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Motivation to Learn Science, Emotions in Science Classes, and Engagement towards Science Studies in Chilean and Spanish Compulsory Secondary Education Students

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Title: Motivation to Learn Science, Emotions in Science Classes, and Engagement towards Science Studies in Chilean and Spanish Compulsory Secondary Education Students
Language: English
Authors: Membiela, Pedro (ORCID 0000-0002-8584-0417), Acosta, Katherine, Yebra, Miguel A., González, Antonio
Source: Science Education. Jul 2023 107(4):939-963.
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: 25
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Learning Motivation, Scientific Attitudes, Student Attitudes, Psychological Patterns, Science Education, Secondary School Science, Secondary School Students, Compulsory Education, Foreign Countries, Self Efficacy, Science Interests, Learner Engagement, STEM Careers
Geographic Terms: Chile, Spain
DOI: 10.1002/sce.21793
ISSN: 0036-8326
1098-237X
Abstract: Important factors in learning science include motivational variables (relevance of science learning for personal goals, self-efficacy for learning science, and interest in a scientific career), emotional variables (boredom and enjoyment in science classes), and engagement variables (vigor, dedication, and absorption towards science studies). Data from 3034 Chilean and Spanish compulsory secondary education students was used to study the relationships between these variables, by means of a self-report questionnaire analyzed using structural equation modeling (SEM). The model, tested for goodness of fit, showed that motivational variables predict emotions in science classes and explained 43% of boredom variance and 67% of enjoyment variance. Motivational and emotional variables explained the 73% variance in engagement toward science studies. Also seen is the essential role played by emotions that mediate between motivational variables in science learning and engagement towards science studies. When promoting engagement towards science studies, these results can be used to increase relevance of science learning to personal goals, self-efficacy for learning science, and interest in a scientific career, besides reducing boredom, increasing enjoyment in science classes, and enhancing engagement towards science studies.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1379876
Database: ERIC
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  Value: <anid>AN0164095124;sed01jul.23;2023Jun06.07:40;v2.2.500</anid> <title id="AN0164095124-1">Motivation to learn science, emotions in science classes, and engagement towards science studies in Chilean and Spanish compulsory secondary education students </title> <p>Important factors in learning science include motivational variables (relevance of science learning for personal goals, self‐efficacy for learning science, and interest in a scientific career), emotional variables (boredom and enjoyment in science classes), and engagement variables (vigor, dedication, and absorption towards science studies). Data from 3034 Chilean and Spanish compulsory secondary education students was used to study the relationships between these variables, by means of a self‐report questionnaire analyzed using structural equation modeling (SEM). The model, tested for goodness of fit, showed that motivational variables predict emotions in science classes and explained 43% of boredom variance and 67% of enjoyment variance. Motivational and emotional variables explained the 73% variance in engagement toward science studies. Also seen is the essential role played by emotions that mediate between motivational variables in science learning and engagement towards science studies. When promoting engagement towards science studies, these results can be used to increase relevance of science learning to personal goals, self‐efficacy for learning science, and interest in a scientific career, besides reducing boredom, increasing enjoyment in science classes, and enhancing engagement towards science studies.</p> <p>Keywords: compulsory secondary education; emotions; engagement; motivation; science learning</p> <hd id="AN0164095124-2">INTRODUCTION</hd> <p>Motivation, emotions, and engagement are linked to proper classroom functioning and to teacher and student well‐being (Pekrun et al., [<reflink idref="bib76" id="ref1">76</reflink>]). Pekrun and Linnenbrink‐García ([<reflink idref="bib74" id="ref2">74</reflink>]) observed that engagement is a relevant mediator between emotions and student achievements. Sinatra et al. ([<reflink idref="bib85" id="ref3">85</reflink>]) noted that emotions mediate in science learning by influencing cognitive, motivational, engagement processes, and achievement outcomes.</p> <p>Research into emotions in education is now gaining importance (Pekrun & Linnenbrink‐García, [<reflink idref="bib75" id="ref4">75</reflink>]), and a parallel tendency observed in science education highlights the qualitative orientation of sociology of emotions (e.g., Davis & Bellocchi, [<reflink idref="bib26" id="ref5">26</reflink>]; Kervinen et al., [<reflink idref="bib49" id="ref6">49</reflink>]; Radoff et al., [<reflink idref="bib81" id="ref7">81</reflink>]; Tomas et al., [<reflink idref="bib87" id="ref8">87</reflink>]; Volet et al., [<reflink idref="bib94" id="ref9">94</reflink>]). However, there is scarce quantitative research on the antecedent and consequent variables of academic achievement emotions and their important mediating role in science learning. Membiela et al. ([<reflink idref="bib59" id="ref10">59</reflink>]) recently published related results on preservice elementary school science teachers.</p> <p>We are unaware of any previous work in compulsory secondary education students that analyses the predictive relationships between a set of variables related to motivation, emotions, and engagement, from a correlational quantitative perspective. Consequently, the main objective is to simultaneously study the direct, indirect, and total predictive relationships of a relevant set of motivational, emotional, and engagement toward science studies variables.</p> <hd id="AN0164095124-3">THEORETICAL FOUNDATION AND HYPOTHESIS</hd> <p>The research is situated within the general framework of one of the main social‐cognitive theories (Bandura, [<reflink idref="bib8" id="ref11">8</reflink>], [<reflink idref="bib9" id="ref12">9</reflink>]), which emphasizes the perception of self‐efficacy. In this model of reciprocal causation, the interactive determinants are action, cognitive, affective, personal factors, and environmental events. Bandura's theory has achieved widespread acceptance in research as a way to explain motivation and engagement (Tytler, [<reflink idref="bib89" id="ref13">89</reflink>]). Within the social‐cognitive framework, Azevedo et al. ([<reflink idref="bib5" id="ref14">5</reflink>]) and Zimmerman and Schunk ([<reflink idref="bib98" id="ref15">98</reflink>]) considered students to self‐regulate cognitively, motivationally, emotionally, and behaviorally through active participation in their own learning process. Motivational and emotional factors participate in academic cognition (Bandura, [<reflink idref="bib8" id="ref16">8</reflink>]; Pintrich, [<reflink idref="bib79" id="ref17">79</reflink>]). Motivation itself is a primary determinant of academic emotions (Bandura, [<reflink idref="bib7" id="ref18">7</reflink>]; Pekrun & Perry, [<reflink idref="bib77" id="ref19">77</reflink>]). The social‐cognitive theory of self‐efficacy is a useful framework for guiding research on high school students in science engagement (Bae & DeBusk‐Lane, [<reflink idref="bib6" id="ref20">6</reflink>]) because self‐efficacy beliefs promote student engagement in science learning (Britner & Pajares, [<reflink idref="bib13" id="ref21">13</reflink>]; Chen & Pajares, [<reflink idref="bib19" id="ref22">19</reflink>]; Chen & Usher, [<reflink idref="bib20" id="ref23">20</reflink>]).</p> <p>The specific theoretical foundation of this study is based on the control‐value theory of academic achievement emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref24">77</reflink>]), which is one of the main theoretical references for studying academic emotions from a quantitative research orientation. The basic proposals of this theory deal with the antecedents (environment, control, and value appraisal) and consequences (achievement) of emotions, where control and value appraisal are the proximal and most important antecedents of emotions. The theory stipulates that emotions impact learning through cognition and motivation. An important mediator between emotions and academic performance is cognitive engagement (Linnenbrink & Pintrich, [<reflink idref="bib55" id="ref25">55</reflink>]; Pekrun & Linnenbrink‐García, [<reflink idref="bib74" id="ref26">74</reflink>]; Pekrun, [<reflink idref="bib69" id="ref27">69</reflink>]; Sinatra et al., [<reflink idref="bib85" id="ref28">85</reflink>]). Last but not the least; achievement emotions are relatively common across different individuals, genders, subjects, and socio‐historical contexts.</p> <p>The social‐cognitive theory of Bandura ([<reflink idref="bib8" id="ref29">8</reflink>]) coincides with the control‐value theory of academic achievement emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref30">77</reflink>]; Pekrun, [<reflink idref="bib69" id="ref31">69</reflink>]) via parallelism between the control and value appraisals that are cues as proximal antecedents of emotions and self‐efficacy expectations that are central to Bandura's socio‐cognitive theory. González and Paoloni ([<reflink idref="bib38" id="ref32">38</reflink>]) and González et al. ([<reflink idref="bib37" id="ref33">37</reflink>]) are among the few works that studied assimilable interactions based on both theories.</p> <p>We will now review previous research on motivation to learn science, emotions in science classes, and engagement toward science studies, to arrive to our main research objective.</p> <hd id="AN0164095124-4">Motivation to learn science</hd> <p>Motivation includes reasons, efforts, beliefs, and feelings, associated with learning science (Glynn et al., [<reflink idref="bib32" id="ref34">32</reflink>], [<reflink idref="bib33" id="ref35">33</reflink>]). According to the control‐value theory of emotions, motivational variables are proximal antecedents as control or value appraisals. We selected the relevance of science learning for personal goals as an intrinsic value appraisal, interest in a scientific career as an extrinsic value appraisal, and self‐efficacy for learning science as the control or expectative appraisal from among the relevant variables associated with motivation in science learning (Fraser, [<reflink idref="bib30" id="ref36">30</reflink>]; Glynn et al., [<reflink idref="bib32" id="ref37">32</reflink>], [<reflink idref="bib33" id="ref38">33</reflink>]).</p> <hd id="AN0164095124-5">Relevance of science learning for personal goals</hd> <p>This is defined in terms of student goals (Cavallo et al., [<reflink idref="bib17" id="ref39">17</reflink>]; Glynn et al., [<reflink idref="bib33" id="ref40">33</reflink>]) and is associated with the personal value of science (Glynn et al., [<reflink idref="bib33" id="ref41">33</reflink>]). The intrinsic motivation, considered akin to intrinsic valuing, is associated with the extent to which students appraise an academic subject based on the level of enjoyment and interest in that subject (Wigfield & Eccles, [<reflink idref="bib95" id="ref42">95</reflink>]). Thus, students who intrinsically value an academic subject, such as science, are more motivated to perform well in that subject (Burns et al., [<reflink idref="bib14" id="ref43">14</reflink>]; Wigfield & Eccles, [<reflink idref="bib95" id="ref44">95</reflink>]).</p> <p>Structural equation models have been used to show how personal and situational interest improved students' engagement in physics, protected them from disaffection (González & Paoloni, [<reflink idref="bib38" id="ref45">38</reflink>]), and furthermore, that personal value attributed to science plays an important predictive role in enjoyment (Ainley & Ainley, [<reflink idref="bib2" id="ref46">2</reflink>], [<reflink idref="bib3" id="ref47">3</reflink>]).</p> <hd id="AN0164095124-6">Self‐efficacy for learning science</hd> <p>This is defined as the student's confidence in obtaining good results in science learning (Glynn et al., [<reflink idref="bib33" id="ref48">33</reflink>]; Lawson et al., [<reflink idref="bib53" id="ref49">53</reflink>]). Perera ([<reflink idref="bib78" id="ref50">78</reflink>]), observed a positive relationship in 15 countries between self‐efficacy for learning science and science achievement.</p> <p>Self‐efficacy plays an important role as a predictor of emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref51">77</reflink>]), and several investigations indicate it as a strong and direct antecedent of science learning engagement (Bae & DeBusk‐Lane, [<reflink idref="bib6" id="ref52">6</reflink>]; Ben‐Eliyahu et al., [<reflink idref="bib12" id="ref53">12</reflink>]; Britner & Pajares, [<reflink idref="bib13" id="ref54">13</reflink>]; Chen & Pajares, [<reflink idref="bib19" id="ref55">19</reflink>]; Lee et al., [<reflink idref="bib54" id="ref56">54</reflink>]; Uçar & Sungur, [<reflink idref="bib91" id="ref57">91</reflink>]; Yang et al., [<reflink idref="bib96" id="ref58">96</reflink>]).</p> <hd id="AN0164095124-7">Interest in a scientific career</hd> <p>This is defined as the interest in pursuing a scientific career in the future (Fraser, [<reflink idref="bib30" id="ref59">30</reflink>]). Professional success is the main reason behind obtaining a university degree (Humphreys & Davenport, [<reflink idref="bib42" id="ref60">42</reflink>]), not only because it is a fundamental requirement in the current job market, but also due to its potential for future professional improvement.</p> <p>The importance of science in future careers influences student motivation, this being slightly higher in women than in men (Glynn et al., [<reflink idref="bib32" id="ref61">32</reflink>]). Scientific career intentions are mediated by interest in science through its influence on science learning self‐efficacy (Deemer et al., [<reflink idref="bib27" id="ref62">27</reflink>]). Personal and situational interest improve the engagement of students in Physics (González & Paoloni, [<reflink idref="bib38" id="ref63">38</reflink>]).</p> <hd id="AN0164095124-8">Emotions in science classes</hd> <p>Emotions are an affective arousal mechanism associated with tasks like studying or results such as success and failure (Pekrun & Perry, [<reflink idref="bib77" id="ref64">77</reflink>]). The influence of emotions on motivation, learning, performance, as well as, on health and well‐being of students is important (Pekrun, [<reflink idref="bib69" id="ref65">69</reflink>]). Emotions mediate in science learning through cognitive, motivational and engagement processes and through learning achievements (Sinatra et al., [<reflink idref="bib85" id="ref66">85</reflink>]). Emotions associated with learning science and those that promote long‐term interest in science and future scientific careers should be used to increase engagement with science (Sinatra et al., [<reflink idref="bib85" id="ref67">85</reflink>]). Boredom and enjoyment in classroom are two habitual and relevant emotions in students (Pekrun & Perry, [<reflink idref="bib77" id="ref68">77</reflink>]). Therefore, boredom in science classes and enjoyment in science classes have been included in the research as key emotions in science learning.</p> <hd id="AN0164095124-9">Boredom in science classes</hd> <p>Boredom is defined as an unpleasant emotional state in which students are not interested in what they are doing and cannot concentrate (Nett et al., [<reflink idref="bib61" id="ref69">61</reflink>]; Pawlak et al., [<reflink idref="bib66" id="ref70">66</reflink>]). It is understood as an achievement emotion comprised of feelings such as negative valence, disinterest, lack of stimulation, and low physiological arousal (Pekrun et al., [<reflink idref="bib71" id="ref71">71</reflink>]; Vogel‐Walcutt et al., [<reflink idref="bib93" id="ref72">93</reflink>]).</p> <p>Boredom has various negative effects because it not only undermines attention, effort, motivation, and engagement during achievement activities, but also task performance (Camacho‐Morles et al., [<reflink idref="bib16" id="ref73">16</reflink>]; Tze et al., [<reflink idref="bib90" id="ref74">90</reflink>]). Boredom appears when students perceive task demands as either too high or too low relative to their personal abilities (Acee et al., [<reflink idref="bib1" id="ref75">1</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref76">71</reflink>]). Pekrun et al. ([<reflink idref="bib76" id="ref77">76</reflink>]) point out that boredom promoted by an excessive challenge is more frequent, and that situations that are too challenging can generate anxiety, anger, and hopelessness.</p> <p>Subjective value perceptions of content, tasks, situations, and outcomes related to learning show clearly negative boredom relationships (Goetz et al., [<reflink idref="bib36" id="ref78">36</reflink>]; Pekrun et al., [<reflink idref="bib71" id="ref79">71</reflink>]). Goetz and Hall ([<reflink idref="bib35" id="ref80">35</reflink>]) observed that student boredom antecedents are class monotony, worthlessness of subjects, and the social environment (teachers, school, parents, and home). Pawlak et al. ([<reflink idref="bib67" id="ref81">67</reflink>]) add repetitiveness and disconnection, as well as lack of satisfaction and challenge to the above factors. Boredom, as a negative emotion may be beneficial for learning in certain circumstances if it promotes a deeper engagement and successful resolution of content (Loderer et al., [<reflink idref="bib56" id="ref82">56</reflink>]).</p> <hd id="AN0164095124-10">Enjoyment in science classes</hd> <p>Enjoyment is defined as an affective state of pleasure or a person's state of mind in terms of pleasantness aroused (Kuppens, [<reflink idref="bib52" id="ref83">52</reflink>]). It is a positive emotional state of personal awareness that fundamentally transforms and motivates engagement in learning (Goetz et al., [<reflink idref="bib34" id="ref84">34</reflink>]; Jack & Lin, [<reflink idref="bib44" id="ref85">44</reflink>], [<reflink idref="bib45" id="ref86">45</reflink>]). Enjoyment in studying science has been positively related to science achievements in various countries (Perera, [<reflink idref="bib78" id="ref87">78</reflink>]). Personal value of science is expected to be a powerful predictor of students' enjoyment of science (Ainley & Ainley, [<reflink idref="bib2" id="ref88">2</reflink>], [<reflink idref="bib3" id="ref89">3</reflink>]). It has also been noted that knowledge and personal value combine to determine student enjoyment (Ainley & Ainley, [<reflink idref="bib2" id="ref90">2</reflink>]).</p> <hd id="AN0164095124-11">Engagement toward science studies</hd> <p>Engagement is defined as a positive and satisfactory cognitive‐affective situation associated with the dimensions of vigor, dedication, and absorption (Schaufeli, Martínez, et al., [<reflink idref="bib83" id="ref91">83</reflink>]; Schaufeli, Salanova, et al., [<reflink idref="bib84" id="ref92">84</reflink>]). It is persistent and pervasive because it is not limited to any specific object, event, individual, or behavior. Vigor means using a lot of energy and resilience in carrying out a task, as well as the will and ability to exert the necessary performance effort. Dedication means giving importance to the task, and being enthusiastic, inspired, proud and challenging. Absorption implies total concentration, being happy to accomplish the task; attracted by the task, in such a way that time passes quickly.</p> <p>Engagement is at the educational forefront in the affective domain (Grabau & Ma, [<reflink idref="bib39" id="ref93">39</reflink>]), and science engagement has been defined from a similar position (OECD, [<reflink idref="bib64" id="ref94">64</reflink>]). In this sense, given that student numbers are decreasing in science, technology, engineering, and mathematics (STEM), the OECD ([<reflink idref="bib63" id="ref95">63</reflink>]) has recommended that governments promote science and technology by making it more attractive. However, previous research on student engagement in science using PISA 2006 data are not assimilable or comparable with research in which engagement is just another variable for all purposes in the set of variables studied.</p> <p>The science learning relationships between engagement and motivational or emotional variables in previous research are as follows:</p> <p>Relevance for personal goals is related to the intrinsic value students give to an academic subject (Wigfield & Eccles, [<reflink idref="bib95" id="ref96">95</reflink>]). Students who intrinsically value a subject, such as science, are more likely to engage with that subject, through deeper learning and better results (Burns et al., [<reflink idref="bib14" id="ref97">14</reflink>]; Wigfield & Eccles, [<reflink idref="bib95" id="ref98">95</reflink>]).</p> <p>Self‐efficacy is a strong and direct determinant of science learning engagement (Bae & DeBusk‐Lane, [<reflink idref="bib6" id="ref99">6</reflink>]; Ben‐Eliyahu et al., [<reflink idref="bib12" id="ref100">12</reflink>]; Britner & Pajares, [<reflink idref="bib13" id="ref101">13</reflink>]; Chen & Pajares, [<reflink idref="bib19" id="ref102">19</reflink>]; Kıran et al., [<reflink idref="bib51" id="ref103">51</reflink>]; Lee et al., [<reflink idref="bib54" id="ref104">54</reflink>]; Uçar & Sungur, [<reflink idref="bib91" id="ref105">91</reflink>]; Yang et al., [<reflink idref="bib96" id="ref106">96</reflink>]). Moreover, self‐efficacy in science is associated with engagement in science‐related activities (Britner & Pajares, [<reflink idref="bib13" id="ref107">13</reflink>]; Velayutham & Aldridge, [<reflink idref="bib92" id="ref108">92</reflink>]).</p> <p>On the subject of interest in a scientific career, González and Paoloni ([<reflink idref="bib38" id="ref109">38</reflink>]) point out that personal and situational interest improve students' engagement in Physics.</p> <p>Insofar as boredom is concerned, Camacho‐Morles et al. ([<reflink idref="bib16" id="ref110">16</reflink>]) and Tze et al. ([<reflink idref="bib90" id="ref111">90</reflink>]) reported negative effects on engagement, while Taasoobshirazi et al. ([<reflink idref="bib86" id="ref112">86</reflink>]) reported no effects, but Loderer et al. ([<reflink idref="bib56" id="ref113">56</reflink>]) reported possible beneficial ones in certain circumstances.</p> <p>The observation in regard to enjoyment is that it seems to lead to higher levels of engagement (Ben‐Eliyahu et al., [<reflink idref="bib12" id="ref114">12</reflink>]; Pekrun et al., [<reflink idref="bib73" id="ref115">73</reflink>]) and is directly and indirectly associated with deep cognitive engagement and conceptual change (Taasoobshirazi et al., [<reflink idref="bib86" id="ref116">86</reflink>]).</p> <p>An important mediator between emotions and academic performance is cognitive engagement (Linnenbrink & Pintrich, [<reflink idref="bib55" id="ref117">55</reflink>]; Pekrun, [<reflink idref="bib69" id="ref118">69</reflink>]; Pekrun & Linnenbrink‐Garcia, [<reflink idref="bib74" id="ref119">74</reflink>]). In this line, Sinatra et al. ([<reflink idref="bib85" id="ref120">85</reflink>]) point out that emotions mediate science learning by influencing cognitive, motivational, engagement processes, and achievement results.</p> <p>There is scant previous research on the simultaneous relationships of engagement, with motivational and emotional variables in science learning. Membiela et al. ([<reflink idref="bib59" id="ref121">59</reflink>]) using SEM analysis, related relevance of science learning for personal goals and self‐efficacy with boredom and enjoyment, and all of them with engagement in preservice elementary science teachers.</p> <p>Engagement toward science studies has been included in the study because it is an important mediator between emotions and student performance (Pekrun & Linnenbrink‐Garcia, [<reflink idref="bib74" id="ref122">74</reflink>]), more so because of its important relationships with motivational and emotional variables.</p> <hd id="AN0164095124-12">Relationship between research variables and hypotheses</hd> <p>The importance of the interactions between motivational, emotional, and engagement variables is generally based on the theoretical framework of the social cognitive theory (Bandura, [<reflink idref="bib9" id="ref123">9</reflink>]) and more specifically on the control‐value theory of achievement academic emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref124">77</reflink>]), wherein control and value appraisals play a key role in emotions and engagement.</p> <p>Therefore, the objective was to analyze the relationships between three variable domains (Figure 1): a) motivation to learn science (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career), b) emotions (boredom in science classes, enjoyment in science classes) and, c) engagement as vigor, dedication and absorption towards science studies.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/SED/01jul23/sce21793-fig-0001.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="sce21793-fig-0001.jpg" title="1 Hypothesis model." /> </p> <p></p> <p>In this sense, it is expected that students assigning greater personal value to science have more self‐efficacy in learning science, are more interested in scientific careers, experience more enjoyment and less boredom in science classes, and show higher levels of engagement towards science studies.</p> <hd id="AN0164095124-14">Hypothesis on motivations as predictors of emotions and engagement</hd> <p>H1: <emph>Relevance of science learning for personal goals as negative predictor of Boredom in science classes (H1a), but positive predictor of Engagement towards science studies (H1b) and Enjoyment in science classes (H1c)</emph>.</p> <p>H2: <emph>Self‐efficacy for learning science as negative predictor of Boredom in science classes (H2a) and positive predictor of Engagement towards science studies (H2b) and Enjoyment in science classes (H2c)</emph>.</p> <p>H3: <emph>Interest in a scientific career as negative predictor of Boredom in science classes (H3a) and positive predictor of Engagement towards science studies (H3b) and Enjoyment in science classes (H3c)</emph>.</p> <hd id="AN0164095124-15">Hypothesis on emotions as predictors of engagement</hd> <p>H4: <emph>Boredom in science classes as negative predictor of Engagement towards science studies (H4a)</emph>.</p> <p>H5: <emph>Enjoyment in science classes as positive predictor of Engagement towards science studies (H5a)</emph>.</p> <hd id="AN0164095124-16">Hypothesis on emotions as mediators between motivation and engagement</hd> <p>H6: <emph>Boredom in science classes as mediator between Relevance of science learning for personal goals and Engagement towards science studies (H6a), Boredom in science classes as mediator between Self‐efficacy for learning science and Engagement towards science studies (H6b), and Boredom in science classes as mediator between Interest in a scientific career and Engagement towards science studies (H6c)</emph>.</p> <p>H7: <emph>Enjoyment in science classes as mediator between Relevance of science learning for personal goals and Engagement towards science studies (H7a), Enjoyment in science classes as mediator between Self‐efficacy for science learning and Engagement towards science studies (H7b), and Enjoyment in science classes as mediator between Interest in a scientific career and Engagement towards science studies (H7c)</emph>.</p> <hd id="AN0164095124-17">Research needs</hd> <p>This study contributes to understanding the influence of motivational variables on learning science, emotions in science classes, and engagement towards science studies in compulsory secondary school students:</p> <p>First, by studying the binary relationships: (<reflink idref="bib1" id="ref125">1</reflink>) between the relevance of science learning to personal goals as a predictor of boredom and enjoyment in science classes, and of engagement toward science studies in compulsory secondary education; (<reflink idref="bib2" id="ref126">2</reflink>) between self‐efficacy as a predictor of boredom and enjoyment in science classes, and engagement toward science studies in compulsory secondary education; (<reflink idref="bib3" id="ref127">3</reflink>) between interest in a scientific career as a predictor of boredom and enjoyment in science classes, and engagement toward science studies in compulsory secondary education. And additionally studying (<reflink idref="bib4" id="ref128">4</reflink>) boredom as a predictor of engagement toward science studies in compulsory secondary education, and (<reflink idref="bib5" id="ref129">5</reflink>) enjoyment as a predictor of engagement toward science studies in compulsory secondary education.</p> <p>Second, by evaluating the set of simultaneous relationships between a relevant group of motivational, emotional and engagement variables and, in particular, the mediating role of emotions between motivational variables and engagement. We are unaware of any previous research on secondary education students that studies the relationships between this set of variables.</p> <p>Third, according to Pekrun and Linnenbrink‐García ([<reflink idref="bib75" id="ref130">75</reflink>]), intercultural studies are needed to analyze the variation of emotions between cultures with different educational values and practices. Also needed are studies that include students from a variety of cultural backgrounds where emotional expression rules and emotion labels are different from those relevant to English‐speaking countries (Bellocchi & Ritchie, [<reflink idref="bib11" id="ref131">11</reflink>]). Comparisons between studies conducted in different countries could help us to understand whether macro‐social phenomena, such as cultural norms and policies, affect the emotions experienced and displayed by students and how this relates to science learning outcomes (Bellocchi & Ritchie, [<reflink idref="bib11" id="ref132">11</reflink>]). Therefore, it would seem interesting to study and see if the relationships between science learning variables are similar in countries with educational similarities such as in the case of Chile and Spain, and furthermore check if one of the statements of the control‐value theory of emotions: the relative universality of academic achievement emotions, is fulfilled.</p> <p>Another reason to analyze this group of variables is that science teachers can improve them through teaching, since they influence science learning.</p> <hd id="AN0164095124-18">METHOD</hd> <p></p> <hd id="AN0164095124-19">The Chilean and Spanish educational context</hd> <p>Chile and Spain are two good examples of Spanish‐speaking countries that have similarities associated with the common language. These similarities have been established over time thanks to extensive and diverse relationships, particularly in the educational field in general and in research and innovation in science education in particular. The similarities in the educational systems and their evolution in terms of educative organization and official curricula are evident through the automatic validation of compulsory studies between both countries thanks to the Andrés Bello Agreement on Educational, Scientific, Technological, and Cultural Integration. The relationships and similarities generated have also been important at the university level, in undergraduate, graduate, and doctoral studies.</p> <p>There are three levels of preuniversity studies in the Chilean educational system: early childhood (0–6), primary (6–13), and middle education (14–17), where primary and middle education are compulsory. In the first year of secondary school, students receive 4 h/week of Biology and Geology, in the second year 3 h/week of Physics and Chemistry, and in the third year 2 h/week of Biology and Geology and 2 h of Physics and Chemistry. In the fourth‐year of academic science specialization, they can choose Biology and Geology and/or Physics and Chemistry as electives.</p> <p>The Spanish educational system has three levels of preuniversity education: early childhood (0–6), primary (6–12), and secondary (12–18). Primary and part of secondary education (12–16) are compulsory. In the first year of secondary school, students learn 4 h/week of Biology and Geology; in the second year, 3 h/week of Physics and Chemistry; and in the third year, 2 h/week of Biology and Geology and 2 h/week of Physics and Chemistry. In the fourth‐year academic science specialization, they may have Biology and Geology and/or Physics and Chemistry as electives.</p> <hd id="AN0164095124-20">Sampling procedures</hd> <p>A convenience subsample was selected from five of the six existing public schools in a city in northern Chile. In like manner, another convenience subsample was selected from five secondary schools in northwestern Spain. All were public schools, two of them were urban, and three were in small villages that also enrolled rural students. Permission from the school management and informed consent was obtained from students, who participated voluntarily and anonymously.</p> <hd id="AN0164095124-21">Participants</hd> <p>The sample of 3034 (out of 3887) high school science students was divided into two subsamples. The first subsample contained 1648 Scientific‐Humanist Middle Education students (first to fourth grade) from five public urban schools in northern Chile that also catered to rural students. They were 14–18 year‐olds and predominantly males (859 boys vs. 773 girls). The other subsample had 1368 Compulsory Secondary Education (first to fourth grade) students from five public schools in northwestern Spain. They were 12–18 year‐olds and predominantly female (522 girls vs. 488 boys) that answered the question of genre.</p> <hd id="AN0164095124-22">Instruments</hd> <p>Sociodemographic variables were included in the questionnaire: School (<reflink idref="bib1" id="ref133">1</reflink>, 2, 3, 4, 5, 6, 7, 8, 9, 10), Academic years (first, second, third, and fourth), Gender (Female = 1/Male = 2), Age (<reflink idref="bib1" id="ref134">1</reflink>, 2, 3, 4, 5 on a scale of 12–18 years in Spain and 14–18 years in Chile), Average mark in the last two science assessments (the data has been normalized. Chile: 1 = 1.00–3.99; 2 = 4.00–4.99; 3 = 5.00–5.99; 4 = 6.00–7.00. Spain: 0 = 0.00–4.99, 1 = 5.00–5.99, 2 = 6.00–6.99, 3 = 7.00–8.99, 4 = 9.00–10.00).</p> <p>The study variables and items are presented below:</p> <hd id="AN0164095124-23">Relevance of science learning for personal goals and Self‐efficacy for learning science</hd> <p>The measurements were made using the Science Motivation Questionnaire (SMQ) (Glynn et al., [<reflink idref="bib33" id="ref135">33</reflink>]). In each dimension, the 3 Likert‐type items (1. never to 5. always) with the greatest weight were selected (Relevance of science learning for personal goals: "The science I learn relates to my personal goals," "The science I learn is relevant to my life," and "The science I learn has practical value for me." Self‐efficacy for learning science: "I expect to do as well as or better than other students in the science course," "I am confident I will do well on in the science tests," and "I believe I can earn get a good score in the science course").</p> <hd id="AN0164095124-24">Interest in a scientific career</hd> <p>The measurements were made with the Test of Science‐Related Attitudes (TOSRA) (Navarro et al., [<reflink idref="bib60" id="ref136">60</reflink>]). In each dimension, the 3 Likert‐type items (1. strongly disagree to 5. strongly agree) with the greatest weight were selected (Interest in a scientific career: "A career in science would be dull and boring," "A job as a scientist would be boring," and "A job as a scientist would be interesting").</p> <hd id="AN0164095124-25">Boredom and Enjoyment in science classes</hd> <p>The measurements were made with the Achievement Emotions Questionnaire for Pre‐Adolescents (AEQ‐PA) (Peixoto et al., [<reflink idref="bib68" id="ref137">68</reflink>]), which is an adaptation of AEQ (Pekrun et al., [<reflink idref="bib72" id="ref138">72</reflink>]). In each dimension, the 3 Likert‐type items (1. strongly disagree to 5. strongly agree) with the greatest weight were selected (Boredom in science classes: "I get bored during science classes," "Science classes bore me," and "I find my science classes fairly dull." Enjoyment in science classes: "I enjoy being in my science classes," "I feel excited about being in my science classes listening to the teacher," and "I'm glad that it paid off to go to my science classes").</p> <hd id="AN0164095124-26">Engagement towards science studies</hd> <p>The measurements were made with the Utrech Work Engagement Student Scale (UWES‐9) (Schaufeli et al., [<reflink idref="bib82" id="ref139">82</reflink>]) and includes three subscales: Vigor, Dedication and Absorption towards science studies. Each dimension had 3 Likert‐type items (0. never to 6. every day). (Vigor: "In my science studies, I feel bursting with energy," "At my science studies, I feel strong and vigorous," and "When I get up in the morning, I feel like going to my science studies." Dedication: "I am enthusiastic about my science studies," "My science studies inspires me," and "I am proud on my science studies." Absorption: "I feel happy when I am studying science intensely," "I am immersed in my science studies," and "I get carried away when I'm studying science."</p> <hd id="AN0164095124-27">Data analysis</hd> <p>Pearson's correlations were calculated between the different variables studied in a preliminary analysis. Other descriptive statistics studied were means and standard deviations. As reliability indices, Cronbach's alpha, composite reliability (CR) and average variance extracted coefficient (AVE) were obtained (Hair et al., [<reflink idref="bib41" id="ref140">41</reflink>]).</p> <p>The structural equation modeling (SEM) methodology was used to study the simultaneous relationship between a relevant group of motivational, emotional and engagement variables, and especially, the mediator role of emotions. This group of variables will be analyzed simultaneously using SEM, due to its advantages in relation to correlation and regression analysis. SEM is a powerful approach to analyze complex relationships between observed and latent variables. In SEM, the research questions are about how multiple variables interact with each other.</p> <p>Several confirmatory factor analyses (CFA) were first performed to check the suitability of different scales. We then used a two‐step structural equation model (SEM) to estimate the measurement model fit in the first step and the structural model in the second step (Byrne, [<reflink idref="bib15" id="ref141">15</reflink>]). Finally, the SEM bootstrap method (Preacher & Hayes, [<reflink idref="bib80" id="ref142">80</reflink>]) was used to study the mediating relationship of boredom and enjoyment in science classes between motivational and engagement variables. For goodness of fit, the SEM indices (Byrne, [<reflink idref="bib15" id="ref143">15</reflink>]) used were <emph>χ</emph><sups>2</sups>, <emph>χ</emph><sups>2</sups>/<emph>df</emph>, comparative fit index (CFI), goodness‐of‐fit index (GFI), and root mean square error of approximation (RMSEA). Values above 0.90 indicate a good fit for CFI and GFI.</p> <p>Moreover, multiple group analysis was used to examine whether the hypothetical model was equivalent or invariant between the subsamples from Chile and Spain. The model invariance tests focused on the equivalence of structural path parameters between the two subsamples. A multigroup model is considered to have a good fit if the CFI is greater than 0.95 and there is a small difference in CFI (∆CFI) between the different models (Byrne, [<reflink idref="bib15" id="ref144">15</reflink>]), no greater than 0.01 (Cheung & Rensvold, [<reflink idref="bib21" id="ref145">21</reflink>]).</p> <hd id="AN0164095124-28">RESULTS</hd> <p></p> <hd id="AN0164095124-29">Preliminary analyses</hd> <p>The Pearson correlations between the different variables are all significant (Table 1). The values ranged from <emph>r</emph> = −0.613 to <emph>r</emph> = 0.704. Some correlations, such as between Engagement towards science studies and Enjoyment in science classes (<emph>r</emph> = 0.704) or between Engagement towards science studies and Relevance of science learning for personal goals (<emph>r</emph> = 0.684), were stronger. They were followed by the correlations between Engagement towards science studies and Self‐efficacy for learning science (<emph>r</emph> = 0.614), Enjoyment in science classes and Boredom in science classes (<emph>r</emph> = −0.613), and the correlations between Relevance of science learning for personal goals with Self‐efficacy for learning science (<emph>r</emph> = 0.608) and Enjoyment in science classes (<emph>r</emph> = 0.606).</p> <p>1 Table Pearson correlations, other descriptive statistics, and reliability indices.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><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></tr></thead><tbody valign="top"><tr><td>1. Relevance of science learning for personal goals</td><td /><td /><td /><td /><td /><td /></tr><tr><td>2. Self‐efficacy for learning science</td><td align="char" char=".">0.608</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td>3. Interest in a scientific career</td><td align="char" char=".">0.530</td><td align="char" char=".">0.395</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td>4. Boredom in science classes</td><td align="char" char=".">−0.477</td><td align="char" char=".">−0.415</td><td align="char" char=".">−0.506</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td>5. Enjoyment in science classes</td><td align="char" char=".">0.606</td><td align="char" char=".">0.534</td><td align="char" char=".">0.557</td><td align="char" char=".">−0.613</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td>6. Engagement towards science studies</td><td align="char" char=".">0.684</td><td align="char" char=".">0.614</td><td align="char" char=".">0.539</td><td align="char" char=".">−0.573</td><td align="char" char=".">0.704</td><td align="char" char="." /></tr><tr><td>Mean</td><td align="char" char=".">2.93</td><td align="char" char=".">3.41</td><td align="char" char=".">3.50</td><td align="char" char=".">2.52</td><td align="char" char=".">3.32</td><td align="char" char=".">3.04</td></tr><tr><td>SD</td><td align="char" char=".">1.10</td><td align="char" char=".">0.99</td><td align="char" char=".">0.99</td><td align="char" char=".">1.12</td><td align="char" char=".">1.02</td><td align="char" char=".">1.70</td></tr><tr><td>Skewness</td><td align="char" char=".">−0.023</td><td align="char" char=".">−0.326</td><td align="char" char=".">0.095</td><td align="char" char=".">0.406</td><td align="char" char=".">−0.391</td><td align="char" char=".">−0.115</td></tr><tr><td>Kurtosis</td><td align="char" char=".">−0.844</td><td align="char" char=".">−0.469</td><td align="char" char=".">5.749</td><td align="char" char=".">−0.629</td><td align="char" char=".">−0.417</td><td align="char" char=".">−1.109</td></tr><tr><td>Cronbach's α</td><td align="char" char=".">0.848</td><td align="char" char=".">0.797</td><td align="char" char=".">0.741</td><td align="char" char=".">0.884</td><td align="char" char=".">0.811</td><td align="char" char=".">0.960</td></tr><tr><td>CR</td><td align="char" char=".">0.85</td><td align="char" char=".">0.81</td><td align="char" char=".">0.74</td><td align="char" char=".">0.88</td><td align="char" char=".">0.81</td><td align="char" char=".">0.87</td></tr><tr><td align="char" char=".">AVE</td><td align="char" char=".">0.65</td><td align="char" char=".">0.59</td><td align="char" char=".">0.49</td><td align="char" char=".">0.71</td><td align="char" char=".">0.59</td><td align="char" char=".">0.95</td></tr></tbody></table> </ephtml> </p> <p>1 Abbreviations: AVE, average variance extracted; CR, composite reliability; SD, standard deviation.</p> <p>2 <emph>r</emph> ≥ 0.063, *<emph>p</emph> < 0.05, <emph>r</emph> > 0.094, **<emph>p</emph> < 0.01.</p> <p>The various reliability indices, alpha, CR, and AVE, were suitable for all variables. The assumption of univariate normality was confirmed in the measured variables, which presented adequate skewness and kurtosis indices.</p> <p>The CFA shows that the model fits well to the data for motivational determinants (Relevance of science learning for personal goals, Self‐efficacy for learning science, and Interest in a scientific career: <emph>χ</emph><sups>2</sups>/<emph>df</emph> = 12.613, GFI = 0.977, CFI = 0.976, RMSEA = 0.062), emotions (boredom in science classes, enjoyment in science classes: <emph>χ</emph><sups>2</sups>/<emph>df</emph> = 3.789, GFI = 0.997, CFI = 0.998, RMSEA = 0.054), and engagement towards science studies (vigor, dedication, absorption: <emph>χ</emph><sups>2</sups>/<emph>df</emph> = 14.420, GFI = 0.978, CFI = 0.990, RMSEA = 0.067).</p> <hd id="AN0164095124-30">The measurement model</hd> <p>Three scores of vigor, dedication, and absorption were used as indicators of engagement towards science studies. Six latent variables and 18 indicators were included in the CFA (see Figure 2).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/SED/01jul23/sce21793-fig-0002.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="sce21793-fig-0002.jpg" title="2 The measurement model. AEQ_BO, boredom in science classes items; AEQ_JO, Enjoyment in science classes items; SMQ_RP, relevance of science learning for personal goals items; SMQ_SE, self‐efficacy for learning science items; TOSRA_CI, interest in a scientific career items." /> </p> <p></p> <p>There is a good fit of the measurement model to the data, as shown by the values of the different calculated indices: <emph>χ</emph><sups>2</sups>/<emph>df</emph> = 8.145, GFI = 0.964, CFI = 0.977, RMSEA = 0.049. The standardized factor loadings that show the relationships of the indicators with the latent variables, range from 0.624 (TOSRA_CI35) to 0.963 (DE_DEDICATION), all of which are significant (<emph>p</emph> < 0.001). Also significant were the correlations between the latent variables (<emph>p</emph> ≤ 0.025).</p> <hd id="AN0164095124-32">The structural model</hd> <p>A SEM was calculated to test the initial hypotheses on how the variables are related to each other (Figure 3). In the analysis, the three motivational variables and the two emotions were related to each other. The model fits the data well, as indicated by the indices: <emph>χ</emph><sups>2</sups>/<emph>df</emph> (<emph>χ</emph><sups>2</sups> = 633.629, <emph>df</emph> = 80) = 7.920, GFI = 0.972, CFI = 0.982, RMSEA = 0.048. Relevance for personal goals is a negative predictor of boredom (<emph>β</emph> = −0.15, <emph>p</emph> < 0.001) and a positive predictor of enjoyment (<emph>β</emph> = 0.28, <emph>p</emph> = 0.001) and engagement (<emph>β</emph> = 0.27, <emph>p</emph> < 0.001). Self‐efficacy is a negative predictor of boredom (<emph>β</emph> = −0.15, <emph>p</emph> < 0.001) and a positive predictor of enjoyment (<emph>β</emph> = 0.24, <emph>p</emph> < 0.001) and engagement (<emph>β</emph> = 0.16, <emph>p</emph> < 0.001). Interest in a scientific career is a negative predictor of boredom (<emph>β</emph> = −0.44, <emph>p</emph> < 0.001), and a positive predictor of enjoyment (<emph>β</emph> = 0.43, <emph>p</emph> < 0.001) and engagement (<emph>β</emph> = 0.05, <emph>p</emph> = 0.046). Finally, boredom is a negative predictor of engagement (<emph>β</emph> = −0.06, <emph>p</emph> = 0.016) and enjoyment is a positive predictor of engagement (<emph>β</emph> = 0.43, <emph>p</emph> = 0.001). The model explained the variance of 43% for boredom in science classes, 67% for enjoyment in science classes, and 73% for engagement towards science studies.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/SED/01jul23/sce21793-fig-0003.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="sce21793-fig-0003.jpg" title="3 The structural model of relations." /> </p> <p></p> <hd id="AN0164095124-34">Mediated relationships</hd> <p>Finally, the AMOS 24 software was used to study the partial or total mediations of boredom in science classes and enjoyment in science classes between the motivational and engagement variables. The estimation of the indirect effects and the confidence intervals confirms the significance and importance of the mediations (Preacher & Hayes, [<reflink idref="bib80" id="ref146">80</reflink>]) (see Table 2).</p> <p>2 Table Direct, indirect, and total effects of relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career in engagement towards science studies.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Predictor → Criterion</th><th align="left" /><th align="left" /><th align="left">Indirect effect (<italic>p</italic>)</th><th align="left" /></tr><tr valign="bottom"><th align="left">Partial mediation</th><th align="left">Mediator</th><th align="left">Direct effect (<italic>p</italic>)</th><th align="left">Sum (<italic>p</italic>) <sup>a</sup></th><th>CI</th><th>Total effect (<italic>p</italic>)</th></tr></thead><tbody valign="top"><tr><td>Relevance for personal goals →Engagement</td><td>Boredom + Enjoyment</td><td>0.27 (=0.001)</td><td>0.13 (=0.001)</td><td>0.09, 0.16</td><td>0.40 (=0.001)</td></tr><tr><td>Self‐efficacy →Engagement</td><td>Boredom + Enjoyment</td><td>0.16 (<0.001)</td><td>0.11 (<0.001)</td><td>0.08, 0.14</td><td>0.27 (<0.001)</td></tr><tr><td>Interest in a scientific career →Engagement</td><td>Boredom + Enjoyment</td><td>0.05 (<0.001)</td><td>0.21 (<0.001)</td><td>0.17, 0.25</td><td>0.26 (<0.001)</td></tr><tr><td>Relevance for personal goals →Engagement</td><td>Boredom</td><td>0.37 (<0.001)</td><td>0.03 (<0.001)</td><td>0.02, 0.05</td><td>0.41 (<0.001)</td></tr><tr><td>Self‐efficacy →Engagement</td><td>Boredom</td><td>0.24 (<0.001)</td><td>0.03 (<0.001)</td><td>0.02, 0.04</td><td>0.27 (<0.001)</td></tr><tr><td>Interest in a scientific career →Engagement</td><td>Boredom</td><td>0.16 (<0.001)</td><td align="left">0.09 (<0.001)</td><td>0.07, 0.11</td><td>0.25 (<0.001)</td></tr><tr><td>Relevance for personal goals →Engagement</td><td>Enjoyment</td><td>0.27 (<0.001)</td><td>0.12 (=0.001)</td><td>0.09, 0.16</td><td>0.39 (=0.001)</td></tr><tr><td>Self‐efficacy →Engagement</td><td>Enjoyment</td><td>0.16 (=0.010)</td><td>0.11 (<0.001)</td><td>0.08, 0.14</td><td>0.25 (<0.001)</td></tr><tr><td>Interest in a scientific career →Engagement</td><td>Enjoyment</td><td>0.06 (=0.028)</td><td>0.20 (<0.001)</td><td>0.16, 0.25</td><td>0.27 (<0.001)</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note</emph>: (a) Standardized indirect effects were calculated through AMOS 24 bootstrap method (confidence level [CI] = 95%; samples = 5000).</p> <p>Relevance for personal goals is a positive direct (<emph>β</emph> = 0.27, <emph>p</emph> = 0.001) and indirect predictor of engagement thanks to the mediation of boredom and enjoyment (<emph>β</emph> = 0.13, <emph>p</emph> = 0.001) in improving engagement.</p> <p>Self‐efficacy is a positive direct (<emph>β</emph> = 0.16, <emph>p</emph> < 0.001) and indirect predictor of engagement thanks to the mediation of boredom and enjoyment (<emph>β</emph> = 0.11, <emph>p</emph> < 0.001) in improving engagement.</p> <p>Interest in a scientific career is a positive direct (<emph>β</emph> = 0.05, <emph>p</emph> < 0.001) and indirect predictor of engagement thanks to the mediation of boredom and enjoyment (<emph>β</emph> = 0.21, <emph>p</emph> < 0.001) in improving engagement.</p> <p>A partial mediation of boredom and enjoyment is observed upon taking into account the direct and indirect effects between the three motivational variables (relevance for personal goals and self‐efficacy for learning science, interest in a scientific career) and engagement.</p> <p>Upon separately studying the mediation of boredom and enjoyment between the different motivational variables and engagement (see Table 2), we observe that the indirect effects are clearly greater for enjoyment than for boredom.</p> <hd id="AN0164095124-35">Multigroup analysis</hd> <p>We performed analyses to confirm the equivalence of the model for the subsamples of Chile and Spain and the result indicates invariance or equivalence between the two subsamples: SEM Model unconstrained <emph>χ</emph><sups>2</sups>/<emph>df</emph> = 5.675, GFI = 0.952, CFI = 0.970, RMSEA = 0.039. Measurement weights CFI = 0.968, Structural weights CFI = 0.966, Structural covariances CFI = 0.963, Structural residuals CFI = 0.960, Measurement residuals CFI = 0.953.</p> <p>Separate analysis of the structural model has also been carried out in the subsamples of Spain and Chile. Relevance for personal goals is a negative predictor of boredom (<emph>β</emph> = −0.15 (Chile) <emph>p</emph> = 0.009, −0.16 (Spain) <emph>p</emph> = 0.004) and a positive predictor of enjoyment (<emph>β</emph> = 0.28 (Chile) <emph>p</emph> < 0.001, 0.26 (Spain) <emph>p</emph> = 0.001) and engagement (<emph>β</emph> = 0.25 (Chile) <emph>p</emph> = 0.001, 0.28 (Spain), <emph>p</emph> < 0.001). Self‐efficacy is a negative predictor of boredom (<emph>β</emph> = −0.12 (Chile) <emph>p</emph> = 0.010, −0.17 (Spain), <emph>p</emph> < 0.001) and a positive predictor of enjoyment (<emph>β</emph> = 0.16 (Chile) <emph>p</emph> < 0.001, 0.27 (Spain), <emph>p</emph> < 0.001) and engagement (<emph>β</emph> = 0.28 (Chile) <emph>p</emph> < 0.001, 0.26 (Spain), <emph>p</emph> = 0.001). Interest in a scientific career is a negative predictor of boredom (<emph>β</emph> = −0.46 (Chile) <emph>p</emph> < 0.001, −0.41 (Spain), <emph>p</emph> < 0.001), and a positive predictor of enjoyment (<emph>β</emph> = 0.51 (Chile) <emph>p</emph> < 0.001, 0.38 (Spain), <emph>p</emph> < 0.001) and engagement (<emph>β</emph> = 0.01 (Chile) <emph>p</emph> = 0.905, 0.11 (Spain), <emph>p</emph> = 0.006). Finally, boredom is a negative predictor of engagement (<emph>β</emph> = −0.12 (Chile) <emph>p</emph> < 0.001, −0.05 (Spain), <emph>p</emph> = 0.209) and enjoyment is a positive predictor of engagement (<emph>β</emph> = 0.36 (Chile) <emph>p</emph> < 0.001, 0.54 (Spain), <emph>p</emph> = 0.001). The model explained the variance of 42% (Chile) and 42% (Spain) for boredom in science classes, 72% (Chile) and 61% (Spain) for enjoyment in science classes, and 68% (Chile) and 81% (Spain) for engagement towards science studies. The results are similar in both countries, where the greatest difference is between career interest in science and enjoyment in science classes, and between enjoyment in science classes and engagement towards science studies.</p> <hd id="AN0164095124-36">DISCUSSION</hd> <p>The relationships between variables of three science‐learning‐related domains have been studied: motivations (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career), emotions (boredom in science classes and enjoyment in science classes) and engagement towards science studies (vigor, dedication, absorption). Preliminary analyses have verified the adequacy of the instruments used. CFA analyses have shown that the measurement model fits the data well, with suitable values for the goodness of the indices.</p> <p>The SEM analysis of the structural model has made relevant theoretical, empirical and practical contributions:</p> <hd id="AN0164095124-37">Theoretical contributions</hd> <p>According to Bandura's social‐cognitive theory (1997, 2001) and the control‐value theory of achievement emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref147">77</reflink>]; Pekrun, [<reflink idref="bib69" id="ref148">69</reflink>]) motivational variables not only influence academic emotions but also in turn determine engagement. We therefore need to interpret the relationship between the three science‐learning‐related domains: motivations (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career), emotions in science classes (boredom and enjoyment) and engagement towards science studies.</p> <p>Our results are also in line with the control‐value theory of emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref149">77</reflink>]), which proposes personal appraisals of control and value as proximal determinants, and stipulates that emotions influence learning through cognition and motivation. In this sense, we interpret the results that show the relevant mediator role played by emotions (boredom and enjoyment in science classes) between the motivational variables (relevance of science learning for personal goals, self‐efficacy for learning science and interest in a scientific career) and engagement towards science studies. Emotions have a mediating role in science learning, due to their influence on cognition, motivation and engagement (Sinatra et al., [<reflink idref="bib85" id="ref150">85</reflink>]).</p> <p>The findings also confirm the model (Elliot & Pekrun, [<reflink idref="bib29" id="ref151">29</reflink>]) associated with the control‐value theory of emotions, which maintains that mastery goals fix students' attention on the positive value associated with the activity. In our case, a construct close to the mastery goals, such as the relevance of science learning for personal goals, should promote a positive emotion like enjoyment in science classes and reduce a negative emotion like boredom in science classes.</p> <p>Different approaches of emotion studies indicate that different theoretical lenses could provide new perspectives (Davis & Bellocchi, [<reflink idref="bib26" id="ref152">26</reflink>]), or that only the combination of research carried out from different perspectives can bring advantages (Pekrun & Linnenbrink‐García, [<reflink idref="bib75" id="ref153">75</reflink>]). In this sense, our research represents an important novel contribution in a little studied field, such as the simultaneous relationships of relevant emotions in science classes (boredom and enjoyment) with motivational antecedents and consequent engagement towards science studies.</p> <hd id="AN0164095124-38">Empirical contributions</hd> <p>We believe this is the first study that uses SEM methodology to analyze all relationships between groups of relevant variables associated with secondary students' learning of science. It deals with the relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career, boredom in science classes, enjoyment in science classes, and student engagement towards science studies.</p> <p>The main contribution of the study is the confirmation of the hypotheses on emotions (boredom H6, enjoyment H7) as mediators between motivational variables (relevance of science learning for personal goals and self‐efficacy in science learning, and interest in a scientific career) and engagement toward science studies. Sinatra et al. ([<reflink idref="bib85" id="ref154">85</reflink>]) had earlier pointed out a mediating role of emotions in influencing motivational and engagement processes in science learning. Membiela et al. ([<reflink idref="bib59" id="ref155">59</reflink>]) likewise observed in preservice elementary school teachers that boredom and enjoyment mediate between relevance of personal goals and self‐efficacy in learning science, and engagement toward science studies.</p> <hd id="AN0164095124-39">Predictive binary relationships</hd> <p>The hypotheses on motivational variables as predictors of emotions and engagement (H1, H2, and H3), as well as, on emotions as predictors of engagement (H4 and H5), are confirmed. These results make novel contributions and confirm previous studies on the binary relationships between the variables studied:</p> <p></p> <ulist> <item> (a) Relevance to personal goals (H1) is confirmed as a predictor of boredom and enjoyment in science classes, and of engagement towards science studies in compulsory secondary education. Related variables have shown a relationship with enjoyment (Ainley & Ainley, [<reflink idref="bib2" id="ref156">2</reflink>], [<reflink idref="bib3" id="ref157">3</reflink>]) and with engagement (González & Paoloni, [<reflink idref="bib38" id="ref158">38</reflink>]).</item> <p></p> <item> (b) Self‐efficacy (H2) is confirmed as a predictor of boredom and enjoyment in science classes, and engagement towards science studies in compulsory secondary education. Self‐efficacy is related to emotions (Pekrun & Perry, [<reflink idref="bib77" id="ref159">77</reflink>]) and learning engagement in science (Bae & DeBusk‐Lane, [<reflink idref="bib6" id="ref160">6</reflink>]; Ben‐Eliyahu et al., [<reflink idref="bib12" id="ref161">12</reflink>]; Britner & Pajares, [<reflink idref="bib13" id="ref162">13</reflink>]; Chen & Pajares, [<reflink idref="bib19" id="ref163">19</reflink>]; Lee et al., [<reflink idref="bib54" id="ref164">54</reflink>]; Uçar & Sungur, [<reflink idref="bib91" id="ref165">91</reflink>]; Yang et al., [<reflink idref="bib96" id="ref166">96</reflink>]).</item> <p></p> <item> (c) Interest in a scientific career (H3) is confirmed as a predictor of boredom and enjoyment in science classes, and of engagement towards science studies in compulsory secondary education. Personal and situational interest improves engagement in Physics students (González & Paoloni, [<reflink idref="bib38" id="ref167">38</reflink>]).</item> <p></p> <item> (d) Boredom (H4) is confirmed as a predictor of engagement towards science studies in compulsory secondary education. Boredom has negative effects on engagement (Camacho‐Morles et al., [<reflink idref="bib16" id="ref168">16</reflink>]; Tze et al., [<reflink idref="bib90" id="ref169">90</reflink>]). The lack of relationship between boredom and engagement found by Taasoobshirazi et al. ([<reflink idref="bib86" id="ref170">86</reflink>]) may be related to our results, as there is hardly any direct effect of boredom on engagement. The above warrants the importance of studying the indirect or mediated effects, besides the direct effects, to have a more complete knowledge of the relationships between variables.</item> <p></p> <item> (e) Enjoyment (H5) is confirmed as a predictor of engagement towards science studies in compulsory secondary education. It has been associated with higher levels of engagement (Ben‐Eliyahu et al., [<reflink idref="bib12" id="ref171">12</reflink>]; Pekrun et al., [<reflink idref="bib73" id="ref172">73</reflink>]; Pekrun, Elliot, et al., [<reflink idref="bib70" id="ref173">70</reflink>]).</item> </ulist> <hd id="AN0164095124-40">Simultaneous predictive relationships</hd> <p>Also confirmed in the set of variables studied are the simultaneous relationships between the relevance of science learning for personal goals and self‐efficacy in science learning, and that of interest in a scientific career with boredom and enjoyment in science classes, and all of them with engagement towards science studies in compulsory secondary education students. Membiela et al. ([<reflink idref="bib59" id="ref174">59</reflink>]), in a previous study with preservice elementary school teachers, related relevance of science learning for personal goals and self‐efficacy in learning science to boredom and enjoyment, and all of them with engagement.</p> <hd id="AN0164095124-41">Direct, indirect and total effects</hd> <p>Upon taking into account the direct, indirect and total effects, students with more willingness to learn science (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career), were observed to have less boredom and more enjoyment in science classes, and thus more engagement towards science studies. Students' engagement is not only due to the direct effects of motivational and emotional variables, but also due to the indirect effects of motivational variables on the engagement achieved thanks to the mediating role of emotions. In this sense, we would like to emphasize that the effect of boredom on engagement is essentially due to indirect or mediated effects because the direct effect is very small. Also noteworthy is the greater mediating effect of enjoyment as compared to boredom in science classes.</p> <hd id="AN0164095124-42">Equivalence or invariance in simultaneous predictive relationships</hd> <p>Moreover, worth highlighting is that the research was done in two sociocultural contexts (Chile and Spain) that are underrepresented in research on emotions in science education. A novel contribution is that the simultaneous predictive relationships are similar, equivalent or invariant between the Chilean and Spanish subsamples. Hence, despite the differences found, there is similarity, equivalence or invariance between the subsamples of Chile and Spain in the relevant set of simultaneous predictive relationships studied. Bellocchi ([<reflink idref="bib10" id="ref175">10</reflink>]) point to the need for a greater understanding of emotions in a wider educational context of the classroom, center, educational systems, cultures or socioeconomic environments. From another theoretical perspective, Pekrun and Linnenbrink‐García ([<reflink idref="bib75" id="ref176">75</reflink>]) indicate that beyond the classroom, the relevant hierarchical levels are: educational institutions lie within educational systems which are part of societies and societies in turn are a part of cultures and socio‐historical macrosystems. This is precisely where our work is an important contribution to not only understand students' emotions in learning science, but also the antecedents and consequences of emotions in the Chilean and Spanish subsamples. Also noteworthy is that the vast majority of research on emotions is focused on studying the context of science classrooms, such as those located in the theoretical framework of the sociology of emotions.</p> <hd id="AN0164095124-43">Practical implications</hd> <p>The results are important to the teaching and learning of science in compulsory secondary education. It is therefore very important to develop actions that engage all students, and not just the ones most motivated in learning science, who experience better emotions in science classes or feel more engaged with science studies.</p> <p>In this sense, we can influence relevance of science learning for personal goals and improve students' self‐efficacy for learning science and interest in a scientific career, besides reducing boredom in science classes and increasing enjoyment in science classes, and promoting engagement (vigor, dedication and absorption) towards science studies. All of these variables are under the influence of science teachers and their teaching. In this sense, and understood as improvement possibilities in teaching practice, actions can be taken to enhance science learning in the following ways:</p> <hd id="AN0164095124-44">The motivations associated with learning science can be increased</hd> <p>The science subjects taught must have personal relevance to students. In this sense, according to Kang and Keinonen ([<reflink idref="bib47" id="ref177">47</reflink>]), topics should be relevant to students so that they positively influence interest and performance in science. The personal value of science powerfully predicts student enjoyment (Ainley & Ainley, [<reflink idref="bib2" id="ref178">2</reflink>], [<reflink idref="bib3" id="ref179">3</reflink>]). However, Kapon et al. ([<reflink idref="bib48" id="ref180">48</reflink>]) highlight the tension between personal relevance of science and school science. Thus, there has been a debate on what scientific content should be included to increase the interest and engagement of all students, whilst catering to that important minority who aspire to become scientists (Treagust & Chi‐Yan, [<reflink idref="bib88" id="ref181">88</reflink>]). Furthermore, Cetin‐Dindar ([<reflink idref="bib18" id="ref182">18</reflink>]) indicates how motivation to learn science increases when there are more opportunities to relate science to real‐world problems. Therefore, science teachers need to put more emphasis on connecting science in school with real life situations, to motivate students to learn science.</p> <p>Self‐efficacy in science learning should be promoted. Adequate mastery and indirect experience are two of the main sources of self‐efficacy (Bandura, [<reflink idref="bib8" id="ref183">8</reflink>]). Hence, effective science instruction programmes should be promoted to take advantage of the positive influence of motivation to learn science and engagement towards science studies.</p> <p>We need to promote interest in a scientific career. In this regard, Zhao et al. ([<reflink idref="bib97" id="ref184">97</reflink>]) suggest that involving students in authentic scientific work with mentors could be a good strategy to promote the pursuit of scientific careers by students. Dabney et al. ([<reflink idref="bib24" id="ref185">24</reflink>]) observed a strong relationship between professional interest in Science, Technology, Engineering and Mathematics (STEM) and participation in extracurricular scientific events since they can influence students' interest in scientific careers (Aubusson et al., [<reflink idref="bib4" id="ref186">4</reflink>]), or in scientific activities outside school hours. The importance of mastery experiences in promoting science, technology, engineering, and mathematics related careers has also been highlighted (Deemer & Sharma, [<reflink idref="bib28" id="ref187">28</reflink>]).</p> <hd id="AN0164095124-45">The emotions associated with learning science can be enhanced</hd> <p>Reducing boredom in science classes, and especially promoting enjoyment, can improve student engagement towards science studies. According to Tomas et al. ([<reflink idref="bib87" id="ref188">87</reflink>]), emotions, and particularly positive ones, dominate students' experiences and perceptions, and such positive emotions could effectively be regulated to keep students focused on learning.</p> <p>Boredom in science classes can be reduced if students perceive high control or value in learning, are motivated for success and achievement in science, have effective teachers, and do not participate in monotonous activities (Pekrun, [<reflink idref="bib69" id="ref189">69</reflink>]). Boredom can be minimized (Goetz & Hall, [<reflink idref="bib35" id="ref190">35</reflink>]), by increasing the perceived value and interest in the learning material, for example by highlighting the relevance of the learning material to students' daily lives. Use of strategies that make situations more interesting or worthwhile are probably more effective than avoiding boring ones. As Goetz and Hall ([<reflink idref="bib35" id="ref191">35</reflink>]) indicate, it is important that teachers know when students are bored in class and the underlying reasons. They can help students to identify and anticipate boredom or use coping strategies to reduce it, and take more responsibility and thus control boredom. It is extremely important to interrupt the downward spiral of emotions that occurs when a student remains bored for long periods, leading to frustration and eventually to disconnection (Graesser et al., [<reflink idref="bib40" id="ref192">40</reflink>]).</p> <p>Enjoyment can also be promoted by achievements in science learning (Perera, [<reflink idref="bib78" id="ref193">78</reflink>]), or by valuing topics that interest students and using them to evaluate achievement in science (Ainley & Ainley, [<reflink idref="bib2" id="ref194">2</reflink>]). Moreover, a higher interest in learning science unaccompanied by enjoyment may mean non‐genuine interest (Jack & Lin, [<reflink idref="bib43" id="ref195">43</reflink>]). Participation in student‐centered activities with support for development of their autonomy in flipped classrooms is expected to increase enjoyment in learning (Cho et al., [<reflink idref="bib22" id="ref196">22</reflink>]).</p> <p>From a broader perspective focused on educational practice, attention has been drawn to the importance of seeing affect as an integral part of science learning and practice, as an instructional goal in its own right, both in research efforts, as well as, in policy documents, curriculum, and professional development (Davidson et al., [<reflink idref="bib25" id="ref197">25</reflink>]. Our work, by evidencing the important mediating role of emotions in science classes, also indicates that emotions should play a central role in learning science. At the other end are broad proposals such as the integration of emotional learning within science classes, which are more likely to help develop emotional literacy and interest in science (Matthews, [<reflink idref="bib57" id="ref198">57</reflink>], [<reflink idref="bib58" id="ref199">58</reflink>]). The Improving Science and Emotional Development curriculum project has shown good results in developing students' emotional literacy and their attitudes towards science (Matthews, [<reflink idref="bib57" id="ref200">57</reflink>]).</p> <hd id="AN0164095124-46">We can enhance engagement towards science studies through teaching strategies and learning ac...</hd> <p>In this sense, it is necessary to involve students in activities that promote interest and skills, especially at the beginning of secondary education (Newhouse, [<reflink idref="bib62" id="ref201">62</reflink>]). On the subject of science teaching (Sinatra et al., [<reflink idref="bib85" id="ref202">85</reflink>]), teachers may want to consider creating a classroom environment in which controversial topics are used to promote student engagement and learning and, at the same time, use strategies that buffer negative emotions associated with the topic that can act as a barrier to learning. Teachers can support autonomous, practice‐based science activities such as sharing and testing scientific ideas, drawing on students' prior knowledge base and experiences, and developing explanations for phenomena in the natural world (Bae & DeBusk‐Lane, [<reflink idref="bib6" id="ref203">6</reflink>]). Scientific education based on inquiry, focused on personal improvement, via decision‐making and control of actions by students in science classes has been recommended by Ucar and Sungur ([<reflink idref="bib91" id="ref204">91</reflink>]). These types of activities also influence engagement in science (Grabau & Ma, [<reflink idref="bib39" id="ref205">39</reflink>]). Thus, some activities that use models, and other applied activities are associated with enjoyment of science and personal appraisal of science, while others such as practical activities are associated with self‐efficacy and general interest in learning science. The use of demonstrations in science education can serve to motivate student learning by increasing interest and engagement in the science classroom from high schools to universities (Treagust & Chi‐Yan, [<reflink idref="bib88" id="ref206">88</reflink>]). Other strategies could include flipped classrooms, which have lowered negative emotions such as boredom (Jeong et al., [<reflink idref="bib46" id="ref207">46</reflink>]), or authentic 5E instruction that can reduce boredom compared to text‐based instruction (Parsons et al., [<reflink idref="bib65" id="ref208">65</reflink>]). Scientific activities with positive emotional responses can contribute to improving interest and participation in science (King et al., [<reflink idref="bib50" id="ref209">50</reflink>]; Volet et al., [<reflink idref="bib94" id="ref210">94</reflink>]).</p> <p>Students at different education levels today learn science both in compulsory primary and secondary education and in non‐compulsory ones. Therefore, relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career, and boredom and enjoyment in science classes, can influence students' engagement towards science studies.</p> <hd id="AN0164095124-47">Limitations and lines of future research</hd> <p>Although this is a rigorously designed and conducted study, its limitations should be taken into account when interpreting its findings. The first limitation is due to the educational contexts of Chilean and Spanish educational systems, since they are to some extent different from those in other countries.</p> <p>The second limitation is that the relationships between the different variables studied can be affected by a common variance bias due to the use of self‐report surveys to collect data. In this sense, the study could be complemented with other instruments such as observation in the classroom or teacher appraisals of motivations, emotions and engagement associated with science learning.</p> <p>The third limitation is that there may be interactions among the variables studied, something already indicated by the models of reciprocal causality of emotions (Frenzel et al., [<reflink idref="bib31" id="ref211">31</reflink>]). Following the social cognitive theory and the control‐value theory of emotions, the relationships are expected to occur in both directions, and we have chosen the direction towards achievement: motivational variables → emotional variables → engagement variable, in accordance with the theoretical foundation and previous research.</p> <p>The fourth limitation is that this study has a correlational quantitative research design, which can be complemented in future research with qualitative or mixed design types (Creswell & Creswell, [<reflink idref="bib23" id="ref212">23</reflink>]). These designs can help better understand the relationships between motivational, emotional and engagement variables associated with learning science.</p> <p>The fifth limitation is that only the relationships between a small number of relevant variables were studied. Future research should include the behavior of other motivational, emotional and engagement variables associated with learning science; or variables from other domains, such as the influence of the classroom environment in science learning. Other possibilities would be to study what happens in other countries, at other levels such as primary or higher education, or even in other subjects such as mathematics or technology.</p> <p>As Pekrun and Linnenbrink‐García ([<reflink idref="bib75" id="ref213">75</reflink>]) point out, future research on emotions in education should try to build more inclusive approaches. More specifically, frameworks are needed that integrate perspectives at different levels and contexts of student and teacher emotions, including the dynamics of processes within and between levels (e.g., between components of emotions, between emotions, motivation, and cognition, between teachers and students; and between different institutional and socio‐historical contexts).</p> <hd id="AN0164095124-48">CONCLUSIONS</hd> <p>The importance of motivational variables (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career) as predictors of emotions (boredom and enjoyment in science classes) has been showed in compulsory science education. Also confirmed is the important joint role of motivational and emotional variables in predicting engagement towards science studies.</p> <p>A relevant original contribution is the mediating role of emotions (boredom and enjoyment in science classes) between motivation (relevance of science learning for personal goals, self‐efficacy for learning science, interest in a scientific career) and engagement towards science studies.</p> <p>We are unaware of any previous SEM analyses used to simultaneously study the relationships between a set of relevant motivational variables, emotions and engagement in science learning by secondary students.</p> <p>Finally, and to increase students' engagement towards science studies, teachers will be able to promote relevance of science learning for personal goals, self‐efficacy for learning science, and interest in a scientific career, as well as reduce boredom and improve enjoyment in science classes.</p> <hd id="AN0164095124-49">DATA AVAILABILITY STATEMENT</hd> <p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p> <ref id="AN0164095124-50"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref75" type="bt">1</bibl> <bibtext> Acee, T. W., Kim, H., Kim, H. J., Kim, J.‐I., Chu, H.‐N. R., Kim, M., Cho, Y., Wicker, F. W., & Boredom Research Group. (2010). Academic boredom in under‐ and over‐challenging situations. Contemporary Educational Psychology, 35 (1), 17 – 27. https://doi.org/10.1016/j.cedpsych.2009.08.002</bibtext> </blist> <blist> <bibl id="bib2" idref="ref46" type="bt">2</bibl> <bibtext> Ainley, M., & Ainley, J. (2011a). Student engagement with science in early adolescence: The contribution of enjoyment to students' continuing interest in learning about science. Contemporary Educational Psychology, 36 (1), 4 – 12. https://doi.org/10.1016/j.cedpsych.2010.08.001.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref47" type="bt">3</bibl> <bibtext> Ainley, M., & Ainley, J. (2011b). A cultural perspective on the structure of student interest in science. International Journal of Science Education, 33 (1), 51 – 71. https://doi.org/10.1080/09500693.2010.518640</bibtext> </blist> <blist> <bibl id="bib4" idref="ref128" type="bt">4</bibl> <bibtext> Aubusson, P., Griffin, J., & Kearney, M. (2012). Learning beyond the classroom: Implications for school science. In B. Fraser, K. Tobin, & C. McRobbie (Eds.), Second international handbook of science education. Springer. https://doi.org/10.1007/978-1-4020-9041-7_74</bibtext> </blist> <blist> <bibl id="bib5" idref="ref14" type="bt">5</bibl> <bibtext> Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45 (4), 210 – 223. https://doi.org/10.1080/00461520.2010.515934</bibtext> </blist> <blist> <bibl id="bib6" idref="ref20" type="bt">6</bibl> <bibtext> Bae, C. L., & DeBusk‐Lane, M. (2019). Middle school engagement profiles: Implications for motivation and achievement in science. Learning and Individual Differences, 74, 101753. https://doi.org/10.1016/j.lindif.2019.101753</bibtext> </blist> <blist> <bibl id="bib7" idref="ref18" type="bt">7</bibl> <bibtext> Bandura, A. (1993). Perceived self‐efficacy in cognitive development and functioning. Educational Psychologist, 28 (2), 117 – 148. https://doi.org/10.1207/s15326985ep2802_3</bibtext> </blist> <blist> <bibl id="bib8" idref="ref11" type="bt">8</bibl> <bibtext> Bandura, A. (1997). Self‐efficacy: The exercise of control. Freeman & Company.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref12" type="bt">9</bibl> <bibtext> Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52 (1), 1 – 26. https://doi.org/10.1146/annurev.psych.52.1.1</bibtext> </blist> <blist> <bibtext> Bellocchi, A. (2019). Early career science teacher experiences of social bonds and emotion management. Journal of Research in Science Teaching, 56 (3), 322 – 347. https://doi.org/10.1002/tea.21520</bibtext> </blist> <blist> <bibtext> Bellocchi, A., & Ritchie, S. M. (2015). I was proud of myself that I didn't give up and I did it": Experiences of pride and triumph in learning science. Science Education, 99 (4), 638 – 668. https://doi.org/10.1002/sce.21159</bibtext> </blist> <blist> <bibtext> Ben‐Eliyahu, A., Moore, D., Dorph, R., & Schunn, C. D. (2018). Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts. Contemporary Educational Psychology, 53, 87 – 105. https://doi.org/10.1016/j.cedpsych.2018.01.002</bibtext> </blist> <blist> <bibtext> Britner, S. L., & Pajares, F. (2006). Sources of science self‐efficacy beliefs of middle school students. Journal of Research in Science Teaching, 43 (5), 485 – 499. https://doi.org/10.1002/tea.20131</bibtext> </blist> <blist> <bibtext> Burns, E. C., Martin, A. J., & Collie, R. J. (2019). Examining the yields of growth feedback from science teachers and students' intrinsic valuing of science: Implications for student‐ and school‐level science achievement. Journal of Research in Science Teaching, 56 (8), 1060 – 1082. https://doi.org/10.1002/tea.21546</bibtext> </blist> <blist> <bibtext> Byrne, B. (2016). Structural equation modeling with AMOS. Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781315757421</bibtext> </blist> <blist> <bibtext> Camacho‐Morles, J., Slemp, G. R., Pekrun, R., Loderer, K., Hou, H., & Oades, L. G. (2021). Activity achievement emotions and academic performance: A meta‐analysis. Educational Psychology Review, 33 (3), 1051 – 1095. https://doi.org/10.1007/s10648-020-09585-3</bibtext> </blist> <blist> <bibtext> Cavallo, A. M. L., Rozman, M., Blinkenstaff, J., & Walker, N. (2003). Students' learning approaches, reasoning abilities, motivational goals, and epistemological beliefs in differing college science courses. Journal of College Science Teaching, 33 (3), 18 – 23.</bibtext> </blist> <blist> <bibtext> Cetin‐Dindar, A. (2016). Student motivation in constructivist learning environment. Eurasia Journal of Mathematics Science and Technology Education, 12 (2), 233 – 247. https://doi.org/10.12973/eurasia.2016.1399a</bibtext> </blist> <blist> <bibtext> Chen, J. A., & Pajares, F. (2010). Implicit theories of ability of grade 6 science students: Relation to epistemological beliefs and academic motivation and achievement in science. Contemporary Educational Psychology, 35 (1), 75 – 87. https://doi.org/10.1016/j.cedpsych.2009.10.003</bibtext> </blist> <blist> <bibtext> Chen, J. A., & Usher, E. L. (2013). Profiles of the sources of science self‐efficacy. Learning and Individual Differences, 24, 11 – 21. https://doi.org/10.1016/j.lindif.2012.11.002</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, 233 – 255. https://doi.org/10.1207/S15328007SEM0902_5</bibtext> </blist> <blist> <bibtext> Cho, M.‐H., Park, S. W., & Lee, S. (2021). Student characteristics and learning and teaching factors predicting affective and motivational outcomes in flipped college classrooms. Studies in Higher Education, 46 (3), 509 – 522. https://doi.org/10.1080/03075079.2019.1643303</bibtext> </blist> <blist> <bibtext> Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage.</bibtext> </blist> <blist> <bibtext> Dabney, K. P., Tai, R. H., Almarode, J. T., Miller‐Friedmann, J. L., Sonnert, G., Sadler, P. M., & Hazari, Z. (2012). Out‐of‐school time science activities and their association with career interest in STEM. International Journal of Science Education, Part B, 2 (1), 63 – 79. https://doi.org/10.1080/21548455.2011.629455</bibtext> </blist> <blist> <bibtext> Davidson, S. G., Jaber, L. Z., & Southerland, S. A. (2020). Emotions in the doing of science: Exploring epistemic affect in elementary teachers' science research experiences. Science Education, 104 (6), 1008 – 1040. https://doi.org/10.1002/sce.21596</bibtext> </blist> <blist> <bibtext> Davis, J. P., & Bellocchi, A. (2018). Objectivity, subjectivity, and emotion in school science inquiry. Journal of Research in Science Teaching, 55 (10), 1419 – 1447. https://doi.org/10.1002/tea.21461</bibtext> </blist> <blist> <bibtext> Deemer, E. D., Marks, L. R., & Miller, K. A. (2017). Peer science self‐efficacy: A proximal contextual support for college students' science career intentions. Journal of Career Assessment, 25 (3), 537 – 551. https://doi.org/10.1177/1069072716651620</bibtext> </blist> <blist> <bibtext> Deemer, E. D., & Sharma, P. (2019). Situational interest and scientific self‐efficacy: Influence of an energy science career intervention. The Career Development Quarterly, 67 (2), 171 – 184. https://doi.org/10.1002/cdq.12179</bibtext> </blist> <blist> <bibtext> Elliot, A. J., & Pekrun, R. (2007). Emotion in the hierarchical model of approach‐avoidance of achievement motivation. In P. A. Schutz & R. Pekrun (Eds.), Emotions in education (pp. 57 – 73). Academic Press. https://doi.org/10.1016/B978-012372545-5/50005-8</bibtext> </blist> <blist> <bibtext> Fraser, B. J. (1978). Development of a test of science‐related attitudes. Science Education, 62 (4), 509 – 515. https://doi.org/10.1002/sce.3730620411</bibtext> </blist> <blist> <bibtext> Frenzel, A. C., Goetz, T., Stephens, E. J., & Jacob, B. (2009). Antecedents and effects of teachers' emotional experiences: An integrated perspective and empirical test. In P. A. Schutz & M. Zembylas (Eds.), Advances in teacher emotion research: The impact on teachers' lives (pp. 129 – 151). Springer. https://doi.org/10.1007/978-1-4419-0564-2_7</bibtext> </blist> <blist> <bibtext> Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2007). Nonscience majors learning science: A theoretical model of motivation. Journal of Research in Science Teaching, 44 (8), 1088 – 1107. https://doi.org/10.1002/tea.20181</bibtext> </blist> <blist> <bibtext> Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: Construct validation with non‐science majors. Journal of Research in Science Teaching, 46 (2), 127 – 146. https://doi.org/10.1002/tea.20267</bibtext> </blist> <blist> <bibtext> Goetz, T., Hall, N. C., Frenzel, A. C., & Pekrun, R. (2006). A hierarchical conceptualization of enjoyment in students. Learning and Instruction, 16 (4), 323 – 338. https://doi.org/10.1016/j.learninstruc.2006.07.004</bibtext> </blist> <blist> <bibtext> Goetz, T., & Hall, N. C. (2014). Academic boredom. In R. Pekrun & L. Linnenbrink‐Garcia (Eds.), International handbook of emotions in education (pp. 311 – 330). Routledge/Taylor & Francis Group.</bibtext> </blist> <blist> <bibtext> Goetz, T., Pekrun, R., Hall, N., & Haag, L. (2006). Academic emotions from a social‐cognitive perspective: Antecedents and domain specificity of students' affect in the context of Latin instruction. British Journal of Educational Psychology, 76 (2), 289 – 308. https://doi.org/10.1348/000709905X42860</bibtext> </blist> <blist> <bibtext> González, A., Fernández, M. V. C., & Paoloni, P. V. (2017). Hope and anxiety in physics class: Exploring their motivational antecedents and influence on metacognition and performance. Journal of Research in Science Teaching, 54 (5), 558 – 585. https://doi.org/10.1002/tea.21377</bibtext> </blist> <blist> <bibtext> González, A., & Paoloni, P. V. (2014). Engagement and performance in physics: The role of class instructional strategies, and student's personal and situational interest. Revista de Psicodidactica/Journal of Psychodidactics, 20 (1), 25 – 45. https://doi.org/10.1387/RevPsicodidact.11370</bibtext> </blist> <blist> <bibtext> Grabau, L. J., & Ma, X. (2017). Science engagement and science achievement in the context of science instruction: A multilevel analysis of U.S. students and schools. International Journal of Science Education, 39 (8), 1045 – 1068. https://doi.org/10.1080/09500693.2017.1313468</bibtext> </blist> <blist> <bibtext> Graesser, A. C., D'Mello, S. K., & Strain, A. C. (2014). Emotions in advanced learning technologies. In R. Pekrun & L. Linnenbrink‐Garcia (Eds.), International handbook of emotions in education (pp. 473 – 493). Routledge.</bibtext> </blist> <blist> <bibtext> Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate data analysis. Pearson.</bibtext> </blist> <blist> <bibtext> Humphreys, D., & Davenport, A. (2005). What really matters in college: How students view and value liberal education. Liberal Education, 91 (3), 36 – 43.</bibtext> </blist> <blist> <bibtext> Jack, B. M., & Lin, H. (2018). Warning! Increases in interest without enjoyment may not be trend predictive of genuine interest in learning science. International Journal of Educational Development, 62, 136 – 147. https://doi.org/10.1016/j.ijedudev.2018.03.005</bibtext> </blist> <blist> <bibtext> Jack, B. M., & Lin, H.‐S. (2014). Igniting and sustaining interest among students who have grown cold toward science. Science Education, 98 (5), 792 – 814. https://doi.org/10.1002/sce.21119</bibtext> </blist> <blist> <bibtext> Jack, B. M., & Lin, H. (2017). Making learning interesting and its application to the science classroom. Studies in Science Education, 53 (2), 137 – 164. https://doi.org/10.1080/03057267.2017.1305543</bibtext> </blist> <blist> <bibtext> Jeong, J. S., González‐Gómez, D., & Cañada‐Cañada, F. (2016). Students' perceptions and emotions toward learning in a flipped general science classroom. Journal of Science Education and Technology, 25, 747 – 758. https://doi.org/10.1007/s10956-016-9630-8.</bibtext> </blist> <blist> <bibtext> Kang, J., & Keinonen, T. (2018). The effect of student‐centered approaches on students' interest and achievement in science: Relevant topic‐based, open and guided inquiry‐based and discussion‐based approaches. Research in Science Education, 48 (4), 865 – 885. https://doi.org/10.1007/s11165-016-9590-2</bibtext> </blist> <blist> <bibtext> Kapon, S., Laherto, A., & Levrini, O. (2018). Disciplinary authenticity and personal relevance in school science. Science Education, 102 (5), 1077 – 1106. https://doi.org/10.1002/sce.21458</bibtext> </blist> <blist> <bibtext> Kervinen, A., Roth, W. M., Juuti, K., & Uitto, A. (2020). "How stupid can a person be?" –Students coping with authoritative dimensions of science lessons. Learning Culture and Social Interaction, 24, Article 100367. https://doi.org/10.1016/j.lcsi.2019.100367</bibtext> </blist> <blist> <bibtext> King, D., Ritchie, S., Sandhu, M., & Henderson, S. (2015). Emotionally intense science activities. International Journal of Science Education, 37 (12), 1886 – 1914. https://doi.org/10.1080/09500693.2015.1055850</bibtext> </blist> <blist> <bibtext> Kıran, D., Sungur, S., & Yerdelen, S. (2019). Predicting science engagement with motivation and teacher characteristics: A multilevel investigation. International Journal of Science and Mathematics Education, 17 (1), 67 – 88. https://doi.org/10.1007/s10763-018-9882-2</bibtext> </blist> <blist> <bibtext> Kuppens, P. (2008). Individual differences in the relationship between pleasure and arousal. Journal of Research in Personality, 42 (4), 1053 – 1059. https://doi.org/10.1016/j.jrp.2007.10.007</bibtext> </blist> <blist> <bibtext> Lawson, A. E., Banks, D. L., & Logvin, M. (2007). Self‐efficacy, reasoning ability, and achievement in college biology. Journal of Research in Science Teaching, 44 (5), 706 – 724. https://doi.org/10.1002/tea.20172</bibtext> </blist> <blist> <bibtext> Lee, C. S., Hayes, K. N., Seitz, J., DiStefano, R., & O'Connor, D. (2016). Understanding motivational structures that differentially predict engagement and achievement in middle school science. International Journal of Science Education, 38 (2), 192 – 215. https://doi.org/10.1080/09500693.2015.1136452</bibtext> </blist> <blist> <bibtext> Linnenbrink, E. A., & Pintrich, P. R. (2004). Role of affect in cognitive processing in academic contexts. In D. Y. Dai (Ed.), Motivation, emotion, and cognition: Integrative perspectives on intellectual development and functioning (pp. 57 – 87). Lawrence Erlbaum Associates.</bibtext> </blist> <blist> <bibtext> Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta‐analysis on emotions in technology‐based learning environments. Learning and Instruction, 70, 101162. https://doi.org/10.1016/j.learninstruc.2018.08.002</bibtext> </blist> <blist> <bibtext> Matthews, B. (2004). Promoting emotional literacy, equity and interest in science lessons for 11‐14 year olds; the 'improving science and emotional development' project. International Journal of Science Education, 26, 281 – 308. https://doi.org/10.1080/0950069032000097406</bibtext> </blist> <blist> <bibtext> Matthews, B. (2015). The elephant in the room: Emotional literacy/intelligence, science education, and gender. In D. Corrigan, C. Buntting, J. Dillon, A. Jones, & R. Gunstone (Eds.), The future in learning science: What's in it for the learner? (pp. 193 – 212). Springer. https://doi.org/10.1007/978-3-319-16543-1_10</bibtext> </blist> <blist> <bibtext> Membiela, P., Vidal, M., Fragueiro, S., Lorenzo, M., García‐Rodeja, I., Aznar, V., Bugallo, A., & González, A. (2021). Motivation for science learning as an antecedent of emotions and engagement in preservice elementary teachers. Science Education, 106 (1), 119 – 141. https://doi.org/10.1002/sce.21686</bibtext> </blist> <blist> <bibtext> Navarro, M., Förster, C., González, C., & González‐Pose, P. (2016). Attitudes toward science: Measurement and psychometric properties of the test of science‐related attitudes for its use in Spanish‐speaking classrooms. International Journal of Science Education, 38 (9), 1459 – 1482. https://doi.org/10.1080/09500693.2016.1195521</bibtext> </blist> <blist> <bibtext> Nett, U. E., Goetz, T., & Daniels, L. M. (2010). What to do when feeling bored? Learning and Individual Differences, 20, 626 – 638. https://doi.org/10.1016/j.lindif.2010.09.004</bibtext> </blist> <blist> <bibtext> Newhouse, C. P. (2016). STEM the boredom: Engage students in the Australian curriculum using ICT with problem‐based learning and assessment. Journal of Science Education and Technology, 26 (1), 44 – 57. https://doi.org/10.1007/s10956-016-9650-4</bibtext> </blist> <blist> <bibtext> Organization for Economic Cooperation and Development (OECD). (2006). Evolution of student interest in science and technology studies: Policy report. OECD Global Science Forum.</bibtext> </blist> <blist> <bibtext> Organization for Economic Cooperation and Development (OECD). (2009). PISA 2006 technical report. OECD.</bibtext> </blist> <blist> <bibtext> Parsons, A. E., Heddy, B. C., Wilson, S. N., Williams, L. A., Atkinson, L. K., & Garn, G. A. (2021). Making 'brain food': Converting photosynthesis into a positive learning experience through authentic instruction. Journal of Biological Education, 55 (3), 293 – 305. https://doi.org/10.1080/00219266.2019.1682640</bibtext> </blist> <blist> <bibtext> Pawlak, M., Derakhshan, A., Mehdizadeh, M., & Kruk, M. (2021). Boredom in online English language classes: Mediating variables and coping strategies. Language Teaching Research, 13621688211064944. https://doi.org/10.1177/13621688211064944</bibtext> </blist> <blist> <bibtext> Pawlak, M., Kruk, M., Zawodniak, J., & Pasikowski, S. (2020). Investigating factors responsible for boredom in English classes: The case of advanced learners. System, 91, 102259. https://doi.org/10.1016/j.system.2020.102259</bibtext> </blist> <blist> <bibtext> Peixoto, F., Mata, L., Monteiro, V., Sanches, C., & Pekrun, R. (2015). The achievement emotions questionnaire: Validation for pre‐adolescent students. European Journal of Developmental Psychology, 12 (4), 472 – 481. https://doi.org/10.1080/17405629.2015.1040757</bibtext> </blist> <blist> <bibtext> Pekrun, R. (2006). The control‐value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18 (4), 315 – 341. https://doi.org/10.1007/s10648-006-9029-9</bibtext> </blist> <blist> <bibtext> Pekrun, R., Elliot, A. J., & Maier, M. A. (2006). Achievement goals and discrete achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology, 98, 583 – 597. https://doi.org/10.1037/0022-0663.98.3.583</bibtext> </blist> <blist> <bibtext> Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: Exploring control‐value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102 (3), 531 – 549. https://doi.org/10.1037/a0019243</bibtext> </blist> <blist> <bibtext> Pekrun, R., Goetz, T., & Perry, R. P. (2005). Achievement Emotions Questionnaire (AEQ)—User's manual. University of Munich, Department of Psychology.</bibtext> </blist> <blist> <bibtext> Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students' self‐regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37 (2), 91 – 105. https://doi.org/10.1207/S15326985EP3702_4</bibtext> </blist> <blist> <bibtext> Pekrun, R., & Linnenbrink‐García, L. (2012). Academic emotions and student engagement. In S. L. Christensen, A. L. Reschley, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259 – 282). Springer.</bibtext> </blist> <blist> <bibtext> Pekrun, R., & Linnenbrink‐García, L. (2014). Conclusions and future directions. In R. Pekrun & L. Linnenbrink‐García (Eds.), International handbook of emotions in education (pp. 659 – 675). Routledge.</bibtext> </blist> <blist> <bibtext> Pekrun, R., Muis, M. R., Frenzel, A. C., & Goetz, T. (2018). Emotions at school. Routledge.</bibtext> </blist> <blist> <bibtext> Pekrun, R., & Perry, R. (2014). Control‐value theory of achievement emotions. In R. Pekrun & L. Linnenbrink‐Garcia (Eds.), International handbook of emotions in education (pp. 120 – 141). Routledge.</bibtext> </blist> <blist> <bibtext> Perera, L. D. H. (2014). Parents' attitudes towards science and their children's science achievement. International Journal of Science Education, 36 (18), 3021 – 3041. https://doi.org/10.1080/09500693.2014.949900</bibtext> </blist> <blist> <bibtext> Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95 (4), 667 – 686. https://doi.org/10.1037/0022-0663.95.4.667</bibtext> </blist> <blist> <bibtext> Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 (3), 879 – 891. https://doi.org/10.3758/BRM.40.3.879</bibtext> </blist> <blist> <bibtext> Radoff, J., Jaber, L. Z., & Hammer, D. (2019). "It's scary but it's also exciting": Evidence of meta‐affective learning in science. Cognition and Instruction, 37 (1), 73 – 92. https://doi.org/10.1080/07370008.2018.1539737</bibtext> </blist> <blist> <bibtext> Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2006). The measurement of work engagement with a short questionnaire: A cross‐national study. Educational and Psychological Measurement, 66 (4), 701 – 716. https://doi.org/10.1177/0013164405282471</bibtext> </blist> <blist> <bibtext> Schaufeli, W. B., Martínez, I. M., Pinto, A. M., Salanova, M., & Bakker, A. B. (2002). Burnout and engagement in university students: A cross‐national study. Journal of Cross‐Cultural Psychology, 33 (5), 464 – 481. https://doi.org/10.1177/0022022102033005003</bibtext> </blist> <blist> <bibtext> Schaufeli, W. B., Salanova, M., González‐Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3 (1), 71 – 92. https://doi.org/10.1023/A:1015630930326</bibtext> </blist> <blist> <bibtext> Sinatra, G. M., Broughton, S. H., & Lombardi, D. O. U. G. (2014). Emotions in science education. In R. Pekrun & L. Linnenbrink‐García (Eds.), International handbook of emotions in education (pp. 415 – 436). Routledge.</bibtext> </blist> <blist> <bibtext> Taasoobshirazi, G., Heddy, B., Bailey, M., & Farley, J. (2016). A multivariate model of conceptual change. Instructional Science, 44 (2), 125 – 145. https://doi.org/10.1007/s11251-016-9372-2</bibtext> </blist> <blist> <bibtext> Tomas, L., Rigano, D., & Ritchie, S. M. (2016). Students' regulation of their emotions in a science classroom: Students' regulation of their emotions. Journal of Research in Science Teaching, 53 (2), 234 – 260. https://doi.org/10.1002/tea.21304</bibtext> </blist> <blist> <bibtext> Treagust, D. F., & Chi‐Yan, T. (2014). General instructional methods and strategies. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II). Routledge.</bibtext> </blist> <blist> <bibtext> Tytler, R. (2014). Attitudes, identity, and aspirations toward science. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 96 – 117). Routledge.</bibtext> </blist> <blist> <bibtext> Tze, V. M. C., Daniels, L. M., & Klassen, R. M. (2016). Evaluating the relationship between boredom and academic outcomes: A meta‐analysis. Educational Psychology Review, 28 (1), 119 – 144. https://doi.org/10.1007/s10648-015-9301-y</bibtext> </blist> <blist> <bibtext> Uçar, F. M., & Sungur, S. (2017). The role of perceived classroom goal structures, self‐efficacy, and engagement in student science achievement. Research in Science & Technological Education, 35 (2), 149 – 168. https://doi.org/10.1080/02635143.2017.1278684</bibtext> </blist> <blist> <bibtext> Velayutham, S., & Aldridge, J. M. (2013). Influence of psychosocial classroom environment on students' motivation and self‐regulation in science learning: A structural equation modeling approach. Research in Science Education, 43 (2), 507 – 527. https://doi.org/10.1007/s11165-011-9273-y</bibtext> </blist> <blist> <bibtext> Vogel‐Walcutt, J. J., Fiorella, L., Carper, T., & Schatz, S. (2012). The definition, assessment, and mitigation of state boredom within educational settings: A comprehensive review. Educational Psychology Review, 24 (1), 89 – 111. https://doi.org/10.1007/s10648-011-9182-7</bibtext> </blist> <blist> <bibtext> Volet, S., Seghezzi, C., & Ritchie, S. (2019). Positive emotions in student‐led collaborative science activities: Relating types and sources of emotions to engagement in learning. Studies in Higher Education, 44 (10), 1734 – 1746. https://doi.org/10.1080/03075079.2019.1665314</bibtext> </blist> <blist> <bibtext> Wigfield, A., & Eccles, J. S. (2000). Expectancy‐value theory of achievement motivation. Contemporary Educational Psychology, 25 (1), 68 – 81. https://doi.org/10.1006/ceps.1999.1015</bibtext> </blist> <blist> <bibtext> Yang, X., Zhang, M., Kong, L., Wang, Q., & Hong, J. C. (2021). The effects of scientific self‐efficacy and cognitive anxiety on science engagement with the "Question‐Observation‐Doing‐Explanation" model during school disruption in COVID‐19 pandemic. Journal of Science Education and Technology, 30 (3), 380 – 393. https://doi.org/10.1007/s10956-020-09877-x</bibtext> </blist> <blist> <bibtext> Zhao, J., Hu, S., He, H., & Chen, J. (2019). Becoming a biologist: The impact of a quasi‐apprenticeship program on Chinese secondary school students' career intention. Research in Science Education, 1 – 27. https://doi.org/10.1007/s11165-019-9832-1</bibtext> </blist> <blist> <bibtext> Zimmerman, B. J., & Schunk, D. H. (2001). Self‐regulated learning and academic achievement: Theoretical perspectives. Erlbaum.</bibtext> </blist> </ref> <aug> <p>By Pedro Membiela; Katherine Acosta; Miguel A. Yebra and Antonio González</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib76" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib74" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib85" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib75" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib26" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib49" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib81" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib87" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib94" firstref="ref9"></nolink> <nolink nlid="nl10" bibid="bib59" firstref="ref10"></nolink> <nolink nlid="nl11" bibid="bib89" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib98" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib79" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib77" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib13" firstref="ref21"></nolink> <nolink nlid="nl16" bibid="bib19" firstref="ref22"></nolink> <nolink nlid="nl17" bibid="bib20" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib55" firstref="ref25"></nolink> <nolink nlid="nl19" bibid="bib69" firstref="ref27"></nolink> <nolink nlid="nl20" bibid="bib38" firstref="ref32"></nolink> <nolink nlid="nl21" bibid="bib37" firstref="ref33"></nolink> <nolink nlid="nl22" bibid="bib32" firstref="ref34"></nolink> <nolink nlid="nl23" bibid="bib33" firstref="ref35"></nolink> <nolink nlid="nl24" bibid="bib30" firstref="ref36"></nolink> <nolink nlid="nl25" bibid="bib17" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib95" firstref="ref42"></nolink> <nolink nlid="nl27" bibid="bib14" firstref="ref43"></nolink> <nolink nlid="nl28" bibid="bib53" firstref="ref49"></nolink> <nolink nlid="nl29" bibid="bib78" firstref="ref50"></nolink> <nolink nlid="nl30" bibid="bib12" firstref="ref53"></nolink> <nolink nlid="nl31" bibid="bib54" firstref="ref56"></nolink> <nolink nlid="nl32" bibid="bib91" firstref="ref57"></nolink> <nolink nlid="nl33" bibid="bib96" firstref="ref58"></nolink> <nolink nlid="nl34" bibid="bib42" firstref="ref60"></nolink> <nolink nlid="nl35" bibid="bib27" firstref="ref62"></nolink> <nolink nlid="nl36" bibid="bib61" firstref="ref69"></nolink> <nolink nlid="nl37" bibid="bib66" firstref="ref70"></nolink> <nolink nlid="nl38" bibid="bib71" firstref="ref71"></nolink> <nolink nlid="nl39" bibid="bib93" firstref="ref72"></nolink> <nolink nlid="nl40" bibid="bib16" firstref="ref73"></nolink> <nolink nlid="nl41" bibid="bib90" firstref="ref74"></nolink> <nolink nlid="nl42" bibid="bib36" firstref="ref78"></nolink> <nolink nlid="nl43" bibid="bib35" firstref="ref80"></nolink> <nolink nlid="nl44" bibid="bib67" firstref="ref81"></nolink> <nolink nlid="nl45" bibid="bib56" firstref="ref82"></nolink> <nolink nlid="nl46" bibid="bib52" firstref="ref83"></nolink> <nolink nlid="nl47" bibid="bib34" firstref="ref84"></nolink> <nolink nlid="nl48" bibid="bib44" firstref="ref85"></nolink> <nolink nlid="nl49" bibid="bib45" firstref="ref86"></nolink> <nolink nlid="nl50" bibid="bib83" firstref="ref91"></nolink> <nolink nlid="nl51" bibid="bib84" firstref="ref92"></nolink> <nolink nlid="nl52" bibid="bib39" firstref="ref93"></nolink> <nolink nlid="nl53" bibid="bib64" firstref="ref94"></nolink> <nolink nlid="nl54" bibid="bib63" firstref="ref95"></nolink> <nolink nlid="nl55" bibid="bib51" firstref="ref103"></nolink> <nolink nlid="nl56" bibid="bib92" firstref="ref108"></nolink> <nolink nlid="nl57" bibid="bib86" firstref="ref112"></nolink> <nolink nlid="nl58" bibid="bib73" firstref="ref115"></nolink> <nolink nlid="nl59" bibid="bib11" firstref="ref131"></nolink> <nolink nlid="nl60" bibid="bib60" firstref="ref136"></nolink> <nolink nlid="nl61" bibid="bib68" firstref="ref137"></nolink> <nolink nlid="nl62" bibid="bib72" firstref="ref138"></nolink> <nolink nlid="nl63" bibid="bib82" firstref="ref139"></nolink> <nolink nlid="nl64" bibid="bib41" firstref="ref140"></nolink> <nolink nlid="nl65" bibid="bib15" firstref="ref141"></nolink> <nolink nlid="nl66" bibid="bib80" firstref="ref142"></nolink> <nolink nlid="nl67" bibid="bib21" firstref="ref145"></nolink> <nolink nlid="nl68" bibid="bib29" firstref="ref151"></nolink> <nolink nlid="nl69" bibid="bib70" firstref="ref173"></nolink> <nolink nlid="nl70" bibid="bib10" firstref="ref175"></nolink> <nolink nlid="nl71" bibid="bib47" firstref="ref177"></nolink> <nolink nlid="nl72" bibid="bib48" firstref="ref180"></nolink> <nolink nlid="nl73" bibid="bib88" firstref="ref181"></nolink> <nolink nlid="nl74" bibid="bib18" firstref="ref182"></nolink> <nolink nlid="nl75" bibid="bib97" firstref="ref184"></nolink> <nolink nlid="nl76" bibid="bib24" firstref="ref185"></nolink> <nolink nlid="nl77" bibid="bib28" firstref="ref187"></nolink> <nolink nlid="nl78" bibid="bib40" firstref="ref192"></nolink> <nolink nlid="nl79" bibid="bib43" firstref="ref195"></nolink> <nolink nlid="nl80" bibid="bib22" firstref="ref196"></nolink> <nolink nlid="nl81" bibid="bib25" firstref="ref197"></nolink> <nolink nlid="nl82" bibid="bib57" firstref="ref198"></nolink> <nolink nlid="nl83" bibid="bib58" firstref="ref199"></nolink> <nolink nlid="nl84" bibid="bib62" firstref="ref201"></nolink> <nolink nlid="nl85" bibid="bib46" firstref="ref207"></nolink> <nolink nlid="nl86" bibid="bib65" firstref="ref208"></nolink> <nolink nlid="nl87" bibid="bib50" firstref="ref209"></nolink> <nolink nlid="nl88" bibid="bib31" firstref="ref211"></nolink> <nolink nlid="nl89" bibid="bib23" firstref="ref212"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Motivation to Learn Science, Emotions in Science Classes, and Engagement towards Science Studies in Chilean and Spanish Compulsory Secondary Education Students
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Membiela%2C+Pedro%22">Membiela, Pedro</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8584-0417">0000-0002-8584-0417</externalLink>)<br /><searchLink fieldCode="AR" term="%22Acosta%2C+Katherine%22">Acosta, Katherine</searchLink><br /><searchLink fieldCode="AR" term="%22Yebra%2C+Miguel+A%2E%22">Yebra, Miguel A.</searchLink><br /><searchLink fieldCode="AR" term="%22González%2C+Antonio%22">González, Antonio</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Science+Education%22"><i>Science Education</i></searchLink>. Jul 2023 107(4):939-963.
– 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: 25
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– 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="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+Motivation%22">Learning Motivation</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+Attitudes%22">Scientific Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Attitudes%22">Student Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Psychological+Patterns%22">Psychological Patterns</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Education%22">Science Education</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Science%22">Secondary School Science</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Compulsory+Education%22">Compulsory Education</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Science+Interests%22">Science Interests</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22STEM+Careers%22">STEM Careers</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Chile%22">Chile</searchLink><br /><searchLink fieldCode="DE" term="%22Spain%22">Spain</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/sce.21793
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0036-8326<br />1098-237X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Important factors in learning science include motivational variables (relevance of science learning for personal goals, self-efficacy for learning science, and interest in a scientific career), emotional variables (boredom and enjoyment in science classes), and engagement variables (vigor, dedication, and absorption towards science studies). Data from 3034 Chilean and Spanish compulsory secondary education students was used to study the relationships between these variables, by means of a self-report questionnaire analyzed using structural equation modeling (SEM). The model, tested for goodness of fit, showed that motivational variables predict emotions in science classes and explained 43% of boredom variance and 67% of enjoyment variance. Motivational and emotional variables explained the 73% variance in engagement toward science studies. Also seen is the essential role played by emotions that mediate between motivational variables in science learning and engagement towards science studies. When promoting engagement towards science studies, these results can be used to increase relevance of science learning to personal goals, self-efficacy for learning science, and interest in a scientific career, besides reducing boredom, increasing enjoyment in science classes, and enhancing engagement towards science studies.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1379876
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1379876
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    Identifiers:
      – Type: doi
        Value: 10.1002/sce.21793
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 25
        StartPage: 939
    Subjects:
      – SubjectFull: Learning Motivation
        Type: general
      – SubjectFull: Scientific Attitudes
        Type: general
      – SubjectFull: Student Attitudes
        Type: general
      – SubjectFull: Psychological Patterns
        Type: general
      – SubjectFull: Science Education
        Type: general
      – SubjectFull: Secondary School Science
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      – SubjectFull: Secondary School Students
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      – SubjectFull: Compulsory Education
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      – SubjectFull: Foreign Countries
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      – SubjectFull: Self Efficacy
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      – SubjectFull: Science Interests
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: STEM Careers
        Type: general
      – SubjectFull: Chile
        Type: general
      – SubjectFull: Spain
        Type: general
    Titles:
      – TitleFull: Motivation to Learn Science, Emotions in Science Classes, and Engagement towards Science Studies in Chilean and Spanish Compulsory Secondary Education Students
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