The Inculcation of Critical Reflection through Reflective Learning Log: An Action Research in Entrepreneurship Module
Bibliographic record
Abstract
Purpose–This study is part of the Scholarship of Teaching and Learning (SoTL) grant to examine written reflective learning log among the students studying BPME 3073 Entrepreneurship in UUM.Method–The data collection techniques is researcher-directed textual data through reflective learning log; obtained from students of one hundred forty. A qualitative thematic method was employed to examine the verbatim format of students’ reflections.Findings–The empirical results found out majority of students have little academic experience in writing a piece of reflective textual learning log. Resultant in their incapability’s to reflect critically and thus; unable to translate their thinking in the written format. Data collected from three classes of Entrepreneurship registered fifty five percent (75 logs) of learning log as Level 1 (descriptive writing); therefore they were not suitable for further analysis. The remaining 61 reflective learning logs were used for the textual data examination. The students’ reflections on their Entrepreneurship’s experience can be categorize systematically into four different themes comprised of (1) The nature of Entrepreneurship module, (2) Entrepreneurial characteristics, (3) Opportunity recognition, and (4) Creativity & innovation.Significance–The results of this research have strongly suggested the need to urgently develop among the students the skills in writing reflectively as they go through the process of higher education which is useful in molding their future professional and entrepreneurial behavior as when they entered the job market which requires a critical reasoning ability.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".