Teachers’ assessment of self-regulated learning: Linking professional competences, assessment practices, and judgment accuracy
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
Abstract Self-regulated learning (SRL) is crucial for successful lifelong learning and an important educational goal. For students to develop SRL skills, they need appropriate SRL support from teachers in the classroom. Teachers, who are aware of their students’ strengths and weaknesses in SRL, can promote SRL more adaptively. This requires teachers to assess students’ SRL skills accurately. However, there is little research on teachers’ diagnostic competences in SRL. To address this research gap, the present exploratory study investigates teachers’ content knowledge about SRL, assessment activities, and accuracy in judging their students’ SRL. Furthermore, the study examines whether teachers’ characteristics and competences in SRL are associated with the accuracy of their judgments. The study included 41 lower secondary school teachers and their 173 students. The students completed metacognitive knowledge tests on several SRL skills while the teachers made predictions about the students’ metacognitive knowledge of those SRL skills. The results indicate that not all teachers were familiar with the assessment of SRL. Moreover, teachers exhibited greater familiarity with offline assessments of SRL than online assessments and a noteworthy proportion of teachers employed assessment activities that were not diagnostic of SRL. Low correlations between students’ actual test scores and teachers’ judgments generally revealed low accuracy for teachers in assessing their students’ metacognitive knowledge of various SRL skills. Teachers’ characteristics and competences in SRL were mainly uncorrelated with their judgment accuracy. Overall, these results highlight the need for further attention and support for teachers in developing their diagnostic competences in SRL.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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