Teachers’ beliefs and attitudes towards students’ self assessment: A latent profile analysis
Bibliographic record
Abstract
Autonomous lifelong learning has been identified as a global competency for 21st century education. Students’ self assessment (SSA) plays significant role in achieving this competency. Understanding teachers’ beliefs and attitudes towards SSA is fundamental in promoting SSA in the classroom. The overarching aim of this study was to understand Ghanaian teachers’ beliefs and attitudes towards SSA. Employing a cross-sectional survey design, 248 basic and senior high school teachers participated in this study. A four-factor structure was identified to explain teachers’ beliefs about SSA (i.e., positive belief about self assessment, developing students’ self assessment capacity, negative beliefs about self assessment, and confidence in students’ capacity). Based on these four factors, a latent profile analysis identified five distinct groups of teachers who have varying beliefs about SSA within the Ghanaian educational context. Although some of the teachers in our study strongly believe that SSA is a useful assessment and learning tool that could help students reflect, monitor their own learning, and promote autonomous lifelong learning, most of the teachers either have no interest in SSA, or perceived students as not supporting effective teaching and learning. Implications for policy and practice have been discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".