Exploring the Pathway of Project-Based Learning for Enhancing Core Competency in Educational Psychology
Bibliographic record
Abstract
This study explores the impact of project-based learning (PBL) on enhancing core competencies in educational psychology courses, specifically learning motivation, critical thinking, and collaborative problem-solving skills among undergraduate students. The research sample comprised 42 undergraduate students enrolled in an educational psychology course. A project-based learning management plan was implemented, with assessments conducted using the Working Preference Inventory, the California Critical Thinking Disposition Inventory, and the Collaborative Problem-Solving Skills Scale. The results indicate that PBL significantly enhances students’ competitive awareness (t = 3.58, p < .01) and thirst for knowledge (t = 5.93, p < .001). Moreover, PBL fosters the systematic nature of students’ cognition (t = 2.98, p < .05) and promotes cognitive maturity (t = 44.08, p < .001). It also strengthens students’ ability to plan and execute problem-solving tasks (t = 4.00, p < .01). However, group collaboration in PBL settings may reduce individual initiative. These findings underscore the effectiveness of PBL in developing students’ core competencies, particularly within educational psychology. On one hand, this study highlights the role of PBL in enhancing students’ comprehensive abilities and promoting deep learning in undergraduate education. On the other hand, it suggests that the design and implementation of PBL should balance the dual needs of individual and team-based learning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".