Exploring the Impact of E-Portfolio Reflections on Learning Efficiency and Cognitive Loads among Thai Undergraduate Students
Bibliographic record
Abstract
The disparity between theoretical knowledge and practical application in undergraduate education poses a formidable challenge, potentially burdening students’ cognitive load. In response, e-portfolios have emerged as a promising solution, capable of fostering self-directed learning and promoting students’ ownership of the quality of their educational outcomes. This study investigates the impact of employing e-portfolios for reflective practices on learning efficiency and cognitive load among Thai undergraduate students. The sample comprised twenty English major undergraduates selected through purposive sampling from a university in Thailand. Employing a mixed-methods experimental design, the intervention involved incorporating reflection activities (i.e., writing a learning log) through e-portfolios. Pre- and post-tests, coupled with a cognitive load survey, were administered, while qualitative data were collected to delve into participants’ attitudes and perspectives. The results revealed a significant improvement in post-test scores when compared to pre-test scores (Cohen’s d = 0.891), underscoring the substantial impact of the intervention. Additionally, the average cognitive load exhibited a decrease in intrinsic and extraneous load, while the automatic load remained unchanged. In addition, student interviewees believed that reflections through e-portfolios could help them learn better, but also expressed some issues with its utlization. However, a small sample could influence the generalizability. These findings hold practical implications for leveraging reflection through e-portfolios to enhance learning efficacy, promote a comprehensive understanding of lessons, and foster self-monitoring of academic progress.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".