The role of task repetition and learner self-assessment in technology-mediated task performance
Bibliographic record
Abstract
Abstract This study examines the impact of task repetition on second language learners’ task performance and the mediating role of teacher feedback and learner self-assessment on oral performance. The study was conducted in a university-based English for Academic Purposes (EAP) program, where, as part of a course, intermediate proficiency learners (n = 52) were tasked with preparing and delivering a technology-mediated oral presentation (i.e., task) on a topic of their choice. First, they presented the task to the whole-class, reflected on their performance in terms of language and format quality, and received teacher’s feedback. Four weeks later, they produced a second recording and reflected on it again. A comparison group (n = 26) also delivered a presentation before a class but did it once, without reflection or teacher feedback. Both groups used technology to prepare, deliver, and document their presentations. The recordings were rated on six rubric-determined traits by the teacher and an independent rater, and the scores were compared between groups. To determine the effects of self-assessment, coupled with teacher feedback, on task repetition, learners’ written reflections and teacher’s comments were analyzed using discourse coding techniques. The results revealed benefits for task repetition and self-assessment during the performance of the same task for the experimental group, confirming the importance of task repetition in EAP contexts and the need for continuous and teacher-supported learner self-assessment in learner task performance and outcome.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".