Use of ePortfolios in EAP classes to facilitate self-efficacy through the improvement of creative, organizational, reflective, revision and technological skills
Bibliographic record
Abstract
Abstract EPortfolios have long been associated with encouraging learners to develop learner autonomy by helping them document and reflect on their learning processes and developing suitable learning strategies. While there is plentiful research in this area, it is still unclear the specific ways in which learners feel that maintaining an ePortfolio can help develop their reflective practices, and implementation of learning strategies. To address this, we surveyed and interviewed learners on an International Foundation Programme regarding their year-long use of ePortfolio practices to ascertain which skills and abilities they felt they developed, and in what ways this was of use to them in their current and future learning journey. The results section synthesizes survey and interview data to represent the students’ voices and their feelings regarding the creation and maintenance of an ePortfolio, and the development of skills. The thematic analysis of the data suggests that students feel that the use of an ePortfolio is challenging but that it facilitates self-efficacy through the improvement of creative, organizational, reflective, revision and technological skills. The discussion and conclusion present ideas about how these practices can be developed in the future, and how other practitioners may decide to implement an ePortfolio in their own context.
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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 | 0.004 | 0.012 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".