Online Training by Active Learning Approaches: A Systematic Literature Review
Bibliographic record
Abstract
This systematic literature review explores the implementation and effectiveness of active learning approaches in online training environments. The rapid growth of online education necessitates strategies that enhance learner engagement and improve educational outcomes. The review identifies various active learning techniques, such as discussions, simulations, case studies, and collaborative problem-solving, which shift the focus from passive information absorption to active knowledge construction. Analyzing empirical studies from 2020 to 2024, the review highlights the positive impacts of active learning on learner engagement, motivation, satisfaction, and overall learning outcomes. Key findings reveal that active learning methods lead to improved knowledge retention, skill development, and practical application of knowledge, addressing the challenges of disengagement commonly associated with traditional online training methods. The review also emphasizes the importance of adaptive learning systems, personalized feedback, and interactive learning activities in fostering an engaging online learning environment. Based on the findings, the review provides recommendations for designing and implementing effective online training programs and suggests directions for future research to further enhance the effectiveness and accessibility of active learning approaches in digital education. By addressing these aspects, the review contributes valuable insights into the development of engaging and impactful online training experiences, ultimately improving learning outcomes for diverse learners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".