSEA < 0.015 by Chen, [<reflink idref="bib19" id="ref154">19</reflink>]).</p> <p>4 TABLE Multi‐group invariance fit statistics for configural, metric and scalar models across gender.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left"><italic>χ</italic><sup>2</sup></th><th align="left">df</th><th align="left">RMSEA</th><th align="left">CFI</th><th align="left">ΔRMSEA</th><th align="left">ΔCFI</th></tr></thead><tbody valign="top"><tr><td align="left">Gender (male, female)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Model 1: Configural invariance</td><td align="char" char=".">4613.341</td><td align="char" char=".">2620</td><td align="char" char=".">0.048</td><td align="char" char=".">0.907</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Model 2: Metric invariance</td><td align="char" char=".">4650.544</td><td align="char" char=".">2659</td><td align="char" char=".">0.047</td><td align="char" char=".">0.907</td><td align="left">0.001</td><td align="left">0.000</td></tr><tr><td align="left">Model 3: Scalar invariance</td><td align="char" char=".">4724.117</td><td align="char" char=".">2698</td><td align="char" char=".">0.047</td><td align="char" char=".">0.905</td><td align="left">0.000</td><td align="left">0.002</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>: <emph>N</emph> = 674. Male, <emph>n</emph> = 353; female, <emph>n</emph> = 321.</p> <p>Therefore, the overall CFA model offers (a) a correlation matrix among measured variables, considering their measurement unreliability, and (b) a baseline for comparison for the subsequent SEM model nested within it (Liem et al., [<reflink idref="bib60" id="ref155">60</reflink>]). Taken together, the preliminary analysis of distributional properties, internal reliabilities and measurement model yielded positive indicative results that the instrumentation works well. Furthermore, the CFA correlations suggest initial support for the hypothesised relationships in the theoretical model (see Figure 1). We will delve deeper into these relationships next by conducting analyses that account for the shared variance among factors in the model, including covariates.</p> <hd id="AN0186252682-32">Structural model</hd> <p>SEM was used to examine the hypothesised structural model in Figure 1. The model showed a good fit to the data: <emph>χ</emph><sups>2</sups> = 2859.213, df = 1466, <emph>p</emph> < 0.001, CFI = 0.933, TLI = 0.922, RMSEA = 0.038, SRMR = 0.039. However, several hypothesised paths in this model were not significant. For example, the paths from teacher autonomy support (TAS) to behavioural (<emph>β</emph> = 0.42, <emph>p</emph> > 0.05), cognitive (<emph>β</emph> = 0.93, <emph>p</emph> > 0.05), emotional (<emph>β</emph> = 0.42, <emph>p</emph> > 0.05) and agentic (<emph>β</emph> = 0.63, <emph>p</emph> > 0.05) engagement. Interestingly, the paths from teacher competence support (TCS) to behavioural (<emph>β</emph> = −0.37, <emph>p</emph> > 0.05), cognitive (<emph>β</emph> = −0.90, <emph>p</emph> > 0.05), emotional (<emph>β</emph> = −0.48, <emph>p</emph> > 0.05) and agentic (<emph>β</emph> = −0.15, <emph>p</emph> > 0.05) engagement became negative. Given that the correlation between TCS and all four student engagement dimensions were positive, these negative regression weights may indicate a suppression effect due to shared variance in student engagement, jointly explained by TCS and the other predictors of TAS and teacher relatedness support (TRS). That is, the significant correlation between TCS and TAS (<emph>r</emph> = 0.96, <emph>p</emph> < 0.001), which was relatively higher than the correlations between TCS and TRS (<emph>r</emph> = 0.88, <emph>p</emph> < 0.001) and TAS and TRS (<emph>r</emph> = 0.85, <emph>p</emph> < 0.001), may have led to multicollinearity. To address this issue, TCS was removed as a predictor because prior research by Ameloot et al. ([<reflink idref="bib3" id="ref156">3</reflink>]) indicated that TAS and TRS were more important due to the higher levels of relatedness and positive feelings of autonomy observed in both the experimental and control groups in their study. Additionally, both behavioural engagement (BE) and cognitive engagement (CE) were found not to significantly predict any facet of learner satisfaction, nor did any exogeneous variables—teacher or digital support dimensions—significantly predict BE or CE. Consequently, both BE and CE were removed from the initial model. This decision aligns with previous findings by Gao et al. ([<reflink idref="bib44" id="ref157">44</reflink>]), where CE was also identified as a non‐significant predictor of learner satisfaction, further supporting its removal from the model. The final model showed a good fit to the data: <emph>χ</emph><sups>2</sups> = 1986.321, df = 995, <emph>p</emph> < 0.001, CFI = 0.942, TLI = 0.932, RMSEA = 0.038, SRMR = 0.041. Table 5 displays the standardised beta coefficients of the final first‐order structural model, while Figure 2 provides a diagrammatic representation of the model.</p> <p>5 TABLE Standardised beta coefficients of the final first‐order SEM.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Mediators/outcomes</th><th align="left">SE</th><th align="left">LS</th></tr><tr><th align="left">Predictors</th><th align="left">EE</th><th align="left">AE</th><th align="left">LCI</th><th align="left">LII</th><th align="left">LLI</th><th align="left">LTI</th><th align="left">GS</th></tr></thead><tbody valign="top"><tr><td align="left">Covariates (COV)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Gender (GEN)</td><td align="left">0.05</td><td align="left">0.01</td><td align="left">0.03</td><td align="left">−0.03</td><td align="left">0.04</td><td align="left">−0.03</td><td align="left">0.03</td></tr><tr><td align="left">Age (AGE)</td><td align="left">0.08<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.02</td><td align="left">−0.03</td><td align="left">−0.01</td><td align="left">−0.02</td><td align="left">−0.05</td><td align="left">0.03</td></tr><tr><td align="left">Task value (TV)</td><td align="left">0.06</td><td align="left">−0.08</td><td align="left">0.24<xref ref-type="fn" rid="tfn6" /></td><td align="left">−0.10<xref ref-type="fn" rid="tfn4" /></td><td align="left">0.03</td><td align="left">−0.14<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.31<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">Perceived difficulty (PD)</td><td align="left">0.07</td><td align="left">0.05</td><td align="left">0.05</td><td align="left">0.01</td><td align="left">0.02</td><td align="left">−0.03</td><td align="left">−0.09<xref ref-type="fn" rid="tfn4" /></td></tr><tr><td align="left">Perceived workload (PW)</td><td align="left">0.01</td><td align="left">−0.06</td><td align="left">0.00</td><td align="left">0.03</td><td align="left">−0.00</td><td align="left">0.02</td><td align="left">−0.06</td></tr><tr><td align="left">Teacher support (TS)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Teacher autonomy support (TAS)</td><td align="left">0.06</td><td align="left">0.52</td><td align="left">0.12</td><td align="left">0.32<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.31<xref ref-type="fn" rid="tfn4" /></td><td align="left">0.16</td><td align="left">0.21<xref ref-type="fn" rid="tfn4" /></td></tr><tr><td align="left">Teacher relatedness support (TRS)</td><td align="left">0.10</td><td align="left">−0.01</td><td align="left">0.11</td><td align="left">0.40<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.06</td><td align="left">−0.13</td><td align="left">−0.06</td></tr><tr><td align="left">Digital support (DS)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Digital autonomy support (DAS)</td><td align="left">−0.07</td><td align="left">0.04</td><td align="left">0.18</td><td align="left">0.09</td><td align="left">0.31<xref ref-type="fn" rid="tfn6" /></td><td align="left">−0.08</td><td align="left">0.12</td></tr><tr><td align="left">Digital competence support (DCS)</td><td align="left">0.38<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.08</td><td align="left">0.07</td><td align="left">−0.04</td><td align="left">−0.11</td><td align="left">0.41<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.04</td></tr><tr><td align="left">Digital relatedness support (DRS)</td><td align="left">0.27<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.28<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.15</td><td align="left">−0.03</td><td align="left">−0.08</td><td align="left">0.13</td><td align="left">0.10</td></tr><tr><td align="left">Student engagement (SE)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Emotional engagement (EE)</td><td align="left">–</td><td align="left">–</td><td align="left">0.20<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.04</td><td align="left">0.17<xref ref-type="fn" rid="tfn4" /></td><td align="left">0.35<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.23<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">Agentic engagement (AE)</td><td align="left">–</td><td align="left">–</td><td align="left">0.28<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.30<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.29<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.10</td><td align="left">0.19<xref ref-type="fn" rid="tfn4" /></td></tr><tr><td align="left">Learner satisfaction (LS)</td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Learner–content interaction (LCI)</td><td align="left" /><td align="left" /><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Learner–instructor interaction (LII)</td><td align="left" /><td align="left" /><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Learner–learner interaction (LLI)</td><td align="left" /><td align="left" /><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">Learner–technology interaction (LTI)</td><td align="left" /><td align="left" /><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">General satisfaction (GS)</td><td align="left" /><td align="left" /><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td></tr><tr><td align="left">R<sup>2</sup></td><td align="left">0.48<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.54<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.70<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.80<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.66<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.61<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.74<xref ref-type="fn" rid="tfn6" /></td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 <emph>Note</emph>: Gender (1 = male, 2 = female).</item> <item>4 * <emph>p</emph> < 0.05.</item> <item>5 ** <emph>p</emph> < 0.01.</item> <item>6 *** <emph>p</emph> < 0.001.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/BED/01jun25/berj4123-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="berj4123-fig-0002.jpg" title="2 Final empirical structural model. *p < 0.05, **p < 0.01, ***p < 0.001. Dotted lines indicate the direct relations from teacher and digital support to learner satisfaction. Model fit indices: χ2 = 1986.321, df = 995, p < 0.001, CFI = 0.942, TLI = 0.932, RMSEA = 0.038, SRMR = 0.041." /> </p> <p></p> <p>As illustrated in Figure 2, the findings show that teacher autonomy support positively and significantly predicted agentic engagement (<emph>β</emph> = 0.52, <emph>p</emph> < 0.001). Although teacher relatedness support did not predict any engagement dimensions, it significantly predicted learner–instructor interaction (<emph>β</emph> = 0.40, <emph>p</emph> < 0.001). Teacher autonomy support significantly predicted learner–instructor interaction (<emph>β</emph> = 0.32, <emph>p</emph> < 0.01), learner–learner interaction (<emph>β</emph> = 0.31, <emph>p</emph> < 0.05) and general satisfaction (<emph>β</emph> = 0.21, <emph>p</emph> < 0.05). Therefore, H1 was partially supported.</p> <p>Regarding digital support, digital competence support was found to positively and significantly predict emotional engagement (<emph>β</emph> = 0.38, <emph>p</emph> < 0.01), while digital relatedness support significantly predicted emotional engagement (<emph>β</emph> = 0.27, <emph>p</emph> < 0.01) and agentic engagement (<emph>β</emph> = 0.28, <emph>p</emph> < 0.01). H2 was therefore partially supported. Additionally, digital autonomy support and digital competence support were also found to positively and significantly predict learner–learner interaction (<emph>β</emph> = 0.31, <emph>p</emph> < 0.001) and learner–technology interaction (<emph>β</emph> = 0.41, <emph>p</emph> < 0.01), respectively.</p> <p>Based on the SEM results, emotional engagement positively and significantly predicted learner–content interaction (<emph>β</emph> = 0.20, <emph>p</emph> < 0.01), learner–learner interaction (<emph>β</emph> = 0.17, <emph>p</emph> < 0.05), learner–technology interaction (<emph>β</emph> = 0.35, <emph>p</emph> < 0.001) and general satisfaction (<emph>β</emph> = 0.23, <emph>p</emph> < 0.001). On the other hand, agentic engagement exhibited positive and significant predictive relationships with learner–content interaction (<emph>β</emph> = 0.28, <emph>p</emph> < 0.01), learner–instructor interaction (<emph>β</emph> = 0.30, <emph>p</emph> < 0.001), learner–learner interaction (<emph>β</emph> = 0.29, <emph>p</emph> < 0.01) and general satisfaction (<emph>β</emph> = 0.19, <emph>p</emph> < 0.05). These results partially support H3.</p> <p>In addition to the direct effects reported above, the indirect estimates of the multiple‐mediators model were evaluated. A bootstrapping approach was adopted to examine the mediating role of student engagement in the relationships between teacher and digital support, and various facets of learner satisfaction. The parameter estimates and 95% bias‐corrected confidence intervals of the indirect effects were performed with 1000 random samples. The following specific indirect paths were found to be significant:</p> <p></p> <ulist> <item> Teacher autonomy support → agentic engagement → learner–content interaction (<emph>β</emph>  = 0.15, <emph>p</emph>  < 0.05).</item> <p></p> <item> Digital relatedness support → agentic engagement → learner–content interaction (<emph>β</emph>  = 0.08, <emph>p</emph>  < 0.05).</item> <p></p> <item> Teacher autonomy support → agentic engagement → learner–instructor interaction (<emph>β</emph>  = 0.16, <emph>p</emph>  < 0.01).</item> <p></p> <item> Digital relatedness support → agentic engagement → learner–instructor interaction (<emph>β</emph>  = 0.09, <emph>p</emph>  < 0.05).</item> <p></p> <item> Teacher autonomy support → agentic engagement → learner–learner interaction (<emph>β</emph>  = 0.15, <emph>p</emph>  < 0.05).</item> <p></p> <item> Digital relatedness support → agentic engagement → learner–learner interaction (<emph>β</emph>  = 0.08, <emph>p</emph>  < 0.05).</item> <p></p> <item> Digital competence support → emotional engagement → learner–technology interaction (<emph>β</emph>  = 0.13, <emph>p</emph>  < 0.05).</item> <p></p> <item> Digital relatedness support → emotional engagement → learner–technology interaction (<emph>β</emph>  = 0.09, <emph>p</emph>  < 0.05).</item> <p></p> <item> Digital relatedness support → emotional engagement → general satisfaction (<emph>β</emph>  = 0.06, <emph>p</emph>  < 0.05).</item> </ulist> <p>Overall, these results demonstrate that agentic and emotional engagement served as mediators between teacher and digital support and learner satisfaction. Notably, agentic engagement mediated most of the significant indirect effects from teacher autonomy and digital relatedness support to satisfaction across learner–content, learner–instructor and learner–learner interactions. Equally important was the role of emotional engagement—shaped by digital competence and relatedness support—in enhancing learners' satisfaction with technology interactions and their general satisfaction. Hence, H4.1 and H4.2 were partially supported. Table 6 shows the standardised beta coefficients of the direct, indirect and total effects of the relationships among the latent constructs.</p> <p>6 TABLE Direct, indirect and total effects of the relationships between first‐order latent variables.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Paths</th><th align="left">Specific indirect effects via</th><th align="left">Direct effect</th><th align="left">Total indirect effect</th><th align="left">Total effect</th></tr><tr><th align="left">EE</th><th align="left">AE</th></tr></thead><tbody valign="top"><tr><td align="left">TAS → LCI</td><td align="char" char="[">0.01 [−0.04, 0.07]</td><td align="char" char="[">0.15<xref ref-type="fn" rid="tfn9" /> [0.05, 0.31]</td><td align="char" char="[">0.12 [−0.15, 0.34]</td><td align="char" char="[">0.16<xref ref-type="fn" rid="tfn9" /> [0.05, 0.32]</td><td align="char" char="[">0.28<xref ref-type="fn" rid="tfn9" /> [0.07, 0.51]</td></tr><tr><td align="left">TRS → LCI</td><td align="char" char="[">0.02 [−0.02, 0.08]</td><td align="char" char="[">−0.00 [−0.09, 0.05]</td><td align="char" char="[">0.11 [−0.08, 0.30]</td><td align="char" char="[">0.02 [−0.08, 0.10]</td><td align="char" char="[">0.13 [−0.07, 0.33]</td></tr><tr><td align="left">DAS → LCI</td><td align="char" char="[">−0.01 [−0.09, 0.02]</td><td align="char" char="[">0.01 [−0.06, 0.07]</td><td align="char" char="[">0.18 [−0.04, 0.36]</td><td align="char" char="[">−0.00 [−0.11, 0.06]</td><td align="char" char="[">0.17 [−0.09, 0.36]</td></tr><tr><td align="left">DCS → LCI</td><td align="char" char="[">0.07 [0.02, 0.21]</td><td align="char" char="[">0.02 [−0.05, 0.13]</td><td align="char" char="[">0.07 [−0.20, 0.38]</td><td align="char" char="[">0.10 [−0.02, 0.25]</td><td align="char" char="[">0.17 [−0.10, 0.51]</td></tr><tr><td align="left">DRS → LCI</td><td align="char" char="[">0.05 [0.01, 0.13]</td><td align="char" char="[">0.08<xref ref-type="fn" rid="tfn9" /> [0.02, 0.19]</td><td align="char" char="[">−0.15 [−0.31, 0.02]</td><td align="char" char="[">0.13<xref ref-type="fn" rid="tfn9" /> [0.06, 0.28]</td><td align="char" char="[">−0.02 [−0.21, 0.17]</td></tr><tr><td align="left">TAS → LII</td><td align="char" char="[">0.00 [−0.01, 0.04]</td><td align="char" char="[">0.16<xref ref-type="fn" rid="tfn10" /> [0.06, 0.30]</td><td align="char" char="[">0.32<xref ref-type="fn" rid="tfn10" /> [0.07, 0.52]</td><td align="char" char="[">0.16<xref ref-type="fn" rid="tfn9" /> [0.06, 0.31]</td><td align="char" char="[">0.48<xref ref-type="fn" rid="tfn11" /> [0.29, 0.68]</td></tr><tr><td align="left">TRS → LII</td><td align="char" char="[">0.00 [−0.01, 0.04]</td><td align="char" char="[">−0.00 [−0.09, 0.05]</td><td align="char" char="[">0.40<xref ref-type="fn" rid="tfn11" /> [0.22, 0.57]</td><td align="char" char="[">0.00 [−0.08, 0.06]</td><td align="char" char="[">0.40<xref ref-type="fn" rid="tfn11" /> [0.21, 0.58]</td></tr><tr><td align="left">DAS → LII</td><td align="char" char="[">−0.00 [−0.04, 0.01]</td><td align="char" char="[">0.01 [−0.06, 0.07]</td><td align="char" char="[">0.09 [−0.07, 0.23]</td><td align="char" char="[">0.01 [−0.08, 0.07]</td><td align="char" char="[">0.09 [−0.06, 0.24]</td></tr><tr><td align="left">DCS → LII</td><td align="char" char="[">0.02 [−0.03, 0.09]</td><td align="char" char="[">0.02 [−0.06, 0.14]</td><td align="char" char="[">−0.04 [−0.30, 0.19]</td><td align="char" char="[">0.04 [−0.05, 0.17]</td><td align="char" char="[">0.00 [−0.28, 0.25]</td></tr><tr><td align="left">DRS → LII</td><td align="char" char="[">0.01 [−0.02, 0.06]</td><td align="char" char="[">0.09<xref ref-type="fn" rid="tfn9" /> [0.03, 0.19]</td><td align="char" char="[">−0.03 [−0.20, 0.16]</td><td align="char" char="[">0.10 [0.03, 0.23]</td><td align="char" char="[">0.07 [−0.13, 0.27]</td></tr><tr><td align="left">TAS → LLI</td><td align="char" char="[">0.01 [−0.03, 0.06]</td><td align="char" char="[">0.15<xref ref-type="fn" rid="tfn9" /> [0.06, 0.30]</td><td align="char" char="[">0.31<xref ref-type="fn" rid="tfn9" /> [0.03, 0.55]</td><td align="char" char="[">0.16<xref ref-type="fn" rid="tfn9" /> [0.05, 0.31]</td><td align="char" char="[">0.47<xref ref-type="fn" rid="tfn11" /> [0.22, 0.72]</td></tr><tr><td align="left">TRS → LLI</td><td align="char" char="[">0.02 [−0.01, 0.08]</td><td align="char" char="[">−0.00 [−0.08, 0.05]</td><td align="char" char="[">0.06 [−0.16, 0.27]</td><td align="char" char="[">0.01 [−0.08, 0.09]</td><td align="char" char="[">0.08 [−0.16, 0.30]</td></tr><tr><td align="left">DAS → LLI</td><td align="char" char="[">−0.01 [−0.07, 0.02]</td><td align="char" char="[">0.01 [−0.06, 0.06]</td><td align="char" char="[">0.31<xref ref-type="fn" rid="tfn10" /> [0.11, 0.49]</td><td align="char" char="[">0.00 [−0.11, 0.06]</td><td align="char" char="[">0.31<xref ref-type="fn" rid="tfn10" /> [0.08, 0.49]</td></tr><tr><td align="left">DCS → LLI</td><td align="char" char="[">0.06 [0.01, 0.19]</td><td align="char" char="[">0.02 [−0.05, 0.13]</td><td align="char" char="[">−0.11 [−0.39, 0.18]</td><td align="char" char="[">0.09 [−0.01, 0.24]</td><td align="char" char="[">−0.02 [−0.29, 0.28]</td></tr><tr><td align="left">DRS → LLI</td><td align="char" char="[">0.05 [0.01, 0.13]</td><td align="char" char="[">0.08<xref ref-type="fn" rid="tfn9" /> [0.03, 0.19]</td><td align="char" char="[">−0.09 [−0.27, 0.11]</td><td align="char" char="[">0.13<xref ref-type="fn" rid="tfn9" /> [0.06, 0.28]</td><td align="char" char="[">0.04 [−0.16, 0.25]</td></tr><tr><td align="left">TAS → LTI</td><td align="char" char="[">0.02 [−0.08, 0.10]</td><td align="char" char="[">0.05 [−0.01, 0.16]</td><td align="char" char="[">0.16 [−0.13, 0.43]</td><td align="char" char="[">0.07 [−0.05, 0.20]</td><td align="char" char="[">0.23 [−0.00, 0.47]</td></tr><tr><td align="left">TRS → LTI</td><td align="char" char="[">0.03 [−0.03, 0.12]</td><td align="char" char="[">−0.00 [−0.04, 0.02]</td><td align="char" char="[">−0.13 [−0.35, 0.09]</td><td align="char" char="[">0.03 [−0.05, 0.12]</td><td align="char" char="[">−0.09 [−0.33, 0.12]</td></tr><tr><td align="left">DAS → LTI</td><td align="char" char="[">−0.02 [−0.11, 0.04]</td><td align="char" char="[">0.00 [−0.01, 0.04]</td><td align="char" char="[">−0.08 [−0.31, 0.11]</td><td align="char" char="[">−0.02 [−0.12, 0.04]</td><td align="char" char="[">−0.10 [−0.34, 0.10]</td></tr><tr><td align="left">DCS → LTI</td><td align="char" char="[">0.13<xref ref-type="fn" rid="tfn9" /> [0.04, 0.28]</td><td align="char" char="[">0.01 [−0.01, 0.08]</td><td align="char" char="[">0.41<xref ref-type="fn" rid="tfn10" /> [0.10, 0.74]</td><td align="char" char="[">0.14<xref ref-type="fn" rid="tfn9" /> [0.03, 0.28]</td><td align="char" char="[">0.55<xref ref-type="fn" rid="tfn10" /> [0.24, 0.90]</td></tr><tr><td align="left">DRS → LTI</td><td align="char" char="[">0.09<xref ref-type="fn" rid="tfn9" /> [0.03, 0.19]</td><td align="char" char="[">0.03 [−0.01, 0.11]</td><td align="char" char="[">0.13 [−0.06, 0.34]</td><td align="char" char="[">0.12<xref ref-type="fn" rid="tfn10" /> [0.05, 0.23]</td><td align="char" char="[">0.25<xref ref-type="fn" rid="tfn9" /> [0.05, 0.47]</td></tr><tr><td align="left">TAS → GS</td><td align="char" char="[">0.01 [−0.05, 0.07]</td><td align="char" char="[">0.10 [0.02, 0.23]</td><td align="char" char="[">0.21 [−0.03, 0.39]</td><td align="char" char="[">0.11 [0.01, 0.25]</td><td align="char" char="[">0.32<xref ref-type="fn" rid="tfn10" /> [0.12, 0.50]</td></tr><tr><td align="left">TRS → GS</td><td align="char" char="[">0.02 [−0.02, 0.08]</td><td align="char" char="[">−0.00 [−0.06, 0.04]</td><td align="char" char="[">−0.06 [−0.22, 0.09]</td><td align="char" char="[">0.02 [−0.06, 0.10]</td><td align="char" char="[">−0.04 [−0.22, 0.12]</td></tr><tr><td align="left">DAS → GS</td><td align="char" char="[">−0.02 [−0.09, 0.02]</td><td align="char" char="[">0.01 [−0.04, 0.05]</td><td align="char" char="[">0.12 [−0.06, 0.25]</td><td align="char" char="[">−0.01 [−0.11, 0.05]</td><td align="char" char="[">0.11 [−0.08, 0.25]</td></tr><tr><td align="left">DCS → GS</td><td align="char" char="[">0.09 [0.03, 0.23]</td><td align="char" char="[">0.01 [−0.03, 0.09]</td><td align="char" char="[">−0.04 [−0.29, 0.19]</td><td align="char" char="[">0.10 [0.01, 0.26]</td><td align="char" char="[">0.06 [−0.17, 0.29]</td></tr><tr><td align="left">DRS → GS</td><td align="char" char="[">0.06<xref ref-type="fn" rid="tfn9" /> [0.02, 0.15]</td><td align="char" char="[">0.05 [0.00, 0.16]</td><td align="char" char="[">0.10 [−0.08, 0.25]</td><td align="char" char="[">0.11<xref ref-type="fn" rid="tfn9" /> [0.04, 0.26]</td><td align="char" char="[">0.22<xref ref-type="fn" rid="tfn10" /> [0.09, 0.38]</td></tr><tr><td align="left">TAS → LCI</td><td align="char" char="[">0.01 [−0.04, 0.07]</td><td align="char" char="[">0.15<xref ref-type="fn" rid="tfn9" /> [0.05, 0.31]</td><td align="char" char="[">0.12 [−0.15, 0.34]</td><td align="char" char="[">0.16<xref ref-type="fn" rid="tfn9" /> [0.05, 0.32]</td><td align="char" char="[">0.28<xref ref-type="fn" rid="tfn9" /> [0.07, 0.51]</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 <emph>Note</emph>: Coefficients in square brackets are 95% confidence intervals resulting from bootstrapping with 1000 draws.</item> <item>8 Abbreviations: AE, agentic engagement; DAS, digital autonomy support; DCS, digital competence support; DRS, digital relatedness support; EE, emotional engagement; GS, general satisfaction; LCI, learner–content interaction; LII, learner–instructor interaction; LLI, learner–learner interaction; LTI, learner–technology interaction; TAS, teacher autonomy support; TCS, teacher competence support; TRS, teacher relatedness support.</item> <item>9 * <emph>p</emph> < 0.05.</item> <item>10 ** <emph>p</emph> < 0.01.</item> <item>11 *** <emph>p</emph> < 0.001.</item> </ulist> <hd id="AN0186252682-34">DISCUSSION</hd> <p>A major contribution of this study was the empirical examination of the theoretical–conceptual model where teacher support and digital support predicted student engagement, which in turn predicted learner satisfaction. The results indicated that agentic engagement mediated the relationships between (<reflink idref="bib1" id="ref158">1</reflink>) teacher autonomy support and leaner–content interaction, (<reflink idref="bib2" id="ref159">2</reflink>) teacher autonomy support and learner–instructor interaction, (<reflink idref="bib3" id="ref160">3</reflink>) teacher autonomy support and learner–learner interaction, (<reflink idref="bib4" id="ref161">4</reflink>) digital relatedness support and learner–content interaction, (<reflink idref="bib5" id="ref162">5</reflink>) digital relatedness support and learner–instructor interaction and (<reflink idref="bib6" id="ref163">6</reflink>) digital relatedness support and learner–learner interaction. We also found that emotional engagement mediated the relationships between (<reflink idref="bib1" id="ref164">1</reflink>) digital relatedness support and learner–technology interaction, (<reflink idref="bib2" id="ref165">2</reflink>) digital relatedness support and general satisfaction and (<reflink idref="bib3" id="ref166">3</reflink>) digital competence support and learner–technology interaction. Our data provided partial support for the hypothesised model, in that the fit statistics were satisfactory and several hypothesised relations among variables were found to be significant.</p> <hd id="AN0186252682-35">Relationships between teacher support and student engagement</hd> <p>The first key finding demonstrated that teacher autonomy support significantly predicted agentic engagement, suggesting that by encouraging students to ask questions, offering students choices about learning goals and strategies, empowering students to see the importance of their course material and accommodating different learning styles, students are likely to make more proactive effort to constructively shape their own learning. This finding partially supports H1 and is in line with Reeve ([<reflink idref="bib68" id="ref167">68</reflink>]), where providing autonomy support enabled students to communicate their learning agendas more effectively with their teachers. However, of note is the absence of significant predictive relations of teacher autonomy support on other facets of student engagement and teacher relatedness support on any student engagement dimensions. This is likely due to the controlling of shared variance with other variables involved in the analysis, especially since teacher autonomy and relatedness support are correlated highly (see Table 3). It appears that teacher autonomy support did not significantly predict behavioural and emotional engagement, indicating that the provision of choice in learning did not result in greater involvement in learning activities or better affective reactions towards blended learning, contradicting findings by Vansteenkiste et al. ([<reflink idref="bib84" id="ref168">84</reflink>]), Skinner et al. ([<reflink idref="bib74" id="ref169">74</reflink>]) and Yang et al. ([<reflink idref="bib88" id="ref170">88</reflink>]). This could be due to individual differences, as task value and perceived workload were observed to significantly predict behavioural engagement, while age significantly predicted emotional engagement (see Table 5). It might be the case that because students perceived the course as interesting, important and useful, it triggered them to participate more in the learning activities, more so than the autonomy support their teachers provided. Additionally, the feeling of being overwhelmed with the amount of time pressure to complete the assignments and tasks in the module could have spurred students to be more involved in the learning activities, as they knew that they had to keep up with the activities closely in order to get through the module. Older students also had more positive affective reactions to blended learning as compared to younger students, possibly because they have more experience with independent learning and are therefore more comfortable with navigating autonomously in the blended learning environment.</p> <hd id="AN0186252682-36">Relationships between digital support and student engagement</hd> <p>The next important finding indicated that digital competence and relatedness support were significantly predictive of emotional engagement. This suggests that when students perceived themselves as good at using the digital tools, able to learn interesting new knowledge with the tools, felt a sense of accomplishment from learning with the tools and felt that the tools made them more connected to their peers, instructors and content, they were more likely to feel more enthusiastic about blended learning, find blended learning rewarding and enjoy learning things in the blended learning environment. Digital relatedness support was also found to significantly predict agentic engagement, indicating that when students felt the digital tools made them more connected to their peers, instructors and content, they were more likely to proactively engage in efforts that contribute to their own learning. These findings partially support H2 and align with Chiu's ([<reflink idref="bib23" id="ref171">23</reflink>]) work, which found that digital relatedness support predicts agentic, behavioural and emotional engagement. They also correspond with Nkomo's ([<reflink idref="bib65" id="ref172">65</reflink>]) systematic review, which highlighted that digital support through learning management systems and social media promotes both cognitive and emotional engagement among students. However, these findings diverge from findings by Chiu ([<reflink idref="bib22" id="ref173">22</reflink>]) showing that digital competence support only correlated strongly with cognitive engagement, relatedness support correlated with emotional engagement and autonomy support correlated with agentic, behavioural and cognitive engagement. When students have high digital competence, they are more confident and comfortable in using digital tools, leading to less anxiety and frustration, resulting in a more positive affective reaction to blended learning. It is also possible that being able to use the digital tools to relate to peers, instructors and content enhances students' sense of belonging to a wider learning community, making them more motivated to participate in the blended learning environment and developing more positive feelings towards it. As a result, students take proactive efforts to constructively contribute to their own learning.</p> <hd id="AN0186252682-37">Relationships between student engagement and learner satisfaction</hd> <p>Our correlational analysis and empirical model demonstrated that behavioural engagement positively and significantly predicted learner–learner interaction, suggesting that the more students participate in learning activities of the module, the more likely learners are satisfied with the two‐way communication between each other. This finding is unsurprising, considering that those who are actively involved in the learning activities tend to communicate, collaborate and interact more with their peers, resulting in more meaningful and frequent learner–learner interactions.</p> <p>A noteworthy finding was the positive and significant predictive relations that emotional engagement showed with four aspects of learner satisfaction, namely, learner–content interaction, learner–learner interaction, learner–technology interaction and general satisfaction. This implies that the more positive one's affective reaction is towards blended learning, the more satisfied learners are with the course contents, lessons, learning activities, two‐way communication with their peers, abilities and level of comfort in their interactions with online environments and overall satisfaction with the module. These predictive relations are in line with results from Gao et al. ([<reflink idref="bib44" id="ref174">44</reflink>]), who found that emotional engagement significantly enhanced learner satisfaction.</p> <p>Similarly, we observed that agentic engagement displayed significant and positive predictive relations with four facets of learner satisfaction (i.e., learner–content, learner–instructor, learner–learner and general satisfaction), suggesting that when students take proactive efforts to constructively contribute to their own learning, they are more likely to feel more satisfied with the course contents, lessons, learning activities, two‐way communication with their peers and instructors and overall satisfaction with the module. When students take the initiative to let their instructor know their learning preferences, what they are interested in and express their opinions, they tend to seek resources, ask questions and explore topics more deeply, enriching their interaction with the learning materials. Additionally, by communicating their needs and feedback to their instructors, they form stronger relationships with their instructors and become more responsive to the teaching methods. Students' proactiveness can also mean more participation in learning activities, enabling better peer interactions and collaborations. Overall, the sense of control and active participation in the learning processes implies a more positive and satisfying learning experience within the module, ultimately leading to higher general satisfaction. In summary, the findings provide partial support for H3.</p> <hd id="AN0186252682-38">Mediating role of student engagement in relationship between support and leaner satisfaction</hd> <p>In terms of the mediating effects of specific student engagement dimensions, we found that only agentic and emotional engagement mediated the relationships between teacher and digital support and learner satisfaction, lending partial support to H4.1 and H4.2. Specifically, taking proactive steps in one's learning could explain why offering choices around learning led to an improvement in how satisfied learners were with their interactions with the course content, two‐way interaction between learners and instructors, and between learners. Affording students autonomy in learning helps foster a sense of control, competence and even relatedness among students, increasing their intrinsic motivation and making them take more ownership of their learning. This likely led to a higher sense of agency, resulting in more meaningful interactions with content, instructors and their fellow peers.</p> <p>Additionally, when students were proactive in their learning, it explained why feeling more connected to their peers, instructors and course content through the digital tools enhanced learners' satisfaction with their interactions with the course content, instructors and two‐way communication with their peers. This is because, when students feel that the digital tools can support them adequately, they are more likely to be confident with using these tools and therefore take the initiative to interact more with the content, instructors and their fellow peers.</p> <p>We also noticed that better affective reactions about the blended learning environment could explain why (<reflink idref="bib1" id="ref175">1</reflink>) feeling more connected to peers, instructors and content via the digital tools improved students' satisfaction with their ability and level of comfort with interactions with online environments and their overall satisfaction with the module and (<reflink idref="bib2" id="ref176">2</reflink>) feeling that one is competent with the use of the digital tools enhanced students' satisfaction with their ability and level of comfort with interactions with online environments. These findings concur with results from Gao et al. ([<reflink idref="bib44" id="ref177">44</reflink>]), who found that emotional engagement functioned as a mediator between the perceptions of the digital platform (e.g., playfulness, usefulness, ease of use, interaction) and course satisfaction.</p> <p>Overall, we observed that emotional and agentic engagement played major roles in mediating the relationships between various aspects of teacher and digital support and learner satisfaction. Interestingly, behavioural and cognitive engagement did not play a role in mediating these relationships. We posit two plausible explanations. First, the correlational analysis showed high correlations among the student engagement constructs (<emph>r</emph> > 0.50), signalling the possibility of overlap between these dimensions. This could obscure the mediating roles of cognitive and behavioural engagement. Second, there could be multiple interacting factors between the types of support and learner satisfaction that were not explicitly examined in this study. For example, task value, which was controlled for as a covariate, showed significant predictive relations with four learner satisfaction facets: learner–content interaction, learner–instructor interaction, learner–technology interaction and general satisfaction. However, its interaction with student engagement and the types of support have yet to be examined. Future studies could therefore be carried out to shed light on how task value interacts with student engagement, digital and teacher support, in affecting learner satisfaction.</p> <hd id="AN0186252682-39">Practical implications</hd> <p>Despite the limitations above, our findings provide two important practical implications for academic staff on how to foster emotional and agentic engagement to enhance the relations between the provision of support and learner satisfaction. First, it is important to create emotionally engaging technological environments (Chiu, [<reflink idref="bib22" id="ref178">22</reflink>]), where students feel enthusiastic about participating in the blended learning. This could be done through the provision of support in using the digital tools, such as demonstrating how certain simulation software tools can be used to visualise and understand the anatomical structure of the human body. Alternatively, instructors could use digital tools like Telegram, Microsoft Teams and discussion forums to promote communication with and between students and invite discussions around a topic or issue related to the content covered. Additionally, the proliferation of generative artificial intelligence (GenAI) offers instructors opportunities to harness tools like ChatGPT, GitHub Copilot and Mid‐Journey to provide personalised learning experiences, fostering deeper student engagement on an individual level (Chiu, [<reflink idref="bib24" id="ref179">24</reflink>]). Notwithstanding, the face‐to‐face segment of blended learning can also be structured such that it encourages peer‐to‐peer and instructor‐to‐student interactions rather than a predominantly one‐way interaction from instructor to student. For instance, instructors could design vignettes illustrating certain content concepts for students to discuss collaboratively, share with the class, allow for inter‐group feedback), and consolidate the key points (Kok, [<reflink idref="bib55" id="ref180">55</reflink>]).</p> <p>Second, there is a need to instil a sense of agency in students, especially since blended learning requires that learners be more independent, and engage autonomously and actively in the learning process (Broadbent & Poon, [<reflink idref="bib11" id="ref181">11</reflink>]). One way to cultivate this sense of agency is for instructors to allow for choices around learning, such as encouraging students to ask questions, getting students to set their own learning goals and strategies for the module, designing learning experiences that demonstrate the practical application of the module's content, enabling students to see its relevance, and design the content materials to accommodate different learning styles. To encourage students to ask questions, discussion forums could be set up where students can ask questions anonymously or non‐anonymously, depending on how comfortable they feel. Students could be encouraged to monitor their learning goals and strategies throughout the module's duration and asked to evaluate them at the end of the module. Lessons could also be designed to incorporate actual case studies and scenarios relevant to the module's content, where students can see how the content is applied in the real world. Finally, multiple formats such as visual (e.g., diagrams, infographics), auditory (e.g., podcasts, recorded lectures) and kinaesthetic (e.g., simulations, hands‐on activities) can be built into the lesson materials to cater for different learning styles. Content delivery methods can also be varied to provide textual (e.g., articles, textbooks), interactive (e.g., online platforms, quizzes) and experiential (e.g., labs, fieldwork) learning experiences for students.</p> <hd id="AN0186252682-40">CONCLUSION</hd> <p>In general, the results of this study demonstrate support for our empirical structural model. Specifically, we found that agentic and emotional engagement mediated the relationships between certain types of support and learner satisfaction. We also observed significant predictive relations between the types of support and student engagement, and student engagement and learner satisfaction. Our study therefore highlights the importance of affectively engaging students and imbuing a sense of agency in them, in order for the type of support provided (be it teacher or digital) to enhance satisfaction with the interactions within a blended learning environment.</p> <hd id="AN0186252682-41">Limitations and future directions</hd> <p>Despite the novel findings, several limitations of the present study should be acknowledged when interpreting the results. First, the study relied primarily on self‐report measures (e.g., perceived teacher support, student engagement, learner satisfaction), which could introduce innate biases such as social desirability (Chung & Monroe, [<reflink idref="bib30" id="ref182">30</reflink>]) and acquiescence (Duckworth & Yeager, [<reflink idref="bib34" id="ref183">34</reflink>]) biases. Future research could consider the use of multiple data sources (e.g., interviews, observations) to triangulate findings from self‐reported measures. Second, the use of a cross‐sectional research design means that causal relationships among variables could not be inferred. However, this does not undermine the validity of our findings as the predictive relationships in our hypothesised model were based on appropriate empirical evidence from the literature. To test causal relationship more robustly, future studies should adopt a longitudinal design, collecting data at different points in time throughout the academic year or across different academic years.</p> <hd id="AN0186252682-42">ACKNOWLEDGEMENTS</hd> <p>The authors would like to express their gratitude to (<reflink idref="bib1" id="ref184">1</reflink>) all survey participants for their willingness to contribute to this study, (<reflink idref="bib2" id="ref185">2</reflink>) colleagues from SIT Teaching and Learning Academy for their assistance in administering the survey and (<reflink idref="bib3" id="ref186">3</reflink>) programme leaders, module coordinators and teaching staff for their generosity in granting access to the student participants. The authors would also like to express their gratitude to Peter Looker for his valuable advice, insights, and suggestions during the conceptualisation phase of the study.</p> <hd id="AN0186252682-43">FUNDING INFORMATION</hd> <p>The authors declare no funding sources.</p> <hd id="AN0186252682-44">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors declare no conflict of interest.</p> <hd id="AN0186252682-45">DATA AVAILABILITY STATEMENT</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p> <hd id="AN0186252682-46">ETHICS STATEMENT</hd> <p>Data collection from students for this study adhered strictly to ethical guidelines. The research protocol was reviewed and approved by the Institutional Review Board (IRB) of the Singapore Institute of Technology (SIT) under approval number RECAS‐0144. Informed consent was obtained from all individual participants included in the study. To protect confidentiality, all data were anonymised and securely stored, with access restricted solely to the research team.</p> <ref id="AN0186252682-47"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self‐determination and intrinsic motivation. Journal of Research on Technology in Education, 52 (3), 322 – 352. https://doi.org/10.1080/15391523.2020.1728449</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Alqurashi, E. (2018). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40 (1), 133 – 148. https://doi.org/10.1080/01587919.2018.1553562</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Ameloot, E., Rotsaert, T., Ameloot, T., Rienties, B., & Schellens, T. (2024). Supporting students' basic psychological needs and satisfaction in a blended learning environment through learning analytics. Computers & Education, 209, Article 104949. https://doi.org/10.1016/j.compedu.2023.104949</bibtext> </blist> <blist> <bibl id="bib4" idref="ref4" type="bt">4</bibl> <bibtext> Assor, A., Kaplan, H., & Roth, G. (2002). Choice is good, but relevance is excellent: Autonomy‐enhancing and suppressing teacher behaviours predicting students' engagement in schoolwork. British Journal of Educational Psychology, 72 (2), 261 – 278. https://doi.org/10.1348/000709902158883</bibtext> </blist> <blist> <bibl id="bib5" idref="ref91" type="bt">5</bibl> <bibtext> Baker, A. R., Lin, T.‐J., Chen, J., Paul, N., Anderson, R. C., & Nguyen‐Jahiel, K. (2017). Effects of teacher framing on student engagement during collaborative reasoning discussions. Contemporary Educational Psychology, 51, 253 – 266. https://doi.org/10.1016/j.cedpsych.2017.08.007</bibtext> </blist> <blist> <bibl id="bib6" idref="ref20" type="bt">6</bibl> <bibtext> Banihashem, S. K., Noroozi, O., Den Brok, P., Biemans, H. J. A., & Kerman, N. T. (2023). Modeling teachers' and students' attitudes, emotions, and perceptions in blended education: Towards post‐pandemic education. The International Journal of Management Education, 21 (2), Article 100803. https://doi.org/10.1016/j.ijme.2023.100803</bibtext> </blist> <blist> <bibl id="bib7" idref="ref69" type="bt">7</bibl> <bibtext> Bedenlier, S., Bond, M., Buntins, K., Zawacki‐Richter, O., & Kerres, M. (2020). Facilitating student engagement through educational technology in higher education: A systematic review in the field of arts and humanities. Australasian Journal of Educational Technology, 36 (4), 126 – 150. https://doi.org/10.14742/ajet.5477</bibtext> </blist> <blist> <bibl id="bib8" idref="ref147" type="bt">8</bibl> <bibtext> Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88 (3), 588 – 606. https://doi.org/10.1037/0033‐2909.88.3.588</bibtext> </blist> <blist> <bibl id="bib9" idref="ref15" type="bt">9</bibl> <bibtext> Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning: A systematic literature review. Educational Research Review, 22, 1 – 18. https://doi.org/10.1016/j.edurev.2017.06.001</bibtext> </blist> <blist> <bibtext> Böheim, R., Schnitzler, K., Gröschner, A., Weil, M., Knogler, M., Schindler, A.‐K., et al. (2021). How changes in teachers' dialogic discourse practice relate to changes in students' activation, motivation and cognitive engagement. Learning, Culture and Social Interaction, 28, Article 100450. https://doi.org/10.1016/j.lcsi.2020.100450</bibtext> </blist> <blist> <bibtext> Broadbent, J., & Poon, W. L. (2015). Self‐regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1 – 13. https://doi.org/10.1016/j.iheduc.2015.04.007</bibtext> </blist> <blist> <bibtext> Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press.</bibtext> </blist> <blist> <bibtext> Bruggeman, B., Tondeur, J., Struyven, K., Pynoo, B., Garone, A., & Vanslambrouck, S. (2021). Experts speaking: Crucial teacher attributes for implementing blended learning in higher education. The Internet and Higher Education, 48, Article 100772. https://doi.org/10.1016/j.iheduc.2020.100772</bibtext> </blist> <blist> <bibtext> Buckley, K., Stone, S., Farrell, A. M., Glynn, M., Lowney, R., & Smyth, S. (2021). Learning from student experience: Large, higher education classes transitioning online. Irish Educational Studies, 40 (2), 399 – 406. https://doi.org/10.1080/03323315.2021.1916566</bibtext> </blist> <blist> <bibtext> Buhl‐Wiggers, J., Kjærgaard, A., & Munk, K. (2023). A scoping review of experimental evidence on face‐to‐face components of blended learning in higher education. Studies in Higher Education, 48 (1), 151 – 173. https://doi.org/10.1080/03075079.2022.2123911</bibtext> </blist> <blist> <bibtext> Byrne, B. M. (2011). Structural equation modeling with Mplus. Routledge.</bibtext> </blist> <blist> <bibtext> Cao, W. (2023). A meta‐analysis of effects of blended learning on performance, attitude, achievement, and engagement across different countries. Frontiers in Psychology, 14, Article 1212056. https://doi.org/10.3389/fpsyg.2023.1212056</bibtext> </blist> <blist> <bibtext> Chang, K. (2013). Factors affecting student satisfaction in different learning deliveries [Doctoral dissertation]. Illinois State University, ISU ReD: Research and eData. https://doi.org/10.30707/ETD2013.Chang.K</bibtext> </blist> <blist> <bibtext> Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14 (3), 464 – 504. https://doi.org/10.1080/10705510701301834</bibtext> </blist> <blist> <bibtext> Chen, R. H. (2022). Effects of deliberate practice on blended learning sustainability: A community of inquiry perspective. Sustainability, 14 (3), Article 1785. https://doi.org/10.3390/su14031785</bibtext> </blist> <blist> <bibtext> Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness‐of‐fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9 (2), 233 – 255. https://doi.org/10.1207/S15328007SEM0902_5</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F. (2021). Digital support for student engagement in blended learning based on self‐determination theory. Computers in Human Behavior, 124, Article 106909. https://doi.org/10.1016/j.chb.2021.106909</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F. (2022). Applying the self‐determination theory (SDT) to explain student engagement in online learning during the COVID‐19 pandemic. Journal of Research on Technology in Education, 54 (S1), S14 – S30. https://doi.org/10.1080/15391523.2021.1891998</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F. (2023). Student engagement in K‐12 online learning amid COVID‐19: A qualitative approach from a self‐determination theory perspective. Interactive Learning Environments, 31 (6), 3326 – 3339. https://doi.org/10.1080/10494820.2021.1926289</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F., & Churchill, D. (2015). Exploring the characteristics of an optimal design of digital materials for concept learning in mathematics: Multimedia learning and variation theory. Computers & Education, 82, 280 – 291. https://doi.org/10.1016/j.compedu.2014.12.001</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F., & Hew, T. K. F. (2018). Factors influencing peer learning and performance in MOOC asynchronous online discussion forum. Australasian Journal of Educational Technology, 34 (4), 16 – 28. https://doi.org/10.14742/ajet.3240</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F., & Lim, C. P. (2020). Strategic use of technology for inclusive education in Hong Kong: A content‐level perspective. ECNU Review of Education, 3 (4), 715 – 734. https://doi.org/10.1177/2096531120930861</bibtext> </blist> <blist> <bibtext> Chiu, T. K. F., Lin, T.‐J., & Lonka, K. (2021). Motivating online learning: The challenges of COVID‐19 and beyond. The Asia‐Pacific Education Researcher, 30 (3), 187 – 190. https://doi.org/10.1007/s40299‐021‐00566‐w</bibtext> </blist> <blist> <bibtext> Christenson, S. L., Reschly, A. L., & Wylie, C. (2012). Handbook of research on student engagement. Springer. https://doi.org/10.1007/978‐1‐4614‐2018‐7</bibtext> </blist> <blist> <bibtext> Chung, J., & Monroe, G. S. (2003). Exploring social desirability bias. Journal of Business Ethics, 44 (4), 291 – 302. https://doi.org/10.1023/A:1023648703356</bibtext> </blist> <blist> <bibtext> Colreavy‐Donelly, S., Ryan, A., O'Connor, S., Caraffini, F., Kuhn, S., & Hasshu, S. (2022). A proposed VR platform for supporting blended learning post COVID‐19. Education Sciences, 12 (7), Article 435. https://doi.org/10.3390/educsci12070435</bibtext> </blist> <blist> <bibtext> Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson.</bibtext> </blist> <blist> <bibtext> Do Lago, M. M., Fernandes, E., & Barboza, R. C. (2023). The strategies of private higher educational institutions during the Covid‐19 pandemic. A review of literature. Social Sciences & Humanities Open, 8 (1), Article 100581. https://doi.org/10.1016/j.ssaho.2023.100581</bibtext> </blist> <blist> <bibtext> Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44 (4), 237 – 251. https://doi.org/10.3102/0013189X15584327</bibtext> </blist> <blist> <bibtext> Duffy, K., & Ney, J. (2015). Exploring the divides among students, educators, and practitioners in the use of digital media as a pedagogical tool. Journal of Marketing Education, 37 (2), 104 – 113. https://doi.org/10.1177/0273475315585826</bibtext> </blist> <blist> <bibtext> Dziuban, C., Picciano, A. G., Graham, C. R., & Moskal, P. D. (2016). Conducting research in online and blended learning environments: New pedagogical frontiers. Routledge.</bibtext> </blist> <blist> <bibtext> Efklides, A. (2002). The systemic nature of metacognitive experiences. In P. Chambres, M. Izaute, & P.‐J. Marescaux (Eds.), Metacognition (pp. 19 – 34). Springer. https://doi.org/10.1007/978‐1‐4615‐1099‐4_2</bibtext> </blist> <blist> <bibtext> Efklides, A. (2017). Affect, epistemic emotions, metacognition, and self‐regulated learning. Teachers College Record: The Voice of Scholarship in Education, 119 (13), 1 – 22. https://doi.org/10.1177/016146811711901302</bibtext> </blist> <blist> <bibtext> Fisher, R., Perényi, Á., & Birdthistle, N. (2021). The positive relationship between flipped and blended learning and student engagement, performance and satisfaction. Active Learning in Higher Education, 22 (2), 97 – 113. https://doi.org/10.1177/1469787418801702</bibtext> </blist> <blist> <bibtext> Fredricks, J. A. (2011). Engagement in school and out‐of‐school contexts: A multidimensional view of engagement. Theory into Practice, 50 (4), 327 – 335. https://doi.org/10.1080/00405841.2011.607401</bibtext> </blist> <blist> <bibtext> Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74 (1), 59 – 109. https://doi.org/10.3102/00346543074001059</bibtext> </blist> <blist> <bibtext> Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1 – 4. https://doi.org/10.1016/j.learninstruc.2016.02.002</bibtext> </blist> <blist> <bibtext> Fulmer, S. M., & Tulis, M. (2013). Changes in interest and affect during a difficult reading task: Relationships with perceived difficulty and reading fluency. Learning and Instruction, 27, 11 – 20. https://doi.org/10.1016/j.learninstruc.2013.02.001</bibtext> </blist> <blist> <bibtext> Gao, B. W., Jiang, J., & Tang, Y. (2020). The effect of blended learning platform and engagement on students' satisfaction – the case from the tourism management teaching. Journal of Hospitality, Leisure, Sport & Tourism Education, 27, Article 100272. https://doi.org/10.1016/j.jhlste.2020.100272</bibtext> </blist> <blist> <bibtext> George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.</bibtext> </blist> <blist> <bibtext> Halverson, L. R., & Graham, C. R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23 (2), 145 – 178. https://doi.org/10.24059/olj.v23i2.1481</bibtext> </blist> <blist> <bibtext> Halverson, L. R., Graham, C. R., Spring, K. J., & Drysdale, J. S. (2012). An analysis of high impact scholarship and publication trends in blended learning. Distance Education, 33 (3), 381 – 413. https://doi.org/10.1080/01587919.2012.723166</bibtext> </blist> <blist> <bibtext> Halverson, L. R., Graham, C. R., Spring, K. J., Drysdale, J. S., & Henrie, C. R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20, 20 – 34. https://doi.org/10.1016/j.iheduc.2013.09.004</bibtext> </blist> <blist> <bibtext> Hartnett, M. K. (2015). Influences that undermine learners' perceptions of autonomy, competence and relatedness in an online context. Australasian Journal of Educational Technology, 31 (1), 86 – 99. https://doi.org/10.14742/ajet.1526</bibtext> </blist> <blist> <bibtext> Hayes, A. F., Montoya, A. K., & Rockwood, N. J. (2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25 (1), 76 – 81. https://doi.org/10.1016/j.ausmj.2017.02.001</bibtext> </blist> <blist> <bibtext> Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6 (1), 1 – 55. https://doi.org/10.1080/10705519909540118</bibtext> </blist> <blist> <bibtext> IBM Corp. (2020). IBM SPSS statistics for Windows (version 27.0). [Computer software]. IBM Corporation.</bibtext> </blist> <blist> <bibtext> Kalyuga, S. (2014). The expertise reversal principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 576 – 597). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369.028</bibtext> </blist> <blist> <bibtext> Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.</bibtext> </blist> <blist> <bibtext> Kok X.‐F. K. 2024). Quick Fix: A Vignette‐Based Approach to Demystifying the Concept of Metacognition. College Teaching, 1 – 3. https://doi.org/10.1080/87567555.2024.2440892</bibtext> </blist> <blist> <bibtext> Knörzer, L., Brünken, R., & Park, B. (2016). Facilitators or suppressors: Effects of experimentally induced emotions on multimedia learning. Learning and Instruction, 44, 97 – 107. https://doi.org/10.1016/j.learninstruc.2016.04.002</bibtext> </blist> <blist> <bibtext> Kyndt, E., Dochy, F., Struyven, K., & Cascallar, E. (2011). The direct and indirect effect of motivation for learning on students' approaches to learning through the perceptions of workload and task complexity. Higher Education Research and Development, 30 (2), 135 – 150. https://doi.org/10.1080/07294360.2010.501329</bibtext> </blist> <blist> <bibtext> Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9 (8), 2207 – 2230. https://doi.org/10.4236/psych.2018.98126</bibtext> </blist> <blist> <bibtext> Lam, Y. W., Hew, K. F., & Chiu, K. F. (2018). Improving argumentative writing: Effects of a blended learning approach and gamification. Language Learning & Technology, 22 (1), 97 – 118. https://doi.org/10125/44583</bibtext> </blist> <blist> <bibtext> Liem, A. D., Lau, S., & Nie, Y. (2008). The role of self‐efficacy, task value, and achievement goals in predicting learning strategies, task disengagement, peer relationship, and achievement outcome. Contemporary Educational Psychology, 33 (4), 486 – 512. https://doi.org/10.1016/j.cedpsych.2007.08.001</bibtext> </blist> <blist> <bibtext> Lietaert, S., Roorda, D., Laevers, F., Verschueren, K., & De Fraine, B. (2015). The gender gap in student engagement: The role of teachers' autonomy support, structure, and involvement. British Journal of Educational Psychology, 85 (4), 498 – 518. https://doi.org/10.1111/bjep.12095</bibtext> </blist> <blist> <bibtext> Mayer, R. E., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12 – 18. https://doi.org/10.1016/j.learninstruc.2014.02.004</bibtext> </blist> <blist> <bibtext> Moore, M. G. (1973). Toward a theory of independent learning and teaching. The Journal of Higher Education, 44 (9), 661 – 679. https://doi.org/10.1080/00221546.1973.11776906</bibtext> </blist> <blist> <bibtext> Muthén, L. K. & Muthén, B. (1998 –2017). Mplus user's guide. Muthén & Muthén.</bibtext> </blist> <blist> <bibtext> Nkomo, L. M. (2021). Synthesis of student engagement with digital technologies: A systematic review of the literature. International Journal of Educational Technology in Higher Education, 18, Article 34. https://doi.org/10.1186/s41239‐021‐00270‐1</bibtext> </blist> <blist> <bibtext> Park, B., Knörzer, L., Plass, J. L., & Brünken, R. (2015). Emotional design and positive emotions in multimedia learning: An eye tracking study on the use of anthropomorphisms. Computers & Education, 86, 30 – 42. https://doi.org/10.1016/j.compedu.2015.02.016</bibtext> </blist> <blist> <bibtext> Pintrich, P., Smith, D., Garcia, T., & McKeachie, W. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). University of Michigan.</bibtext> </blist> <blist> <bibtext> Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105 (3), 579 – 595. https://doi.org/10.1037/a0032690</bibtext> </blist> <blist> <bibtext> Reeve, J., & Jang, H. (2006). What teachers say and do to support students' autonomy during a learning activity. Journal of Educational Psychology, 98 (1), 209 – 218. https://doi.org/10.1037/0022‐0663.98.1.209</bibtext> </blist> <blist> <bibtext> Ruzek, E. A., Hafen, C. A., Allen, J. P., Gregory, A., Mikami, A. Y., & Pianta, R. C. (2016). How teacher emotional support motivates students: The mediating roles of perceived peer relatedness, autonomy support, and competence. Learning and Instruction, 42, 95 – 103. https://doi.org/10.1016/j.learninstruc.2016.01.004</bibtext> </blist> <blist> <bibtext> Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self‐determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61, Article 101860. https://doi.org/10.1016/j.cedpsych.2020.101860</bibtext> </blist> <blist> <bibtext> Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13 (2), 141 – 156. https://doi.org/10.1016/S0959‐4752(02)00017‐8</bibtext> </blist> <blist> <bibtext> Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50 (1), 1 – 13. https://doi.org/10.1080/00461520.2014.1002924</bibtext> </blist> <blist> <bibtext> Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100 (4), 765 – 781. https://doi.org/10.1037/a0012840</bibtext> </blist> <blist> <bibtext> Spanjers, I. A. E., Könings, K. D., Leppink, J., Verstegen, D. M. L., De Jong, N., Czabanowska, K., & Van Merriënboer, J. J. G. (2015). The promised land of blended learning: Quizzes as a moderator. Educational Research Review, 15, 59 – 74. https://doi.org/10.1016/j.edurev.2015.05.001</bibtext> </blist> <blist> <bibtext> Spring, K. J., Graham, C. R., & Hadlock, C. A. (2016). The current landscape of international blended learning. International Journal of Technology Enhanced Learning, 8 (1), 84‐102. https://doi.org/10.1504/IJTEL.2016.075961</bibtext> </blist> <blist> <bibtext> Sudirman, A., Gemilang, A. V., Kristanto, T. M. A., Male, H., & Kurniawan, R. (2023). The impact of pandemic writing on EFL students' critical thinking skills: An exploratory study. Journal of English Development, 3 (2), 65 – 79. https://doi.org/10.25217/jed.v3i01.3374</bibtext> </blist> <blist> <bibtext> Syska, A., & Pritchard, C. (2023). Blended learning as a site of struggle: A critical realist analysis of students' perceptions of blended learning and its impact on their sense of belonging. Journal of University Teaching and Learning Practice, 20 (6), Article 15. https://doi.org/10.53761/1.20.6.15</bibtext> </blist> <blist> <bibtext> Torrado, M., & Blanca, M. J. (2022). Assessing satisfaction with online courses: Spanish version of the learner satisfaction survey. Frontiers in Psychology, 13, Article 875929. https://doi.org/10.3389/fpsyg.2022.875929</bibtext> </blist> <blist> <bibtext> Trenshaw, K. F., Revelo, R. A., Earl, K. A., & Herman, G. L. (2016). Using self determination theory principles to promote engineering students' intrinsic motivation to learn. International Journal of Engineering Education, 32 (3(A)), 1194 – 1207. https://selfdeterminationtheory.org/wp‐content/uploads/2017/05/2016_Trenshaw_etal_IJEE.pdf</bibtext> </blist> <blist> <bibtext> Vanslambrouck, S., Zhu, C., Lombaerts, K., Philipsen, B., & Tondeur, J. (2018). Students' motivation and subjective task value of participating in online and blended learning environments. The Internet and Higher Education, 36, 33 – 40. https://doi.org/10.1016/j.iheduc.2017.09.002</bibtext> </blist> <blist> <bibtext> Vansteenkiste, M., Ryan, R. M., & Soenens, B. (2020). Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion, 44 (1), 1 – 31. https://doi.org/10.1007/s11031‐019‐09818‐1</bibtext> </blist> <blist> <bibtext> Vansteenkiste, M., Sierens, E., Soenens, B., Luyckx, K., & Lens, W. (2009). Motivational profiles from a self‐determination perspective: The quality of motivation matters. Journal of Educational Psychology, 101 (3), 671 – 688. https://doi.org/10.1037/a0015083</bibtext> </blist> <blist> <bibtext> Vansteenkiste, M., Zhou, M., Lens, W., & Soenens, B. (2005). Experiences of autonomy and control among Chinese learners: Vitalizing or immobilizing? Journal of Educational Psychology, 97 (3), 468 – 483. https://doi.org/10.1037/0022‐0663.97.3.468</bibtext> </blist> <blist> <bibtext> Vaughan, N. (2014). Student engagement and blended learning: Making the assessment connection. Education Sciences, 4 (4), 247 – 264. https://doi.org/10.3390/educsci4040247</bibtext> </blist> <blist> <bibtext> Vollet, J. W., Kindermann, T. A., & Skinner, E. A. (2017). In peer matters, teachers matter: Peer group influences on students' engagement depend on teacher involvement. Journal of Educational Psychology, 109 (5), 635 – 652. https://doi.org/10.1037/edu0000172</bibtext> </blist> <blist> <bibtext> Xie, K., & Ke, F. (2011). The role of students' motivation in peer‐moderated asynchronous online discussions: Motivation in peer‐moderated discussions. British Journal of Educational Technology, 42 (6), 916 – 930. https://doi.org/10.1111/j.1467‐8535.2010.01140.x</bibtext> </blist> <blist> <bibtext> Yang, D., Chen, P., Wang, H., Wang, K., & Huang, R. (2022). Teachers' autonomy support and student engagement: A systematic literature review of longitudinal studies. Frontiers in Psychology, 13, Article 925955. https://doi.org/10.3389/fpsyg.2022.925955</bibtext> </blist> <blist> <bibtext> Yilmaz, R., & Yilmaz, F. G. K. (2020). Examination of the effectiveness of the task and group awareness support system used for computer‐supported collaborative learning. Educational Technology Research and Development, 68 (3), 1355 – 1380. https://doi.org/10.1007/s11423‐020‐09741‐0</bibtext> </blist> <blist> <bibtext> Yousaf, H. Q., Rehman, S., Ahmed, M., & Munawar, S. (2023). Investigating students' satisfaction in online learning: The role of students' interaction and engagement in universities. Interactive Learning Environments, 31 (10), 7104 – 7121. https://doi.org/10.1080/10494820.2022.2061009</bibtext> </blist> </ref> <aug> <p>By Xiao‐Feng Kenan Kok; Ching Yee Pua; Shermain Puah; Oran Zane Devilly; Peng Cheng Wang and Eric Chern‐Pin Chua</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib78" firstref="ref5"></nolink> <nolink nlid="nl2" bibid="bib31" firstref="ref6"></nolink> <nolink nlid="nl3" bibid="bib77" firstref="ref7"></nolink> <nolink nlid="nl4" bibid="bib20" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib33" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib14" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib13" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib15" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib36" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib75" firstref="ref16"></nolink> <nolink nlid="nl11" bibid="bib35" firstref="ref17"></nolink> <nolink nlid="nl12" bibid="bib85" firstref="ref18"></nolink> <nolink nlid="nl13" bibid="bib17" firstref="ref19"></nolink> <nolink nlid="nl14" bibid="bib11" firstref="ref21"></nolink> <nolink nlid="nl15" bibid="bib22" firstref="ref22"></nolink> <nolink nlid="nl16" bibid="bib59" firstref="ref23"></nolink> <nolink nlid="nl17" bibid="bib73" firstref="ref24"></nolink> <nolink nlid="nl18" bibid="bib48" firstref="ref25"></nolink> <nolink nlid="nl19" bibid="bib76" firstref="ref26"></nolink> <nolink nlid="nl20" bibid="bib47" firstref="ref27"></nolink> <nolink nlid="nl21" bibid="bib46" firstref="ref28"></nolink> <nolink nlid="nl22" bibid="bib42" firstref="ref31"></nolink> <nolink nlid="nl23" bibid="bib44" firstref="ref33"></nolink> <nolink nlid="nl24" bibid="bib29" firstref="ref35"></nolink> <nolink nlid="nl25" bibid="bib40" firstref="ref37"></nolink> <nolink nlid="nl26" bibid="bib68" firstref="ref38"></nolink> <nolink nlid="nl27" bibid="bib41" firstref="ref41"></nolink> <nolink nlid="nl28" bibid="bib63" firstref="ref47"></nolink> <nolink nlid="nl29" bibid="bib49" firstref="ref50"></nolink> <nolink nlid="nl30" bibid="bib61" firstref="ref54"></nolink> <nolink nlid="nl31" bibid="bib83" firstref="ref55"></nolink> <nolink nlid="nl32" bibid="bib86" firstref="ref56"></nolink> <nolink nlid="nl33" bibid="bib71" firstref="ref57"></nolink> <nolink nlid="nl34" bibid="bib82" firstref="ref60"></nolink> <nolink nlid="nl35" bibid="bib69" firstref="ref65"></nolink> <nolink nlid="nl36" bibid="bib84" firstref="ref66"></nolink> <nolink nlid="nl37" bibid="bib74" firstref="ref67"></nolink> <nolink nlid="nl38" bibid="bib26" firstref="ref73"></nolink> <nolink nlid="nl39" bibid="bib27" firstref="ref75"></nolink> <nolink nlid="nl40" bibid="bib87" firstref="ref82"></nolink> <nolink nlid="nl41" bibid="bib89" firstref="ref89"></nolink> <nolink nlid="nl42" bibid="bib88" firstref="ref90"></nolink> <nolink nlid="nl43" bibid="bib10" firstref="ref92"></nolink> <nolink nlid="nl44" bibid="bib25" firstref="ref94"></nolink> <nolink nlid="nl45" bibid="bib72" firstref="ref95"></nolink> <nolink nlid="nl46" bibid="bib28" firstref="ref96"></nolink> <nolink nlid="nl47" bibid="bib53" firstref="ref97"></nolink> <nolink nlid="nl48" bibid="bib62" firstref="ref100"></nolink> <nolink nlid="nl49" bibid="bib66" firstref="ref101"></nolink> <nolink nlid="nl50" bibid="bib56" firstref="ref102"></nolink> <nolink nlid="nl51" bibid="bib70" firstref="ref107"></nolink> <nolink nlid="nl52" bibid="bib80" firstref="ref108"></nolink> <nolink nlid="nl53" bibid="bib23" firstref="ref109"></nolink> <nolink nlid="nl54" bibid="bib65" firstref="ref112"></nolink> <nolink nlid="nl55" bibid="bib90" firstref="ref119"></nolink> <nolink nlid="nl56" bibid="bib39" firstref="ref120"></nolink> <nolink nlid="nl57" bibid="bib67" firstref="ref121"></nolink> <nolink nlid="nl58" bibid="bib81" firstref="ref122"></nolink> <nolink nlid="nl59" bibid="bib57" firstref="ref123"></nolink> <nolink nlid="nl60" bibid="bib37" firstref="ref124"></nolink> <nolink nlid="nl61" bibid="bib32" firstref="ref125"></nolink> <nolink nlid="nl62" bibid="bib50" firstref="ref126"></nolink> <nolink nlid="nl63" bibid="bib43" firstref="ref128"></nolink> <nolink nlid="nl64" bibid="bib38" firstref="ref129"></nolink> <nolink nlid="nl65" bibid="bib18" firstref="ref134"></nolink> <nolink nlid="nl66" bibid="bib79" firstref="ref135"></nolink> <nolink nlid="nl67" bibid="bib52" firstref="ref136"></nolink> <nolink nlid="nl68" bibid="bib64" firstref="ref137"></nolink> <nolink nlid="nl69" bibid="bib16" firstref="ref138"></nolink> <nolink nlid="nl70" bibid="bib54" firstref="ref139"></nolink> <nolink nlid="nl71" bibid="bib12" firstref="ref140"></nolink> <nolink nlid="nl72" bibid="bib51" firstref="ref141"></nolink> <nolink nlid="nl73" bibid="bib58" firstref="ref148"></nolink> <nolink nlid="nl74" bibid="bib45" firstref="ref151"></nolink> <nolink nlid="nl75" bibid="bib21" firstref="ref153"></nolink> <nolink nlid="nl76" bibid="bib19" firstref="ref154"></nolink> <nolink nlid="nl77" bibid="bib60" firstref="ref155"></nolink> <nolink nlid="nl78" bibid="bib24" firstref="ref179"></nolink> <nolink nlid="nl79" bibid="bib55" firstref="ref180"></nolink> <nolink nlid="nl80" bibid="bib30" firstref="ref182"></nolink> <nolink nlid="nl81" bibid="bib34" firstref="ref183"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1475462
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: The Mediating Role of Student Engagement in the Relationship between Teacher and Digital Support and Learner Satisfaction in Blended Learning Environments at Higher Education
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Xiao-Feng+Kenan+Kok%22">Xiao-Feng Kenan Kok</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8679-7641">0000-0002-8679-7641</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ching+Yee+Pua%22">Ching Yee Pua</searchLink><br /><searchLink fieldCode="AR" term="%22Shermain+Puah%22">Shermain Puah</searchLink><br /><searchLink fieldCode="AR" term="%22Oran+Zane+Devilly%22">Oran Zane Devilly</searchLink><br /><searchLink fieldCode="AR" term="%22Peng+Cheng+Wang%22">Peng Cheng Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Eric+Chern-Pin+Chua%22">Eric Chern-Pin Chua</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Educational+Research+Journal%22"><i>British Educational Research Journal</i></searchLink>. 2025 51(3):1313-1341.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 29
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Mediation+Theory%22">Mediation Theory</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Student+Relationship%22">Teacher Student Relationship</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Satisfaction%22">Student Satisfaction</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+Learning%22">Blended Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22College+Faculty%22">College Faculty</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Pandemics%22">Pandemics</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Professional+Autonomy%22">Professional Autonomy</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+Literacy%22">Digital Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Singapore%22">Singapore</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/berj.4123
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0141-1926<br />1469-3518
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Given the emergence of blended learning as the dominant mode of learning at university in a post-COVID-19 world, the need to examine students' perceptions of blended learning is increasingly becoming more important. This study examined the mediating role of student engagement in the relationship between the types of support (i.e., teacher, digital) and learner satisfaction in blended learning environments. A sample of 674 Year 1 and Year 2 students from a public university in Singapore participated in this study. Structural equation modelling showed that (1) teacher autonomy and digital relatedness support predicted agentic engagement, (2) digital competence and relatedness support predicted emotional engagement, (3) emotional engagement predicted all learner satisfaction facets except for learner-instructor interaction and (4) agentic engagement predicted all learner satisfaction facets except for learner-technology interaction. Of the four dimensions of student engagement, only emotional and agentic engagement mediated the relationships between various dimensions of support and learner satisfaction. Overall, these findings highlight the importance of emotionally engaging students and imbuing a sense of agency in them to enhance the relationships between the types of support and learner satisfaction.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1475462
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1475462
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/berj.4123
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 1313
    Subjects:
      – SubjectFull: Mediation Theory
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Teacher Student Relationship
        Type: general
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Student Satisfaction
        Type: general
      – SubjectFull: Blended Learning
        Type: general
      – SubjectFull: Higher Education
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: College Faculty
        Type: general
      – SubjectFull: COVID-19
        Type: general
      – SubjectFull: Pandemics
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Professional Autonomy
        Type: general
      – SubjectFull: Digital Literacy
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Singapore
        Type: general
    Titles:
      – TitleFull: The Mediating Role of Student Engagement in the Relationship between Teacher and Digital Support and Learner Satisfaction in Blended Learning Environments at Higher Education
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Xiao-Feng Kenan Kok
      – PersonEntity:
          Name:
            NameFull: Ching Yee Pua
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          Name:
            NameFull: Shermain Puah
      – PersonEntity:
          Name:
            NameFull: Oran Zane Devilly
      – PersonEntity:
          Name:
            NameFull: Peng Cheng Wang
      – PersonEntity:
          Name:
            NameFull: Eric Chern-Pin Chua
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0141-1926
            – Type: issn-electronic
              Value: 1469-3518
          Numbering:
            – Type: volume
              Value: 51
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: British Educational Research Journal
              Type: main
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