Analysis of the Process and Outcomes of Eportfolio Development to Recognise Formal Learning in Zimbabwe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The process of developing electronic teaching portfolios can be used to evaluate the teacher’s competency and guide a long-term professional development. This paper addressed the issue of assessment that is linked to the demand for accountability and standards through use of ePortfolio system. The ePortfolio system is then used as an authentication measure for students’ work. The study survey is based on two groups selected from a local university; one group used paper portfolio and the other used electronic portfolio. Data was then analyzed from these two perspectives and digital story telling. EPortfolio development involved defining goals and context of the case, the collection of artifacts, selecting relevant information, showing a reflection and a projection of how the results are produced. Results showed that ePortfolios can be easy to be designed and implemented as a learning tool.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it