Teachers’ assessment of self-regulated learning: Linking professional competences, assessment practices, and judgment accuracy
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".