Psychological Factors Influencing Learners’ Engagement and Academic Performance in Blended Learning Environments: A Systematic Review
Bibliographic record
Abstract
This systematic review investigates the psychological factors that influence learners’ engagement and academic performance in blended learning environments. Guided by the PRISMA framework, 26 empirical studies published between 2015 and 2025 were analysed. The synthesis identified six key psychological factors that shape learners’ engagement and learning outcomes in blended learning, which are motivation, emotional engagement, psychological capital, cognitive engagement, social presence and interaction, and technology acceptance and design quality. Intrinsic motivation and emotional engagement consistently predicted academic success, while psychological capital, which includes resilience and self-efficacy, supported persistence in demanding learning contexts. Cognitive engagement, expressed through active thinking and problem-solving, and social presence, developed through peer and instructor interaction, further enhanced learning experiences. Furthermore, technology acceptance and design quality were found to influence satisfaction and engagement, especially when learning tools were reliable and user-friendly. These findings can guide educators in designing blended learning environments that promote both engagement and academic performance. This research contributes by providing a comprehensive framework that identifies the interplay of key psychological factors, such as motivation, emotional engagement, and cognitive engagement, in shaping learners' academic performance in blended learning environments. The findings offer practical insights for educators to design more effective, engaging, and supportive blended learning experiences that promote both learner engagement and academic success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".