The examination of the relationship between learning motivation and learning effectiveness: a mediation model of learning engagement

In 2010, China officially entered the process of universalization of higher education, bringing increased attention to education quality and its evaluation. From the student perspective, beyond final grades, learning initiative and engagement during the learning process are crucial. Prior work suggests greater participation relates to higher achievement and stronger school identification, underpinned by learning motivation. University students may show insufficient motivation compared to high school, resulting in low commitment and suboptimal learning states. Learning motivation is the inner psychological state that directs, energizes, and sustains learning behavior; varying motivation levels are associated with different degrees of learning engagement. Learning engagement is a positive, fulfilling mental state related to learning, and higher enthusiasm typically yields greater engagement and better outcomes. Personality traits have been linked to academic performance. Learning effectiveness encompasses not only grades but also understanding and mastery. The study addresses a gap by jointly examining learning motivation (internal and external), learning engagement (behavioral and emotional), personality traits, and learning effectiveness within one framework for Chinese undergraduates. It aims to clarify their relationships, including mediation by engagement and moderation by personality traits, and to provide recommendations to improve motivation, engagement, depth of learning, and learning effectiveness.

Learning motivation: It stimulates and sustains learning behavior and is a complex construct including desire, interest orientation, and willingness. Psychological perspectives distinguish intrinsic (interest, curiosity, achievement needs) and extrinsic (rewards, punishments, fear of failure) motivations. Prior studies report a positive relationship between learning motivation and academic performance. This study divides motivation into internal and external components. Hypothesis 1 (H1): Learning motivation has a significant positive impact on learning effectiveness: H1.1 internal learning motivation; H1.2 external learning motivation. Learning engagement: Defined as behavioral, emotional, and cognitive engagement, it is predictive of academic performance. External (family, community, culture, educational environment) and internal (initiative, personality traits, motivation, self-efficacy, cooperation) factors influence engagement. Hypothesis 2 (H2): Learning motivation positively impacts learning engagement: H2.1 internal; H2.2 external. Learning effectiveness: It comprises learning achievements, performance, and progress, influenced by personal factors (motivation, interest), family background, school teaching/management, and social environment (e.g., employment pressure). Relationship among constructs: Motivation and engagement are key predictors of achievement; time and energy invested relate to performance. Hypothesis 3 (H3): Learning engagement positively impacts learning effectiveness: H3.1 behavioral engagement; H3.2 emotional engagement. Mediation hypothesis: H5: Learning engagement mediates the relationship between learning motivation and learning effectiveness. Personality traits: Big Five traits (agreeableness, conscientiousness, extraversion, neuroticism, openness) are associated with differing behaviors and outcomes. Studies show personality relates to self-efficacy, social expectations, and performance. Hypothesis 4 (H4): Personality traits positively moderate the relationship between learning motivation (H4.1 internal; H4.2 external) and learning effectiveness.

Design: Cross-sectional survey of undergraduate students across various Chinese universities via the Questionnaire Star online platform. Total collected questionnaires: 280; valid: 251 (effective rate 89.6%). Measures: Five-point Likert scales. - Learning Motivation (Qin, 2021): Two dimensions—Internal Learning Motivation (ILM, 5 items) and External Learning Motivation (ELM, 5 items). - Learning Engagement (Dong, 2021): Two dimensions—Behavioral Engagement (BLE, 3 items) and Emotional Engagement (ELE, 5 items). - Personality Traits: Big-Five short-form adapted from Goldsmith (2016), 10 items (treated as one dimension). - Learning Effectiveness (Feng, 2021): One dimension, 9 items. Reliability: Cronbach’s alpha—ILM 0.835; ELM 0.791; BLE 0.766; ELE 0.824; Personality traits 0.944; Learning effectiveness 0.932. Validity: - Learning Motivation: KMO 0.841; Bartlett’s p<0.001; two factors extracted (eigenvalues 4.443, 1.383; explained variance 44.431% and 13.829%); all loadings >0.5 indicating convergent and discriminant validity. - Learning Engagement: KMO 0.836; Bartlett’s p<0.001; two factors extracted (eigenvalues 3.869, 1.171; explained variance 48.360% and 14.636%); loadings >0.5. - Learning Effectiveness: KMO 0.916; Bartlett’s p<0.001; one factor (eigenvalue 5.831; explained variance 64.785%); loadings >0.5. - Personality Traits: KMO 0.964; Bartlett’s p<0.001; CR 0.935; AVE 0.590; discriminant validity 0.768. Analysis: Pearson product-moment correlations to examine associations. Multiple regression analyses (stepwise and hierarchical) to test effects. Mediation tested following Baron and Kenny (1986) and MacKinnon et al. (1995; 2008). Moderation tested using centralized variables and interaction terms per Fang et al. (2022). Software: SPSS 21 and AMOS 24. Sample demographics (N=251): 30.28% male, 69.72% female; Grades—Freshman 12.35%, Sophomore 15.54%, Junior 55.78%, Senior 16.33%; Majors—Science & Engineering 66.93%, Literature & History 29.08%, Art & Sports 3.98%; Origin—Rural 67.73%, Urban 32.27%.

- Correlations: Internal and external learning motivation were positively correlated with learning effectiveness (r=0.582 and r=0.494, p<0.01). Behavioral and emotional engagement correlated with learning effectiveness (r=0.533 and r=0.766, p<0.01). Personality traits correlated with learning effectiveness (r=0.724, p<0.01). - H1 (Motivation → Effectiveness): Both internal learning motivation (β=0.410, p<0.001) and external learning motivation (β=0.255, p<0.001) significantly predicted learning effectiveness; model F=77.994, R^2=0.386. - H2 (Motivation → Engagement): Internal (β=0.435, p<0.001) and external (β=0.186, p<0.001) motivation significantly predicted learning engagement; F=68.162, R^2=0.355. - H3 (Engagement → Effectiveness): Behavioral engagement (β=0.145, p<0.001) and emotional engagement (β=0.629, p<0.001) significantly predicted learning effectiveness; F=194.528, R^2=0.611. Emotional engagement had a stronger effect than behavioral engagement. - H5 (Mediation by Engagement): When learning engagement was added (β=0.627, p<0.001), the coefficients for internal and external motivation on effectiveness decreased from β=0.414 to β=0.146 (p<0.01) and from β=0.259 to β=0.140 (p<0.01), indicating partial mediation. Model R^2 increased to 0.629 (Model 3). - H4 (Moderation by Personality Traits): Interaction terms were significant: Internal motivation × personality traits (standardized β=0.183, t=4.516, p<0.001) and External motivation × personality traits (standardized β=0.247, t=6.103, p<0.001), supporting a positive moderation effect. - Reliability was high across scales (alphas 0.766–0.944); validity indices (KMO, AVE, CR) met acceptable thresholds.

Findings confirm that higher learning motivation is associated with better learning effectiveness among undergraduates, aligning with prior literature. Motivation also promotes deeper learning engagement; both internal interest-driven factors and external influences enhance engagement. In turn, greater engagement—especially emotional engagement—yields stronger learning effectiveness, underscoring the critical role of students’ affective investment in their studies. Mediation analyses show engagement partially transmits the effect of motivation to effectiveness, suggesting that converting motivation into engaged behaviors and emotional involvement is a key mechanism but not the only one; additional mediators likely operate. Personality traits significantly moderate the motivation–effectiveness link, indicating that individual differences shape how motivation translates into outcomes. These results emphasize the importance of cultivating intrinsic motivation, fostering emotionally engaging learning environments, and considering personality-driven variability when designing interventions to improve academic effectiveness.

All primary hypotheses were supported. Internal and external learning motivation positively affected learning effectiveness (H1), and both positively influenced behavioral and emotional engagement (H2). Behavioral and emotional engagement positively affected learning effectiveness, with emotional engagement exerting a stronger influence (H3). Learning engagement partially mediated the motivation–effectiveness relationship (H5). Personality traits positively moderated the effects of both internal and external motivation on learning effectiveness (H4). Practical implications: Students should cultivate intrinsic motivation, clarify goals, and engage deeply in coursework. Teachers should innovate pedagogy to stimulate interest and emotional involvement, assign appropriately challenging tasks, and monitor mastery. Institutions should provide thematic lectures, enrich learning resources, and create supportive learning atmospheres. Future research should examine additional mediators/moderators and employ more detailed personality assessments (e.g., long-form Big-Five) to better understand individual differences.

The study indicates that learning engagement only partially mediates the relationship between learning motivation and learning effectiveness, implying other mediators exist and should be explored. The personality traits measure used a short-form scale; the authors suggest employing the long-form Big-Five in future work for deeper analysis. Data were based on self-report questionnaires from undergraduate students, and additional factors influencing the studied relationships may exist.