E-Portfolio Reflective Learning Strategies to Enhance Research Skills, Analytical Ability, Creativity and Problem-Solving
Bibliographic record
Abstract
This paper presents the effect on reflective learning strategies towards the implementation of e-Portfolio to enhance learner higher order thinking skills. The purpose of the study was to examine the learner’s higher order thinking skills that focus on four factors which is research skills, analytical ability, creativity and problem-solving after the implementation of e-Portfolio in their learning. Initially, this paper was conducted a study with a total number of twenty-four students as a small group evaluation. The qualitative analysis was explored four factors which involved (1) research skills (2) analytical ability (3) creativity and (4) problem-solving to investigate the practicality of e-Portfolio in reflecting their learning. The findings were reported that learners reflective learning has a significant effect to create a self-confident, self-directed and retain their motivation at higher level. Reflective learning strategies will enforce the learner in gaining their interest in learning. The integration of e-Portfolio and reflective learning strategies will create an opportunity to enhance higher order thinking skills in teaching and learning for higher education environment.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".