Developing Intentional Learning Skills Among E-Commerce Student—Action Research in Metacognition Through Reflective Learning Log
Bibliographic record
Abstract
Intentional learners are self-directed people who take charge of their education, whether in a setting or an informal setting. Learners who practice intentional learning skills choose their learning methodologies and organize their studies in accordance with their interests, preferences, and speed. This study embarks on the following objectives: (i) To look into ways how to scaffold intentional learning experiences among the student and ritualize the intentional learner’s mindsets and best practice skills, (ii) To investigate the effects (through a reflective learning log) of intentional learning skill of student on their study habits and attitudes toward learning, and, (iii) To examine their critical reflection log while they are studying E-commerce module which is underpinned by the intentional learning paradigm and Hatton and Smith’s framework. In this qualitative study, data from 140 students in three classes of E-Commerce were required to write reflections that would be used to determine final grades using the reflective learning log. In order to support students’ purposeful learning development as they join the job market, the study’s findings strongly imply that they urgently need to strengthen their reflective writing skills while enrolled in higher education. The undergraduate must also be given the fundamental tools necessary to shape them into purposeful learners through exercises in reflective learning. The Hatton and Smith reflective framework worked well for categorizing written reflections and making the reflective learning log evaluation less subjective. This study brought to light the fact that many business students were unfamiliar with the genre of reflective writing and that this genre needed to be explicitly taught in the relevant course. Hence, to support students’ purposeful learning development as they join the job market, the study’s findings strongly imply that they urgently need to strengthen their reflective writing skills while enrolled in higher education.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".