Exploring Trends and Impacts of Problem Based Learning E-Modules in Elementary Science Learning: Systematic Literature Review
Bibliographic record
Abstract
The use of interactive digital learning materials, such as electronic modules based on Problem-Based Learning (PBL), is one way the education sector is adapting to technological advances in the Society 5.0 era. This digital transformation requires innovation in learning methods to prepare a generation with 21st-century skills. The purpose of this study is to systematically review the trends in the implementation and impact of the use of e-modules based on Problem-Based Learning (PBL) in Science Education at the elementary school level. Using the Systematic Literature Review (SLR) method based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this article reviews 20 national and international journals published between 2021 and 2025. The study results indicate that PBL-based e-modules are effective in enhancing various cognitive skills among students, including critical thinking, scientific literacy, problem-solving abilities, and overall learning outcomes. Additionally, PBL e-modules demonstrate high levels of validity and practicality, making them suitable and applicable teaching materials. However, their implementation faces challenges such as limited digital infrastructure, insufficient teacher readiness, and the need for long-term effectiveness analysis and differentiation for students with special needs. This study provides a comprehensive synthesis of the development of PBL e-modules in the field of elementary education and offers strategic recommendations for further research and development, including the exploration of integration with cutting-edge technologies such as artificial intelligence (AI) and Augmented Reality (AR).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.025 | 0.021 |
| 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) | 0.003 | 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 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".