Formative assessment strategies used in the University of Northern British Columbia School of Education
Bibliographic record
Abstract
This study explores the perspectives of professors and instructors using formative assessment strategies in the classroom. A qualitative phenomenology was used to utilize the data from nine (n=9) questionnaires and three (n=3) in-depth semi-structured interviews with UNBC School of Education professors and instructors. The questionnaire and the interview questions regarding the use of formative assessment strategies were drafted based on the strategies identified by Black and Wiliam (1998). The findings from the questionnaire revealed that professors and instructors were aware of the purpose of assessment, the importance of student-focused assessment, and the various ways of implementing formative assessment. Additionally, the interviews showed that professors and instructors were aware of the importance and impact of formative assessment when implemented in teaching and learning, which, in turn, could move students' learning forward by providing effective and continuous feedback. The findings from this research can increase understanding of assessment in post-secondary settings and may benefit educators who implement formative assessment practices, through continuous and regular professional development (Brancato, 2003). --Leaf ii.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".