Student perspectives on assessment for learning
Bibliographic record
Abstract
ABSTRACT Assessment for learning (AfL) has become a widespread approach across many educational systems. To date, AfL research has emphasized teachers’ knowledge, skills, and practices, with few studies examining students’ responses to an AfL pedagogical approach. The purpose of this research was to focus directly on students’ perspectives on their use and value of AfL approaches through a survey of 1079 K–12 students and portfolio‐based interviews with 12 purposefully selected students. Survey data were analyzed through descriptive and inferential statistics across grade levels. Interview data were analyzed using standard thematic coding processes. Students most frequently used and valued teacher feedback and success criteria to support their learning. Peer feedback was the least valued AfL approach for all students. Some significant differences between grade levels were noted. Our results suggest that using AfL approaches is a learned behaviour; students need to be explicitly taught about AfL concepts, terminology, and use over time. This study also highlights that AfL implementation requires sustained focus, research, and support in schools and classrooms for students to value and fully benefit from assessment‐based teaching.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".