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Ineffective Methods Lead to Wasted Effort: Learning Strategies Are More Important than Student Engagement in Predicting Academic Achievement

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Title: Ineffective Methods Lead to Wasted Effort: Learning Strategies Are More Important than Student Engagement in Predicting Academic Achievement
Language: English
Authors: Hu Zhiqiao, Jiang Wenyuan, Niu Duan (ORCID 0009-0006-9225-4287)
Source: Psychology in the Schools. 2025 62(10):4102-4115.
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: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Junior High Schools
Middle Schools
Secondary Education
Elementary Education
Grade 8
Descriptors: Learning Strategies, Learner Engagement, Predictor Variables, Academic Achievement, Middle School Students, Grade 8, Foreign Countries, Questionnaires, Gender Differences, Skill Development, Time Management
Geographic Terms: China
Assessment and Survey Identifiers: Motivated Strategies for Learning Questionnaire
DOI: 10.1002/pits.23600
ISSN: 0033-3085
1520-6807
Abstract: This study investigates the relationship among student engagement, learning strategies, and academic achievement. Data were collected from 4,825 eighth-grade students in Guangzhou, China, using the Delaware Student Engagement Scale (DSES) and the Motivated Strategies for Learning Questionnaire (MSLQ). Structural Equation Modeling (SEM) with mediation analysis revealed that both student engagement and learning strategies positively predict academic achievement, with learning strategies fully mediating the effect of engagement on achievement. While higher engagement generally leads to greater use of learning strategies, students with high engagement but low strategy use perform significantly worse than those with lower engagement but more effective strategy use--supporting the concept that "ineffective methods lead to wasted effort." Findings show that cognitive engagement was the lowest among the engagement factors, while organizational strategy was the lowest among learning strategy dimensions. Female students exhibited more effective strategy use than their male counterparts. Practical implications suggest that educators focus on improving students' learning strategies, especially organizational skills, to enhance academic performance and address gender differences. However, due to the study's cultural context, the results may not be generalizable across all populations, underscoring the need for further research in diverse settings.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1483615
Database: ERIC
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  Value: <anid>AN0187949522;pis01oct.25;2025Sep16.03:20;v2.2.500</anid> <title id="AN0187949522-1">Ineffective Methods Lead to Wasted Effort: Learning Strategies Are More Important Than Student Engagement in Predicting Academic Achievement </title> <p>This study investigates the relationship among student engagement, learning strategies, and academic achievement. Data were collected from 4,825 eighth‐grade students in Guangzhou, China, using the Delaware Student Engagement Scale (DSES) and the Motivated Strategies for Learning Questionnaire (MSLQ). Structural Equation Modeling (SEM) with mediation analysis revealed that both student engagement and learning strategies positively predict academic achievement, with learning strategies fully mediating the effect of engagement on achievement. While higher engagement generally leads to greater use of learning strategies, students with high engagement but low strategy use perform significantly worse than those with lower engagement but more effective strategy use—supporting the concept that "ineffective methods lead to wasted effort." Findings show that cognitive engagement was the lowest among the engagement factors, while organizational strategy was the lowest among learning strategy dimensions. Female students exhibited more effective strategy use than their male counterparts. Practical implications suggest that educators focus on improving students' learning strategies, especially organizational skills, to enhance academic performance and address gender differences. However, due to the study's cultural context, the results may not be generalizable across all populations, underscoring the need for further research in diverse settings.</p> <p>Summary: Learning strategy fully mediates the effect of student engagement on academic performance, as evidenced by an analysis of 4,825 eighth‐grade students.Students who exhibit high engagement but employ ineffective learning strategies perform significantly worse than those with lower engagement but more effective strategy use.Cognitive engagement and organizational strategy are the lowest among their respective dimensions in eighth‐grade students, suggesting these areas should be prioritized for improvement.</p> <p>Keywords: academic achievement; eighth‐grade students; learning strategies; mediation analysis; student engagement</p> <hd id="AN0187949522-2">Introduction</hd> <p>Academic achievement, defined as proficiency in general or specific academic skills (e.g., arithmetic, reading), is a critical outcome variable in educational psychology. It is associated with stronger psychological resilience and mental health (Bandura [<reflink idref="bib4" id="ref1">4</reflink>]) and significantly predicts career choice, income level, and job satisfaction (OECD [<reflink idref="bib29" id="ref2">29</reflink>]). High academic achievement also facilitates entry into high‐skilled industries and social mobility (Heckman and Rubinstein [<reflink idref="bib22" id="ref3">22</reflink>]). Historically, academic achievement was considered to be largely determined by genetic factors. In contrast, behaviorist perspectives emphasized the influence of educational quality and environmental conditions. More recently, the concept of self‐regulated learning has gained prominence as a key determinant of academic success. This perspective underscores the active role of learners in managing and directing their own learning processes (Zimmerman [<reflink idref="bib48" id="ref4">48</reflink>]). Students' use of self‐regulated learning strategies (e.g., goal‐setting, organizing, seeking assistance) was found to be the strongest predictor of academic achievement, surpassing demographic factors like gender and socioeconomic status (Zimmerman and Pons [<reflink idref="bib51" id="ref5">51</reflink>]). The process of self‐regulated learning is characterized by the interaction of two critical variables: student engagement and learning strategies, both of which have been shown to significantly influence academic achievement (Lee and Shute [<reflink idref="bib25" id="ref6">25</reflink>]; Marzano and Kendall [<reflink idref="bib28" id="ref7">28</reflink>]).</p> <p>Student engagement is a multidimensional construct that encompasses students' investment of time and energy in various aspects of school education (Appleton et al. [<reflink idref="bib1" id="ref8">1</reflink>]; Fredricks et al. [<reflink idref="bib18" id="ref9">18</reflink>]). Unlike learning motivation, which focuses primarily on cognitive aspects, student engagement also includes behavioral and affective dimensions (Fredricks et al. [<reflink idref="bib18" id="ref10">18</reflink>]). The three‐factor model of student engagement—behavioral, cognitive, and emotional—is particularly relevant to K‐12 education (Lee and Shute [<reflink idref="bib25" id="ref11">25</reflink>]). Behavioral engagement entails active participation in learning and compliance with classroom norms (Finn [<reflink idref="bib15" id="ref12">15</reflink>]; Finn et al. [<reflink idref="bib16" id="ref13">16</reflink>]). Emotional engagement reflects students' affective responses and sense of belonging within the school context (Connell and Wellborn [<reflink idref="bib11" id="ref14">11</reflink>]; Finn [<reflink idref="bib15" id="ref15">15</reflink>]). Cognitive engagement involves psychological investment and sustained effort to master academic content. Empirical evidence consistently links student engagement to academic achievement. Lei et al. ([<reflink idref="bib26" id="ref16">26</reflink>]), in a meta‐analysis of 69 studies, found a moderate positive correlation between engagement and academic performance. Similarly, Wong et al. ([<reflink idref="bib45" id="ref17">45</reflink>]) confirmed that behavioral, emotional, and cognitive engagement are all moderately and positively associated with achievement outcomes.</p> <p>Learning strategies are mental or behavioral methods used to enhance learning, such as forming mental images, organizing information, identifying associations, or practicing retrieval. Multiple frameworks have been developed to classify these strategies. Weinstein and Mayer ([<reflink idref="bib44" id="ref18">44</reflink>]) identified five types: rehearsal, elaboration, organizational, comprehension monitoring, and affective strategies. Biggs ([<reflink idref="bib7" id="ref19">7</reflink>]), in contrast, categorized strategies as surface, deep, or achieving based on motivational orientation within his Student Approaches to Learning (SAL) model. Pintrich and De Groot ([<reflink idref="bib33" id="ref20">33</reflink>]) proposed a three‐component model—cognitive, metacognitive, and resource management strategies—which aligns closely with self‐regulated learning (SRL) theory and is adopted in this study. Cognitive strategies support memorization and understanding, with advanced techniques like summarizing, mapping, and self‐explanation promoting deeper comprehension (Fiorella and Mayer [<reflink idref="bib17" id="ref21">17</reflink>]). Metacognitive strategies involve goal‐setting, monitoring, and regulating learning to optimize cognitive strategy use (Corno [<reflink idref="bib12" id="ref22">12</reflink>]). Resource management strategies include time management, effort regulation, and environmental control (Pintrich [<reflink idref="bib32" id="ref23">32</reflink>]). Empirical research demonstrates that learning strategies significantly enhance academic achievement. Hattie et al. ([<reflink idref="bib20" id="ref24">20</reflink>]) found that students taught learning strategies outperformed those who were not, and Zimmerman and Pons ([<reflink idref="bib51" id="ref25">51</reflink>]) reported that self‐regulated learning strategies accounted for 36%–41% of the variance in test scores.</p> <hd id="AN0187949522-3">The Current Status and Gender Differences of Student Engagement and Learning Strategies Among...</hd> <p>This study focuses on junior high school students—a developmental stage widely recognized as both academically and psychosocially challenging (Cleary and Chen [<reflink idref="bib9" id="ref26">9</reflink>]). During this period, students undergo significant personal development, including identity formation, while navigating academic transitions, increased workload, and greater exposure to high‐stakes assessments. Given that student engagement and learning strategies are well‐established predictors of academic achievement, examining these factors at this pivotal stage can inform targeted interventions that address the unique academic, social, and emotional needs of early adolescents.</p> <p>Previous research on junior high school students in China has often been limited in scope, typically focusing on a small number of schools. For example, Jin ([<reflink idref="bib24" id="ref27">24</reflink>]) examined 404 students from mainland China and found that overall student engagement was moderate, with the highest level observed in behavioral engagement, followed by cognitive engagement, and the lowest in emotional engagement (based on the scale midpoint). Similarly, Tsa and Tzou ([<reflink idref="bib40" id="ref28">40</reflink>]) investigated 119 seventh and ninth graders in Taiwan, reporting that junior high school students struggled with time management and help‐seeking strategies.</p> <p>This study also examines gender differences in student engagement and learning strategies, as gender significantly influences academic performance. Previous research has highlighted gender disparities in academic achievement, with female junior high school students outperforming males in overall achievement, language, mathematics, and science (Voyer and Voyer [<reflink idref="bib41" id="ref29">41</reflink>]). However, the mechanisms behind these differences are not well understood. Given that student engagement and learning strategies are key determinants of academic outcomes, it is likely that gender differences in these areas contribute to the observed disparities in academic performance.</p> <hd id="AN0187949522-4">The Mechanisms by Which Student Engagement and Learning Strategy Influence Academic Achieveme...</hd> <p>Prior research has explored the relationships among student engagement, learning strategies, and academic achievement. High student engagement significantly predicts the use of deep learning strategies (S. Floyd et al. [<reflink idref="bib38" id="ref30">38</reflink>]), and both engagement and learning strategies are significant predictors of academic achievement (Lee and Shute [<reflink idref="bib25" id="ref31">25</reflink>]). However, few studies have specifically analyzed the mediating processes through which these two factors influence academic achievement.</p> <p>There is theoretical support for the mediating role of learning strategies in the relationship between student engagement and academic achievement. Marzano and Kendall's ([<reflink idref="bib28" id="ref32">28</reflink>]) model posits that task performance involves a sequential activation of self‐system (student engagement), metacognitive system (learning strategies), and cognitive system (information processing), implying that student engagement may not directly influence academic achievement but operates through learning strategies. Similarly, Zimmerman ([<reflink idref="bib50" id="ref33">50</reflink>]) model emphasizes that effective learning strategies—such as task strategies, help‐seeking, and metacognitive monitoring—require initiative and persistence, core components of student engagement. Proactive learners, who are motivated and strategic, tend to achieve higher academic performance, whereas reactive learners, lacking clear strategies, tend to perform less effectively (Zimmerman [<reflink idref="bib49" id="ref34">49</reflink>]). These models collectively suggest that learning strategies may mediate the effect of student engagement on academic outcomes.</p> <p>Numerous studies have confirmed the mediating role of learning strategies in the relationship between various motivational factors and academic achievement. Full mediation has been found for mastery goals, performance‐approach goals (Diseth and Kobbeltvedt [<reflink idref="bib14" id="ref35">14</reflink>]; Greene et al. [<reflink idref="bib19" id="ref36">19</reflink>]), perceived value (Greene et al. [<reflink idref="bib19" id="ref37">19</reflink>]; Pokay and Blumenfeld [<reflink idref="bib37" id="ref38">37</reflink>]), and persistence (Zhang and Zhang [<reflink idref="bib47" id="ref39">47</reflink>]), while partial mediation has been reported for performance‐avoidance goals (Diseth and Kobbeltvedt [<reflink idref="bib14" id="ref40">14</reflink>]) and self‐efficacy (Greene et al. [<reflink idref="bib19" id="ref41">19</reflink>]; Hu and Xu [<reflink idref="bib23" id="ref42">23</reflink>]; Wang and Liu [<reflink idref="bib43" id="ref43">43</reflink>]; Zhang and Zhang [<reflink idref="bib47" id="ref44">47</reflink>]). However, these studies have not addressed the broader construct of motivation encompassed by student engagement. As previously discussed, student engagement includes behavioral, emotional, and cognitive components—the latter of which aligns with traditional conceptions of motivation (Lee and Shute [<reflink idref="bib25" id="ref45">25</reflink>]). Moreover, student engagement reflects not only motivational states but also their behavioral and emotional outcomes, making it more directly linked to the use of learning strategies (Marzano [<reflink idref="bib27" id="ref46">27</reflink>]). The proposed conceptual model is presented in Figure 1 (Model 1).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23600-fig-0001.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23600-fig-0001.jpg" title="1 Conceptual models of the relationships among student engagement, learning strategy, and academic achievement." /> </p> <p></p> <p>Other studies have identified student engagement as a mediator between learning strategies and academic achievement. For instance, Büchele ([<reflink idref="bib8" id="ref47">8</reflink>]) found that the regularity and persistence of learning fully mediated the effect of students' strategy use on mathematics performance. This pathway is plausible, as the effective use of learning strategies may enhance self‐efficacy, which in turn fosters greater engagement and ultimately leads to improved academic outcomes. Therefore, this study also examines the potential mediating role of student engagement in the relationship between learning strategies and academic achievement. The proposed conceptual model is presented in Figure 1 (Model 2).</p> <p>Previous research has primarily focused on specific motivational factors, with limited attention to student engagement as a broader construct. The relationships among student engagement, learning strategies, and academic achievement require further investigation, as both engagement and strategies may serve as mediators. Most studies have focused on college or high school students, highlighting the need for research on junior high school populations. Additionally, the reliance on small‐sample studies emphasizes the importance of large‐scale research for more generalizable findings.</p> <p>In summary, this study examines junior high school students and addresses the following research questions: (<reflink idref="bib1" id="ref48">1</reflink>) What are the levels of student engagement and learning strategies among Chinese junior high school students, and are there gender differences in these variables? (<reflink idref="bib2" id="ref49">2</reflink>) Do learning strategies mediate the relationship between student engagement and academic achievement, or does student engagement mediate the impact of learning strategies on academic achievement?</p> <hd id="AN0187949522-6">Methods</hd> <p></p> <hd id="AN0187949522-7">Participants</hd> <p>This study surveyed all eighth‐grade students from 28 junior high schools in a district of Guangzhou, China, excluding those with identified learning disabilities. The schools, all emphasizing academic instruction, were geographically distributed as follows: 19 urban, seven town (administrative units between urban and rural areas), and four rural schools. During data cleaning, 2,877 responses were excluded due to inattentive answering (e.g., identical option selections across subscales), missing gender or academic achievement data, or excessive missing responses. For cases with fewer than five missing items, the series mean method was used for imputation. After data cleaning, a total of 4,825 valid responses were retained, consisting of 2,453 boys (50.84%) and 2,372 girls (49.16%). To validate the internal structures of the engagement and strategy scales, the sample was divided into two sub‐samples based on student IDs, a unique 10‐digit identifier. Sub‐sample A (<emph>N</emph><subs><emph>1</emph></subs> = 2,426), with odd‐numbered IDs, was used for exploratory factor analysis (EFA), and sub‐sample B (<emph>N</emph><subs><emph>2</emph></subs> = 2,399), with even‐numbered IDs, was used for confirmatory factor analysis (CFA). Group comparisons revealed statistically significant differences between included and excluded participants in gender (<emph>χ</emph>²(<reflink idref="bib1" id="ref50">1</reflink>) = 37.79, <emph>p</emph> < 0.001, Cramér's V = 0.071) and school area type (<emph>χ</emph>²(<reflink idref="bib2" id="ref51">2</reflink>) = 10.22, <emph>p</emph> = 0.006, Cramér's V = 0.036); however, both effect sizes were small (V < 0.10), indicating limited practical significance. Academic achievement also differed significantly between groups, with the included group performing better (<emph>t</emph>(<reflink idref="bib7" id="ref52">7</reflink>,<reflink idref="bib632" id="ref53">632</reflink>) = 11.77, <emph>p</emph> < 0.001), though the effect size was small (Cohen's <emph>d</emph> = 0.28).</p> <hd id="AN0187949522-8">Instruments</hd> <p></p> <hd id="AN0187949522-9">Delaware Student Engagement Scale (DSES)</hd> <p>We used the Delaware Student Engagement Scale (DSES; Bear et al. [<reflink idref="bib5" id="ref54">5</reflink>]) to assess student engagement. The DSES was selected because it is specifically designed for basic education and aligns with the three‐factor model of student engagement. Comprising 12 items, the scale measures cognitive engagement (e.g., "I try my best in school"), behavioral engagement (e.g., "I follow the rules at school"), and emotional engagement (e.g., "I feel happy in school"), with four items per dimension. The scale uses a 4‐point Likert scale, where higher scores indicate greater engagement. All items were translated into Chinese using mutual translation and expert review to ensure accuracy and cultural relevance.</p> <p>We used the grades 6–12 version of the DSES, which includes an item in the cognitive engagement dimension: "I have plans for more school or training after high school." This item is more relevant to high school students. However, the grades 3–5 version contains a more suitable cognitive engagement item for eighth graders: "When I make a mistake, I try to fix it." Therefore, we replaced the high school‐specific item with the one from the grades 3–5 version to better align with the target population.</p> <p>An exploratory factor analysis (EFA) was conducted on sub‐sample A (<emph>N</emph><subs><emph>1</emph></subs> = 2426) using principal axis factoring (PAF) extraction and Promax rotation. Three factors—emotional engagement, cognitive engagement, and behavioral engagement—explained 51.60% of the total variance, with Cronbach's alpha coefficients of 0.82, 0.79, and 0.77, respectively. The EFA revealed differences in factor‐item relationships compared to the original scale. Specifically, two items—"I pay attention in class" and "When I don't do well, I work harder"—were reclassified from behavioral to cognitive engagement, while "When I make a mistake, I try to fix it" and "I turn in my homework on time" were reassigned from cognitive to behavioral engagement. Interviews with six teachers and eight students suggested that these discrepancies may reflect cultural differences (see supporting Theme 1).</p> <p>A confirmatory factor analysis (CFA) was conducted on sub‐sample B (<emph>N</emph><subs><emph>2</emph></subs> = 2399) to validate the three‐factor model. The results showed a good model fit: <emph>χ</emph>² = 729.440, <emph>df</emph> = 51, RMSEA = 0.074, CFI = 0.941, TLI = 0.924, SRMR = 0.047. The composite reliability values for emotional, behavioral, and cognitive engagement were 0.84, 0.78, and 0.81, respectively, with average variance extracted (AVE) values of 56.30%, 47.39%, and 52.12%, indicating satisfactory construct validity. The second‐order model, which produced equivalent fit indices and aligned with the overall construct of student engagement, was selected as the final model. The composite reliability for the second‐order factor was 0.81, with an AVE of 59.28%. For further details, see supplementary Table S1 and Table S2.</p> <hd id="AN0187949522-10">Motivated Strategies for Learning Questionnaire (MSLQ)</hd> <p>We used the learning strategies subscales of the motivated strategies for learning questionnaire (MSLQ; Pintrich et al. [<reflink idref="bib35" id="ref55">35</reflink>]) to assess learning strategies. The original scale includes nine subscales and 50 items. To minimize student burden and accommodate time constraints, we selected 20 items from eight subscales with high factor loadings. The critical thinking subscale was excluded due to low factor loadings of its items. The scale used a 6‐point Likert scale, where higher scores indicate better strategy use. All items were translated into Chinese through mutual translation and expert review.</p> <p>To validate the scale's internal structure among Chinese junior high school students, an exploratory factor analysis (EFA) was performed on sub‐sample A (<emph>N</emph><subs><emph>1</emph></subs> = 2426). The analysis revealed five factors: organization, peer learning and help seeking, metacognitive self‐regulation, rehearsal and elaboration, and effort and time management, accounting for 49.41% of the total variance. Cronbach's alpha coefficients were 0.80, 0.81, 0.78, 0.82, and 0.66, respectively, indicating good internal consistency for all factors except effort and time management. Unlike the original MSLQ, factors of rehearsal and elaboration, effort and time management, and peer learning and help seeking were combined into single factors. Additionally, two items "I usually study in a place where I can concentrate on my course work." and "Even when course materials are dull and uninteresting, I manage to keep working until I finish." were reclassified under metacognitive self‐regulation, consistent with Pintrich and De Groot ([<reflink idref="bib33" id="ref56">33</reflink>]). Interviews with teachers and students clarified these factor‐item relationships (see supporting Theme 2).</p> <p>A confirmatory factor analysis (CFA) was conducted on sub‐sample B (<emph>N</emph><subs><emph>2</emph></subs> = 2,399). The model showed good fit: <emph>χ</emph>² = 1175.770, df = 160, RMSEA = 0.051, CFI = 0.950, TLI = 0.940, SRMR = 0.036. Composite reliabilities for the five first‐order factors—organization, peer learning and help seeking, metacognitive self‐regulation, rehearsal and elaboration, and effort and time management—were 0.83, 0.83, 0.80, 0.82, and 0.64, respectively. Average variance extracted (AVE) values were 61.74%, 55.21%, 40.36%, 52.96%, and 37.65%, indicating adequate construct validity. The second‐order factor model also fit well: <emph>χ</emph>² = 1257.517, df = 165, RMSEA = 0.053, CFI = 0.946, TLI = 0.938, SRMR = 0.039. Given the comparable fit and the overarching construct of learning strategy, the second‐order model was retained as the final model, with a composite reliability of 0.91 and AVE of 67.69%. For further details, see supplementary Table S3 and S4.</p> <hd id="AN0187949522-11">Academic Achievement Tests</hd> <p>Academic achievement was measured using district‐wide standardized tests in six subjects: Chinese (0–120), Mathematics (0–120), English (0–90), Physics (0–90), History (0–90), and Morality and Law (0–90). An exploratory factor analysis (EFA) on the raw scores yielded a single factor—academic achievement—which accounted for 74.94% of the variance. The measure demonstrated excellent internal consistency, with a Cronbach's alpha of 0.93. For further details, see supplementary Table S5.</p> <hd id="AN0187949522-12">Procedure</hd> <p>The District Institute of Educational Development in Guangzhou administered both the academic achievement tests and the questionnaire surveys, and was also responsible for grading the test results. Following informed consent from students' guardians, teachers distributed the questionnaires immediately after the final subject‐specific achievement test. Students were informed that all data collected would remain strictly confidential and be used solely for scientific research. They were instructed to complete the questionnaires carefully, independently, and truthfully. Upon completion, teachers collected the questionnaires on‐site and forwarded them for machine scanning.</p> <hd id="AN0187949522-13">Statistical Analysis Plan</hd> <p>This study employed a comprehensive data analysis approach to examine the relationships among student engagement, learning strategies, and academic achievement. First, we validated the instrument structures, as described earlier. To ensure data quality, Harman's single‐factor test (Podsakoff and Organ [<reflink idref="bib36" id="ref57">36</reflink>]) was then applied to assess common method bias. Descriptive statistics were used to characterize student engagement and learning strategies, with gender differences considered. Next, Pearson correlation analysis was conducted to explore the relationships among the three variables. Structural equation modeling (SEM) with mediation was then employed to further explore these relationships, with student engagement and learning strategy modeled as separate mediators. Participants were subsequently grouped by their engagement and strategy levels, and analysis of variance (ANOVA) was used to test group differences. Additionally, a planned contrast analysis was conducted to compare the "high engagement–low strategy" and "low engagement–high strategy" groups. SEM and factor analysis were conducted using Mplus 8.3, while all other analyses were performed using SPSS 26.0.</p> <hd id="AN0187949522-14">Results</hd> <p></p> <hd id="AN0187949522-15">Common Method Bias Test</hd> <p>Harman's single‐factor test was conducted to assess potential common method bias. An unrotated exploratory factor analysis of all items from the student engagement and learning strategy scales yielded seven factors with eigenvalues greater than 1. The first factor accounted for 31.21% of the total variance, which falls below the commonly accepted threshold of 40%, suggesting that common method bias is unlikely to pose a significant threat in this study.</p> <hd id="AN0187949522-16">Current Status of Student Engagement and Learning Strategies</hd> <p></p> <hd id="AN0187949522-17">Overall Status</hd> <p>We used SPSS 26.0 to analyze descriptive statistics for student engagement, learning strategies, and academic achievement. To improve interpretability, Likert‐scale scores were converted to a 0–100 scale using the formula 100 × (M − 1)/(N − 1), where <emph>M</emph> is the mean score and <emph>N</emph> is the highest Likert scale value (Peterson et al. [<reflink idref="bib31" id="ref58">31</reflink>]). Single‐factor repeated measures ANOVAs were conducted for student engagement and learning strategies to compare levels within each dimension. A 60‐point threshold (equivalent to 2.8 on a 4‐point scale or 4 on a 6‐point scale) served as a benchmark for meeting general expectations. Table 1 reports the overall means, standard deviations, and post hoc comparison results. Partial eta squared (η²) was used to assess effect sizes: > 0.14 = large, > 0.06 = medium, > 0.01 = small (Cohen [<reflink idref="bib10" id="ref59">10</reflink>]).</p> <p>1 Table Descriptive statistics of student engagement and learning strategies among eighth‐grade students.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Factor</th><th>Likert scale</th><th align="center">Percentage scale</th><th>Multiple comparison</th></tr><tr valign="bottom"><th align="center">M</th><th>SD</th><th>M</th><th>SD</th></tr></thead><tbody valign="top"><tr><td>Emotional engagement (1)</td><td>3.06</td><td>.58</td><td>68.80</td><td>19.32</td><td>2 > 1 > 3</td></tr><tr><td>Behavioral engagement (2)</td><td>3.40</td><td>.46</td><td>80.15</td><td>15.43</td></tr><tr><td>Cognitive engagement (3)</td><td>3.00</td><td>.49</td><td>66.56</td><td>16.21</td></tr><tr><td>Student engagement</td><td>3.16</td><td>.41</td><td>71.84</td><td>13.60</td></tr><tr><td>Organization (4)</td><td>3.95</td><td>1.13</td><td>59.01</td><td>22.55</td><td>(5 = 6 = 8) > 7 > 4</td></tr><tr><td>Metacognitive self‐regulation (5)</td><td>4.44</td><td>.82</td><td>68.81</td><td>16.39</td></tr><tr><td>Peer learning & help seeking (6)</td><td>4.42</td><td>1.01</td><td>68.38</td><td>20.20</td></tr><tr><td>Effort & time management (7)</td><td>4.29</td><td>1.01</td><td>65.89</td><td>20.20</td></tr><tr><td>Rehearsal & elaboration (8)</td><td>4.45</td><td>.89</td><td>69.05</td><td>17.81</td></tr><tr><td>Overall learning strategy</td><td>4.31</td><td>.75</td><td>66.23</td><td>15.05</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note:</emph> A 4‐point Likert scale was used for student engagement and its dimensions, while a 6‐point Likert scale was applied to learning strategies and their dimensions.</p> <p>Table 1 indicates that eighth‐grade students exhibited moderate overall student engagement (<emph>M</emph> = 71.84). A repeated‐measures ANOVA revealed significant differences among the three dimensions of student engagement, <emph>F</emph>(<reflink idref="bib2" id="ref60">2</reflink>, 9648) = 1603.86, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0001" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.250. Behavioral engagement scored the highest (<emph>M</emph> = 80.15), followed by emotional engagement (<emph>M</emph> = 68.80), and cognitive engagement, the lowest <emph>(M</emph> = 66.56).</p> <p>For learning strategies, students showed moderate overall performance (<emph>M</emph> = 66.23). Significant differences were found among the dimensions, <emph>F</emph>(<reflink idref="bib2" id="ref61">2</reflink>, 9648) = 442.85, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0002" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.084. Rehearsal and elaboration (<emph>M</emph> = 69.05), metacognitive self‐regulation (<emph>M</emph> = 68.81), and peer learning and help‐seeking (<emph>M</emph> = 68.38) were the highest‐performing factors, with no significant differences among them. Effort and time management (<emph>M</emph> = 65.89) followed, and organization had the lowest score (<emph>M</emph> = 59.01).</p> <p>Item‐level repeated measures ANOVAs were conducted for cognitive engagement and organizational strategy to identify differences among specific items. For cognitive engagement, the main effect was significant, <emph>F</emph>(<reflink idref="bib3" id="ref62">3</reflink>, 14472) = 865.67, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0003" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.152, with students scoring significantly lower on "I get good grades in school" than on other items. For organizational strategy, the main effect was also significant, <emph>F</emph>(<reflink idref="bib2" id="ref63">2</reflink>, 9468) = 531.35, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0004" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.099. Students scored lower on "I make simple charts to help me organize course material" and "When I study the readings for this course, I outline the material" compared to "When I study for this course, I write brief summaries of the main ideas."</p> <hd id="AN0187949522-18">Gender Differences</hd> <p>Gender differences in student engagement and learning strategies were examined using mixed‐design ANOVAs, with gender as a between‐subjects factor and student engagement and learning strategies as within‐subjects factors. The results, including means and standard deviations for each factor across genders, are summarized in Table 2.</p> <p>2 Table Gender differences in student engagement and learning strategies.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Factor</th><th>Boys</th><th>Girls</th><th>Comparison</th></tr><tr valign="bottom"><th>M (SD)</th><th>M (SD)</th></tr></thead><tbody valign="top"><tr><td>Emotional engagement</td><td>71.19 (19.32)</td><td>66.33 (19.03)</td><td>Boys > Girls</td></tr><tr><td>Behavioral engagement</td><td>77.48 (15.74)</td><td>82.91 (14.59)</td><td>Girls > Boys</td></tr><tr><td>Cognitive engagement</td><td>66.35 (17.12)</td><td>66.77 (15.22)</td><td>Boys = Girls</td></tr><tr><td>Student engagement</td><td>71.67 (14.02)</td><td>72.00 (13.16)</td><td>Boys = Girls</td></tr><tr><td>Organization</td><td>67.55 (18.97)</td><td>70.61 (16.38)</td><td>Girls > Boys</td></tr><tr><td>Metacognitive self‐regulation</td><td>67.49 (17.27)</td><td>70.17 (15.31)</td><td>Girls > Boys</td></tr><tr><td>Peer learning & help seeking</td><td>67.19 (20.78)</td><td>69.61 (19.51)</td><td>Girls > Boys</td></tr><tr><td>Effort & time management</td><td>65.80 (20.72)</td><td>65.98 (19.66)</td><td>Boys = Girls</td></tr><tr><td>Rehearsal & elaboration</td><td>56.44 (23.59)</td><td>61.68 (21.10)</td><td>Girls > Boys</td></tr><tr><td>Overall learning strategy</td><td>64.89 (15.62)</td><td>67.61 (14.31)</td><td>Girls > Boys</td></tr></tbody></table> </ephtml> </p> <p>The mixed ANOVA on gender differences in student engagement revealed no significant main effect of gender, <emph>F</emph>(<reflink idref="bib1" id="ref64">1</reflink>, 4823) = 0.71, <emph>p</emph> = 0.399. However, a significant main effect of engagement dimension was observed, <emph>F</emph>(<reflink idref="bib2" id="ref65">2</reflink>, 9646) = 1687.83, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0005" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.259, as well as a significant interaction between gender and engagement dimension, <emph>F</emph>(<reflink idref="bib2" id="ref66">2</reflink>, 9646) = 208.92, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0006" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.042. Simple effects analysis indicated that boys scored higher in emotional engagement, while girls scored higher in behavioral engagement. No significant gender differences were found in cognitive engagement or overall student engagement.</p> <p>The mixed ANOVA on gender differences in learning strategies revealed significant main effects of gender, <emph>F</emph>(<reflink idref="bib1" id="ref67">1</reflink>, 4823) = 39.54, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0007" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.008, and learning strategy dimension, <emph>F</emph>(<reflink idref="bib4" id="ref68">4</reflink>, 19292) = 442.72, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0008" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.084, as well as a significant interaction between these variables, <emph>F</emph>(<reflink idref="bib4" id="ref69">4</reflink>, 19292) = 20.22, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0009" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.183. Simple effects analysis indicated that girls scored significantly higher than boys in all dimensions of learning strategies except effort and time management.</p> <hd id="AN0187949522-19">Correlations Among Student Engagement, Learning Strategies, and Academic Achievement</hd> <p>A correlation analysis examined the relationships among student engagement, learning strategies, and academic achievement, with results shown in Table 3. All 55 correlations among the 11 variables were positive (<emph>r</emph> = 0.10–0.85) and statistically significant at <emph>p</emph> < 0.01. Student engagement and overall learning strategy were highly correlated (<emph>r</emph> = 0.66), with both positively correlated with academic achievement. Overall learning strategy showed a stronger correlation with academic achievement (<emph>r</emph> = 0.38) than student engagement (<emph>r</emph> = 0.27). Cognitive engagement showed the strongest correlation with academic achievement among engagement dimensions (<emph>r</emph> = 0.37), while peer learning and help‐seeking was highest among learning strategies (<emph>r</emph> = 0.40).</p> <p>3 Table Correlations among student engagement, learning strategies, and academic achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th>Factor</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th></tr></thead><tbody valign="top"><tr><td>1. Emotional engagement</td><td>—</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>2. Behavioral engagement</td><td>.39</td><td>—</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>3. Cognitive engagement</td><td>.46</td><td>.54</td><td>—</td><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>4. Student engagement</td><td>.80</td><td>.78</td><td>.82</td><td>—</td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>5. Organization</td><td>.30</td><td>.36</td><td>.49</td><td>.47</td><td>—</td><td /><td /><td /><td /><td /><td /></tr><tr><td>6. Metacognitive self‐regulation</td><td>.37</td><td>.50</td><td>.63</td><td>.61</td><td>.59</td><td>—</td><td /><td /><td /><td /><td /></tr><tr><td>7. Peer learning & help seeking</td><td>.34</td><td>.37</td><td>.54</td><td>.52</td><td>.48</td><td>.66</td><td>—</td><td /><td /><td /><td /></tr><tr><td>8. Effort & time management</td><td>.28</td><td>.30</td><td>.43</td><td>.41</td><td>.28</td><td>.40</td><td>.37</td><td>—</td><td /><td /><td /></tr><tr><td>9. Rehearsal & elaboration</td><td>.33</td><td>.46</td><td>.62</td><td>.57</td><td>.63</td><td>.70</td><td>.59</td><td>.40</td><td>—</td><td /><td /></tr><tr><td>10. Learning strategy</td><td>.42</td><td>.50</td><td>.69</td><td>.66</td><td>.78</td><td>.85</td><td>.80</td><td>.63</td><td>.84</td><td>—</td><td /></tr><tr><td>11. Academic achievement</td><td>.10</td><td>.20</td><td>.37</td><td>.27</td><td>.11</td><td>.34</td><td>.40</td><td>.29</td><td>.36</td><td>.38</td><td>—</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note:</emph> All coefficients are significant at <emph>p</emph> < 0.01 (two‐tails).</p> <hd id="AN0187949522-20">Testing the Mediating Role of Learning Strategy Versus Student Engagement in Predicting Acade...</hd> <p>Mplus 8.3 was used to examine the mediating role of learning strategy in the effect of student engagement on academic achievement (Model 1). Student engagement was modeled as a second‐order factor with emotional, behavioral, and cognitive engagement as first‐order factors. Learning strategy was also modeled as a second‐order factor, with organization, peer learning and help‐seeking, metacognitive self‐regulation, rehearsal and elaboration, and effort and time management as first‐order factors. Academic achievement was modeled as a first‐order factor with grades in six subjects as observed indicators. Gender was included as a covariate to control for its potential influence and to explore gender differences in academic achievement. Model fit indices were <emph>χ</emph>² = 10073.459, <emph>df</emph> = 691, RMSEA = 0.053, CFI = 0.901, TLI = 0.897, and SRMR = 0.053, indicating acceptable model fit.</p> <p>The results in Figure 2 show that learning strategy fully mediated the relationship between student engagement and academic achievement. The indirect effect ("student engagement → learning strategy → academic achievement") was significant (<emph>β</emph> = 0.333, <emph>p</emph> < 0.001, 95% CI = [.267, 0.399]), while the direct effect of student engagement on academic achievement was not significant (<emph>β</emph> = 0.036, <emph>p</emph> = 0.375, 95% CI = [−.043, 0.115]), indicating full mediation. Gender (1 = girls, 0 = boys) significantly influenced academic achievement, with girls outperforming boys (<emph>β</emph> = 0.091, <emph>p</emph> < 0.001). Together, student engagement, learning strategy, and gender explained 18.7% of the variance in academic achievement. The path coefficient from student engagement to learning strategy was 0.85 (<emph>p</emph> < 0.001), suggesting higher engagement is associated with higher learning strategy levels.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23600-fig-0002.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23600-fig-0002.jpg" title="2 Standardized coefficients for student engagement affecting academic achievement: learning strategy as the mediator (Model 1). Note: AA = academic achievement,CE = cognitive engagement, CHI = Chinese, BE = behavioral engagement, EE = emotional engagement, ENG = English, E&T = effort and time management, GEN = gender (1 = girls, 0 = boys), HIS = history, LS = learning strategies, MAT = mathematics, MR = metacognitive self‐regulation, M&L = morality and law, O = organization, P&H = peer learning and help seeking, PHY = physics, R&E = rehearsal and elaboration, SE = student engagement." /> </p> <p></p> <p>We also tested an alternative model (Model 2) in which student engagement served as the mediating variable. Results showed that the direct effect of learning strategy on academic achievement was significant (<emph>β</emph> = 0.392, <emph>p</emph> < 0.001, 95% CI [0.316, 0.468]), whereas the indirect effect through student engagement was not significant (<emph>β</emph> = 0.030, <emph>p</emph> = 0.375, 95% CI [–0.037, 0.097]). These findings further support the conclusion that learning strategy fully mediates the relationship between student engagement and academic achievement, rather than the reverse.</p> <hd id="AN0187949522-22">Testing the Robustness of the Mediation Model (Model 1)</hd> <p>Given the full mediating role of learning strategy in the effect of student engagement on academic achievement, it is hypothesized that engagement enhances achievement only when it leads to more effective strategy use. This supports the "Ineffective Methods Lead to Wasted Effort" hypothesis. Therefore, the impact of varying learning strategy levels at a fixed engagement level should be greater than that of varying engagement at a fixed strategy level. In extreme cases, students with high engagement but low strategy use may perform worse than those with low engagement but high strategy use.</p> <p>To further validate the results, participants were grouped based on their student engagement and learning strategy levels, determined by total scores on respective scales. To avoid imbalance due to identical student engagement scores, the 30% percentile criterion (Beuchert and Mendoza [<reflink idref="bib6" id="ref70">6</reflink>]) was used, dividing participants into three groups: high (top 30%), medium (middle 40%), and low (bottom 30%). High and low classifications for student engagement were set at scores above 77.78 and below 66.67, respectively, while high and low classifications for learning strategy were set at scores above 74.67 and below 58.67. This resulted in nine distinct combinations of student engagement and learning strategy levels. Academic achievement was measured by the total score across six subjects.</p> <p>Table 4 and Figure 3 present the descriptive statistics for the nine student groups. A two‐factor ANOVA was conducted to assess group differences in academic achievement and the effect sizes of student engagement (SE) and learning strategy (LS). The majority of students (59.7%) showed consistency in SE and LS levels, with 18.7% in the high SE‐high LS group, 23% in the medium SE‐medium LS group, and 18% in the low SE‐low LS group. Inconsistent cases included 1.9% in the high SE‐low LS group and 1.7% in the low SE‐high LS group. A planned contrast compared these inconsistent groups to test the "Ineffective Methods Lead to Wasted Effort" hypothesis. Cohen's <emph>d</emph> was used to measure effect size, with values > 0.8 indicating a large effect, > 0.5 a medium effect, and > 0.2 a small effect (Cohen [<reflink idref="bib10" id="ref71">10</reflink>]).</p> <p>4 Table Number of students (%) and mean and standard deviation (SD) of academic achievement across nine groups.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th /><th>High LS</th><th>Medium LS</th><th>Low LS</th><th>Overall</th></tr></thead><tbody valign="top"><tr><td>High SE</td><td>M (SD)</td><td>432.99 (91.32)</td><td>397.84 (96.39)</td><td>367.09 (108.15)</td><td>419.21 (96.06)</td></tr><tr><td align="center">N (%)</td><td>904 (18.7%)</td><td>366 (7.6%)</td><td>89 (1.9%)</td><td>1359 (28.2%)</td></tr><tr><td>Medium SE</td><td>M (SD)</td><td>419.68 (91.29)</td><td>398.73 (92.39)</td><td>355.09 (105.34)</td><td>393.26 (97.88)</td></tr><tr><td align="center">N (%)</td><td>456 (9.5%)</td><td>1108 (23.0%)</td><td>469 (9.7%)</td><td>2033 (42.1%)</td></tr><tr><td>Low SE</td><td>M (SD)</td><td>411.51 (92.79)</td><td>385.10 (99.93)</td><td>325.83 (113.18)</td><td>350.69 (112.21)</td></tr><tr><td align="center">N (%)</td><td>83 (1.7%)</td><td>481 (10.0%)</td><td>869 (18.0%)</td><td>1433 (29.7%)</td></tr><tr><td>Overall</td><td>M (SD)</td><td>427.55 (91.62)</td><td>395.21 (95.17)</td><td>338.02 (111.36)</td><td>387.97 (105.22)</td></tr><tr><td align="center">N (%)</td><td>1427 (29.6%)</td><td>1955 (40.5%)</td><td>1443 (29.9%)</td><td>4825 (100.0%)</td></tr></tbody></table> </ephtml> </p> <p>3 Abbreviations: LS = learning strategy, SE = student engagement.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23600-fig-0003.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23600-fig-0003.jpg" title="3 Number of students (%) and mean academic achievement across nine aroups." /> </p> <p></p> <p>The results of the two‐factor between‐subjects ANOVA, presented in Table 5, revealed significant main effects for both student engagement (SE) and learning strategy (LS), as well as a significant interaction between them. Specifically, SE had a significant effect on academic achievement, <emph>F</emph>(<reflink idref="bib2" id="ref72">2</reflink>, 4816) = 10.30, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0010" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.004. LS showed a stronger effect, <emph>F</emph>(<reflink idref="bib2" id="ref73">2</reflink>, 4816) = 83.34, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0011" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.033. The interaction between SE and LS was also significant, <emph>F</emph>(<reflink idref="bib4" id="ref74">4</reflink>, 4816) = 2.43, <emph>p</emph> = 0.046, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0012" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.002, indicating that the effect of one factor depended on the level of the other.</p> <p>5 Table Two‐factor anova examining the effects of student engagement and learning strategy on academic achievement.</p> <p> <ephtml> <table><thead valign="bottom"><tr valign="bottom"><th /><th>SS</th><th>MS</th><th><italic>df</italic></th><th><italic>F</italic></th><th><italic>p</italic></th><th><p><math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0013" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math></p></th></tr></thead><tbody valign="top"><tr><td>Student engagement (SE)</td><td>200990.20</td><td>100495.10</td><td>2</td><td>10.30</td><td>< 0.001</td><td>.004</td></tr><tr><td>Learning strategy (LS)</td><td>1626695.94</td><td>813347.97</td><td>2</td><td>83.34</td><td>< 0.001</td><td>.033</td></tr><tr><td>SE <p><math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0014" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics xmlns=""><mo>×</mo></semantics></math></p> LS</td><td>94837.13</td><td>23709.28</td><td>4</td><td>2.43</td><td>.046</td><td>.002</td></tr><tr><td>Error</td><td>47003670.40</td><td>9759.90</td><td>4816</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <p>Further analysis examined the simple effects of learning strategy (LS) at varying levels of student engagement (SE). When SE was low, LS had a significant effect on academic achievement, <emph>F</emph>(<reflink idref="bib2" id="ref75">2</reflink>, 4816) = 72.42, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0015" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.029. Both the high and medium LS groups outperformed the low LS group, with no significant difference between the high and medium LS groups. At medium levels of SE, LS again had a significant impact, <emph>F</emph>(<reflink idref="bib2" id="ref76">2</reflink>, 4816) = 53.02, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0016" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.022, with academic achievement following the pattern: high LS > medium LS > low LS. At high SE levels, LS remained a significant predictor, <emph>F</emph>(<reflink idref="bib2" id="ref77">2</reflink>, 4816) = 29.75, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0017" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.012, with high LS outperforming medium LS, and medium LS outperforming low LS.</p> <p>The analysis also examined the simple effects of student engagement (SE) at different levels of learning strategy (LS). When LS was low, SE significantly impacted academic achievement, <emph>F</emph>(<reflink idref="bib2" id="ref78">2</reflink>, 4816) = 17.47, <emph>p</emph> < 0.001, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0018" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.007, with high and medium SE groups outperforming the low SE group, but no difference between high and medium SE groups. At medium levels of LS, SE had a weaker yet significant effect, <emph>F</emph>(<reflink idref="bib2" id="ref79">2</reflink>, 4816) = 3.35, <emph>p</emph> = 0.035, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0019" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.001, with only the medium SE group outperforming the low SE group. When LS was high, SE had a small but significant effect, <emph>F</emph>(<reflink idref="bib2" id="ref80">2</reflink>, 4816) = 3.91, <emph>p</emph> = 0.020, <ephtml> <math altimg="urn:x-wiley:00333085:media:pits23600:pits23600-math-0020" display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><msubsup><mi mathvariant="normal">η</mi><mi>p</mi><mn>2</mn></msubsup></mrow></mrow></semantics></math> </ephtml>  = 0.002, with high and medium SE groups outperforming the low SE group, but no difference between high and medium SE groups.</p> <p>In summary, both student engagement (SE) and learning strategy (LS) significantly influenced academic achievement, with LS demonstrating a stronger overall effect. The simple effect sizes of LS across levels of student engagement (0.029, 0.022, 0.012, from low SE to high SE) were consistently larger than those of SE across levels of learning strategy (0.007, 0.001, 0.002, from low LS to high LS). This underscores the greater predictive importance of learning strategy compared to student engagement for academic success.</p> <p>A direct comparison between the two extreme groups—"high SE‐low LS" and "low SE‐high LS"—further supports this conclusion. A significant difference in academic achievement was observed between these groups, <emph>F</emph>(<reflink idref="bib1" id="ref81">1</reflink>, 4816) = 3.91, <emph>p</emph> = 0.020, <emph>d</emph> = 0.339. Specifically, the "low SE‐high LS" group (<emph>M</emph> = 411.51, SD = 92.79) significantly outperformed the "high SE‐low LS" group (<emph>M</emph> = 367.09, SD = 108.15). This finding provides direct support for the hypothesis that "Ineffective Methods Lead to Wasted Effort."</p> <hd id="AN0187949522-24">Discussion</hd> <p>This study investigated student engagement and learning strategy levels among eighth‐grade students and examined how these factors influence academic achievement. Overall, students showed moderate engagement and strategy use, with cognitive engagement and organizational strategy being the weakest areas. Girls outperformed boys in academic achievement and learning strategies, though no significant gender differences were found in engagement. Structural equation modeling revealed that learning strategy fully mediated the effect of engagement on achievement. Simple effects analysis further showed that learning strategy had a stronger predictive value than engagement at corresponding levels. Notably, students with low engagement but high strategy use outperformed those with high engagement but low strategy use, underscoring the greater importance of learning strategy in academic success. These findings deepen our understanding of how engagement and strategy interact to shape academic outcomes.</p> <hd id="AN0187949522-25">Factor Structure of the Student Engagement and Learning Strategy Scales</hd> <p>This study employed the Delaware Student Engagement Survey (DSES) with Chinese junior high school students, revealing inconsistent factor‐item relationships in behavioral and cognitive engagement compared to American students. While DSES has shown measurement invariance across race and ethnicity in the U.S. (Bear et al. [<reflink idref="bib5" id="ref82">5</reflink>]), its applicability to other cultures remains uncertain. These differences may reflect cultural distinctions between Eastern and Western contexts. Interviews with students and teachers indicated that Chinese students view paying attention and working harder as indicators of internal motivation, thus categorizing these activities under cognitive engagement. Conversely, behaviors like fixing mistakes and submitting homework are seen as compliance with school rules and habit formation, aligning with behavioral engagement (see supporting Theme 1). These findings suggest that cultural nuances influence how specific activities are classified within the constructs of engagement.</p> <p>To balance respondent burden and psychometric quality, we used a shortened version of the motivated strategies for learning questionnaire (MSLQ) with 20 high‐loading items. Five factors were extracted, combining highly correlated original factors. Interviews with teachers and students supported these merges: rehearsal and elaboration were combined as core skills for memory and comprehension; effort regulation and time management were merged for their focus on regulating personal resources; peer learning and help‐seeking were combined due to their shared emphasis on social learning resources. These combinations ensured each factor had at least three items, improving reliability. Additionally, two items related to study environment and persistence were integrated into metacognitive self‐regulation, as they reflect active internal learning processes. Factor analysis and interviews support these merges (see supporting Theme 2), though further research is needed to confirm the item‐factor relationships.</p> <hd id="AN0187949522-26">Current Status and Gender Differences of Student Engagement and Learning Strategies</hd> <p></p> <hd id="AN0187949522-27">Student Engagement</hd> <p>Students showed moderate overall engagement, with behavioral engagement being the highest, followed by emotional, and cognitive engagement being the lowest. Interviews with teachers and students suggested that this pattern aligns with the perceived difficulty of these engagement types (see supporting Theme 1). Behavioral engagement, seen as fundamental and easily observable through actions like following school rules and participating in activities, is the easiest to achieve. Cognitive engagement, requiring active learning, sustained attention, and concentration, is the most challenging. Emotional engagement falls between the two. Item‐level analysis of cognitive engagement revealed lower scores on items such as "I get good grades in school," likely due to a lack of successful learning experiences or confidence.</p> <p>To enhance cognitive engagement, teachers should highlight the value of subjects, offer timely support, and select tasks within students' zone of proximal development to promote learning success and self‐efficacy (Bandura [<reflink idref="bib3" id="ref83">3</reflink>]). Improving emotional engagement requires schools to focus on enhancing teaching quality, fostering positive classroom experiences, and supporting healthy relationships among students (Zepke and Leach [<reflink idref="bib46" id="ref84">46</reflink>]).</p> <p>This study found no significant gender differences in overall student engagement or cognitive engagement. However, boys significantly outperformed girls in emotional engagement, while girls outperformed boys in behavioral engagement. These findings suggest that although girls are more compliant with tasks such as submitting assignments and following rules, they may not experience positive emotions or a sense of school belonging. In contrast, boys have more positive emotional experiences at school but are less likely to adhere to rules. Interviews with students and teachers revealed that eighth‐grade girls tend to be more mature and rule‐abiding, but their focus on details and lower tolerance for school shortcomings, such as cleanliness and meal quality, may reduce their sense of school identification. Boys, on the other hand, are motivated by social interactions and physical activities, contributing to their positive emotional experiences, but their rebellious nature makes them less willing to follow rules (see supporting Theme 3).</p> <p>To address these findings, educators should foster inclusive and supportive classroom environments for girls by integrating group activities that promote positive peer relationships and opportunities for self‐expression. Improvements in school hygiene and meal quality may further strengthen girls' sense of school belonging. For boys, promoting self‐discipline through clear behavioral expectations, timely feedback, and positive reinforcement is essential. Additionally, democratic classroom management, respect for individual differences, and reframing challenges as growth opportunities may enhance boys' engagement.</p> <hd id="AN0187949522-28">Learning Strategies</hd> <p>Students showed moderate use of learning strategies, performing best in rehearsal, elaboration, peer learning, help‐seeking, and metacognitive self‐regulation. Effort and time management were average, while organization was weakest. Interviews indicated that managing time and effort requires self‐control, which many students lack due to distractions from electronic devices and dependence on adults for scheduling. Organizational skills were also underdeveloped, with teachers noting limited instruction and practice in this area. Item‐level analysis revealed better performance on basic tasks (e.g., summarizing) than complex ones (e.g., creating charts), likely due to a focus on fragmented rather than integrated knowledge.</p> <p>To improve organizational strategies, teachers should begin with simple summarization and gradually introduce more complex tools such as concept maps, flowcharts, mind maps, and outlines. These techniques help students identify key ideas, supporting details, and connections for better encoding and recall (Weinstein and Mayer [<reflink idref="bib44" id="ref85">44</reflink>]). For effort and time management, teachers can support students by setting clear goals, prioritizing tasks, breaking tasks into manageable steps, scheduling time blocks, tracking progress, and reducing distractions (Covey [<reflink idref="bib13" id="ref86">13</reflink>]). This study indicates that strengthening these skills can effectively enhance academic achievement.</p> <p>Significant gender differences were found in learning strategy use, with girls outperforming boys overall, except in effort and time management, where no difference emerged. Interviews suggest that girls' greater maturity, openness to guidance, and collaborative habits contribute to their advantage. Girls more often engage in group study and share strategies, while boys are less proactive and more drawn to recreational activities. To address this, teachers can offer girls more advanced strategy training and support boys by introducing basic techniques through practical, engaging examples. Encouraging peer collaboration among boys can also help, emphasizing that cooperation supports, rather than undermines, independence (see supporting Theme 3).</p> <p>Additionally, girls significantly outperformed boys in academic achievement, aligning with previous research (Voyer and Voyer [<reflink idref="bib41" id="ref87">41</reflink>]). Given that learning strategies are a critical predictor of academic success, gender differences in strategy use likely contribute to the observed achievement gap. Girls' more effective use of learning strategies may partially account for their higher performance, further supporting the study's primary finding that learning strategy is a stronger predictor of academic achievement than student engagement.</p> <hd id="AN0187949522-29">The Role of Learning Strategies and Student Engagement in Predicting Academic Achievement</hd> <p>Our findings align with previous research showing that learning strategy fully mediates the effect of motivational factors on academic achievement (Diseth and Kobbeltvedt [<reflink idref="bib14" id="ref88">14</reflink>]; Greene et al. [<reflink idref="bib19" id="ref89">19</reflink>]; Pokay and Blumenfeld [<reflink idref="bib37" id="ref90">37</reflink>]; Zhang and Zhang [<reflink idref="bib47" id="ref91">47</reflink>]; Hu and Xu [<reflink idref="bib23" id="ref92">23</reflink>]). Extending this, we confirm that student engagement also influences academic achievement exclusively through learning strategy, based on a large‐scale sample of junior high students. This supports a complete mediation model, with learning strategy fully mediating the effect of engagement on achievement, validated through ANOVA and planned contrast analyses.</p> <p>The complete mediating role of learning strategy suggests it directly influences academic achievement, while student engagement affects achievement only indirectly through learning strategy. Thus, learning strategy is more critical than engagement in predicting academic success. This aligns with self‐regulated learning models, which argue that self‐regulated learning activities are key to knowledge acquisition and outcomes (Pintrich [<reflink idref="bib34" id="ref93">34</reflink>]). Effective learning strategies enable students to enhance performance through self‐regulation, whereas high engagement without mastery of strategies does not ensure strong academic achievement, reinforcing the principle: "Ineffective Methods Lead to Wasted Effort."</p> <p>While learning strategy fully mediates the effect of student engagement on academic achievement, this does not diminish the importance of engagement. The strong path coefficient from engagement to learning strategy suggests that engagement often predicts effective strategy use, consistent with S. Floyd et al. ([<reflink idref="bib38" id="ref94">38</reflink>]). According to Zimmerman ([<reflink idref="bib50" id="ref95">50</reflink>]) self‐regulated learning model, effective strategies are driven by personal initiative and persistence, indicating that engagement is crucial for developing advanced strategies. However, engagement alone is insufficient; it must be paired with effective strategies to avoid aimless learning and poor academic performance (Zimmerman [<reflink idref="bib49" id="ref96">49</reflink>]). Interviews with teachers and students emphasize that while engagement is foundational, strategies determine academic success (see supporting Theme 4). Thus, improving engagement should be prioritized when students struggle to initiate learning, with strategies becoming the primary driver once engagement is established.</p> <p>The relationship between student engagement, learning strategies, and academic achievement may be cyclical, as illustrated in Figure 4. Specifically, student engagement promotes the use of effective learning strategies, which subsequently enhance academic performance. Improved performance can, in turn, boost engagement, forming a virtuous cycle. Conversely, when students exert effort without effective strategies, they may perform poorly, leading to learned helplessness and reduced engagement—resulting in a vicious cycle. In school settings, where external pressure and supervision are common, the key concern is whether students have acquired effective learning strategies to achieve academic success and strengthen their sense of efficacy in engagement.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/PIS/01oct25/pits23600-fig-0004.jpg?ephost1=dGJyMMvl7ESepq84yOvsOLCmsE6epq5Srqa4SK6WxWXS" alt="pits23600-fig-0004.jpg" title="4 Potential cyclical mechanism linking engagement, strategy use, and academic achievement." /> </p> <p></p> <p>The importance of student engagement and learning strategy varies with educational context and student needs (Assor [<reflink idref="bib2" id="ref97">2</reflink>]). Engagement is crucial when motivation is a primary concern, while learning strategy becomes increasingly critical as academic demands rise, particularly in junior high school and beyond. In contrast to primary school, junior high introduces abstract concepts, multi‐step problem‐solving, and interconnected knowledge, making surface‐level strategies inadequate. Advanced generative strategies are necessary for deeper comprehension, retention, and transfer of knowledge (Fiorella and Mayer [<reflink idref="bib17" id="ref98">17</reflink>]). This shift aligns with cognitive load theory, which suggests that higher academic levels impose greater working memory demands. Effective learning strategies help manage cognitive load, enabling critical thinking and application (Paas et al. [<reflink idref="bib30" id="ref99">30</reflink>]). As content complexity increases, the consequences of ineffective methods are amplified, and students without effective strategies may struggle to integrate concepts, even with high engagement.</p> <p>In summary, to enhance academic achievement, students must actively engage in learning and apply effective strategies, rather than passively participate without reflection. High engagement often leads to the adoption of advanced strategies through practice and feedback from teachers and peers. Research shows that teacher feedback on learning strategies can bridge the gap between current understanding and goals, improving academic performance (Hattie and Timperley [<reflink idref="bib21" id="ref100">21</reflink>]).</p> <hd id="AN0187949522-31">Limitations and Future Research</hd> <p>While this study provides valuable insights, several limitations warrant consideration for future research. First, the cross‐sectional design limits causal inferences, as unmeasured third variables (e.g., parental support, cognitive ability) may influence the observed relationships. Future research should use longitudinal designs, such as cross‐lagged panel analysis, to clarify the temporal and reciprocal relationships between student engagement, learning strategy, and academic achievement, while controlling for potential confounders. Additionally, natural experiments (e.g., altering engagement levels with or without corresponding improvements in strategy use) could help validate the mediation effect.</p> <p>Second, this study derived a single factor for overall academic achievement from six subjects, potentially obscuring disciplinary differences in how student engagement and learning strategies function, as each subject involves distinct cognitive processes (Stodolsky [<reflink idref="bib39" id="ref101">39</reflink>]). Emerging evidence suggests that subject domains moderate these relationships, with different mediation patterns across subjects. For example, mathematical achievement is particularly sensitive to engagement‐mediated strategy effects (Büchele [<reflink idref="bib8" id="ref102">8</reflink>]), while language learning shows stronger strategy‐mediated engagement pathways (Wang and Lu [<reflink idref="bib42" id="ref103">42</reflink>]). Future research should explore the moderating effect of subject type and develop subject‐specific mediation models to assess whether engagement‐strategy‐achievement pathways differ across subjects and identify domain‐specific strategies (e.g., metacognitive monitoring for sciences vs. elaboration for humanities).</p> <p>Third, cultural validity poses a potential limitation. As this study was based on a large Chinese sample, its findings may not generalize across cultures, since cultural differences can affect how items are interpreted and how scale structures function (e.g., differing factor‐item relationships for behavioral and cognitive engagement between China and the U.S.). Future research should conduct cross‐cultural comparisons to test the cultural boundaries of the engagement–strategy–achievement mechanism and avoid overgeneralization.</p> <p>Finally, excluding insincere or missing responses may have systematically omitted students with low engagement or limited cognitive resources—groups that also showed significantly lower academic achievement. These students likely represent a high‐risk group for "ineffective effort," yet their behavioral patterns are underrepresented. Future research should use mixed methods, combining qualitative interviews with quantitative analysis, to investigate barriers to engagement and strategy failure among marginalized students and avoid selection bias in conclusions.</p> <hd id="AN0187949522-32">Conclusion</hd> <p>Based on a large‐scale sample of Chinese eighth‐grade students, this study demonstrates that learning strategy fully mediates the effect of student engagement on academic achievement. While high engagement provides a solid foundation for academic success, its full potential is realized only when guided by effective strategies. Without effective learning strategies, even highly engaged students may struggle academically, embodying the concept of "Ineffective Methods Lead to Wasted Effort." Key findings reveal that cognitive engagement and organizational strategy were the weakest areas among students, and girls outperformed boys in strategy use, contributing to their higher academic achievement. These findings suggest that teachers should prioritize explicit instruction in learning strategies, adopt gender‐responsive approaches, and support students in cultivating a virtuous cycle of engagement–strategy use–achievement.</p> <hd id="AN0187949522-33">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <p>GRAPH: Supplementary_Material_R2_0504.</p> <ref id="AN0187949522-34"> <title> References </title> <blist> <bibl id="bib1" idref="ref8" type="bt">1</bibl> <bibtext> Appleton, J. J., S. L. Christenson, and M. J. Furlong. 2008. " Student Engagement With School: Critical Conceptual and Methodological Issues of the Construct." Psychology in the Schools 45, no. 5 : 369 – 386. https://doi.org/10.1002/pits.20303.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref49" type="bt">2</bibl> <bibtext> Assor, A. 2012. " Allowing Choice And Nurturing an Inner Compass: Educational Practices Supporting Students' Need for Autonomy." 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  Data: Ineffective Methods Lead to Wasted Effort: Learning Strategies Are More Important than Student Engagement in Predicting Academic Achievement
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  Data: <searchLink fieldCode="AR" term="%22Hu+Zhiqiao%22">Hu Zhiqiao</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang+Wenyuan%22">Jiang Wenyuan</searchLink><br /><searchLink fieldCode="AR" term="%22Niu+Duan%22">Niu Duan</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-9225-4287">0009-0006-9225-4287</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Psychology+in+the+Schools%22"><i>Psychology in the Schools</i></searchLink>. 2025 62(10):4102-4115.
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  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
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  Data: 14
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  Data: 2025
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+8%22">Grade 8</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+8%22">Grade 8</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Questionnaires%22">Questionnaires</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Skill+Development%22">Skill Development</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Management%22">Time Management</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
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  Data: 10.1002/pits.23600
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  Data: 0033-3085<br />1520-6807
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  Data: This study investigates the relationship among student engagement, learning strategies, and academic achievement. Data were collected from 4,825 eighth-grade students in Guangzhou, China, using the Delaware Student Engagement Scale (DSES) and the Motivated Strategies for Learning Questionnaire (MSLQ). Structural Equation Modeling (SEM) with mediation analysis revealed that both student engagement and learning strategies positively predict academic achievement, with learning strategies fully mediating the effect of engagement on achievement. While higher engagement generally leads to greater use of learning strategies, students with high engagement but low strategy use perform significantly worse than those with lower engagement but more effective strategy use--supporting the concept that "ineffective methods lead to wasted effort." Findings show that cognitive engagement was the lowest among the engagement factors, while organizational strategy was the lowest among learning strategy dimensions. Female students exhibited more effective strategy use than their male counterparts. Practical implications suggest that educators focus on improving students' learning strategies, especially organizational skills, to enhance academic performance and address gender differences. However, due to the study's cultural context, the results may not be generalizable across all populations, underscoring the need for further research in diverse settings.
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  Data: 2025
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  Data: EJ1483615
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        Value: 10.1002/pits.23600
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      – Text: English
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      Pagination:
        PageCount: 14
        StartPage: 4102
    Subjects:
      – SubjectFull: Learning Strategies
        Type: general
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Academic Achievement
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      – SubjectFull: Middle School Students
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      – SubjectFull: Grade 8
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      – SubjectFull: Time Management
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      – SubjectFull: China
        Type: general
      – SubjectFull: Motivated Strategies for Learning Questionnaire
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    Titles:
      – TitleFull: Ineffective Methods Lead to Wasted Effort: Learning Strategies Are More Important than Student Engagement in Predicting Academic Achievement
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            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Psychology in the Schools
              Type: main
ResultId 1