E-portfolios rescue biology students from a poorer final exam result: Promoting student metacognition
Bibliographic record
Abstract
E-portfolios have the potential to transform students' learning experiences. They promote reflection on the significance of what and how students have learned. Such reflective practices enhance students' ability to articulate their knowledge and skills to their peers, teachers, and future employers. In addition, e-portfolios can help assess the ability of teachers and institutions to inculcate students with their core learning objectives and skills. In 2012/13, I piloted the use of an e-portfolio assignment in a sophomore molecular cell biology course to determine whether it could enhance student learning. My pilot assignment found: 1. The e-portfolio rescued students from a poorer final exam result relative to their midterm exam - students who did not complete the e-portfolio assignment had a greater probability of performing more poorly on the final relative to the midterm exam (p = 0.004); 2. E- portfolios can enhance student engagement; 3. Google Sites works well as an e-portfolio platform; 4. Instructors do not need to be technical experts when the e-portfolio platform is embedded in students' everyday digital life; 5. Instructors are able to focus on developing students' learning outcomes associated with e-portfolio assignments when e-portfolios are so embedded; 6. Students may choose whichever e-portfolio platform they prefer, needing only to submit a URL to their e-portfolio for grading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".