A Critical Review of Portfolio Assessment as an Alternative Tool in English Language Teaching Classrooms
Bibliographic record
Abstract
Recent developments and issues in the education has brought radical changes in the way learners’ and teachers alike need to reconsider about assessments, particularly in ESL/EFL learning and teaching classrooms. As such, alternative assessment tools are meant to be worked out as solutions over traditional approaches, so that learners are truly assessed for the calibre of work that is produced by them. Hence, this paper will delve upon portfolio assessment as an alternative tool to gauge learners’ true potential over traditional testing methods. The paper has critically reviewed about portfolio assessment under five sub-sections with discussions about portfolio assessment in ESL/EFL teaching and learning being first, followed by the types of portfolio assessments as the second item. Then, models and implementation of portfolio assessment in the ESL/EFL classrooms next, with merit and demit points of portfolio assessment being the fourth and fifth items to be discussed respectively. The critical review is summed up by providing some recommendations and concluding remarks for the whole piece.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".