The nature and impact of teachers’ formative assessment practices
Bibliographic record
Abstract
Theory and research suggest the critical role that formative assessment can play in student learning. The use of assessment in guiding instruction has long been advocated: Through the assessment of students’ needs and the monitoring of student progress, learning sequences can be appropriately designed, instruction adjusted during the course of learning, and programs refined to be more effective in promoting student learning goals. Moving toward more modern pedagogical conceptions, assessment moves from an information source on which to base action to part and parcel of the teaching and learning process. The following study provides food for thought about the research methods needed to study teachers’ assessment practices and the complexity of assessing their effects on student learning. On the one hand, our study suggests that effective formative assessment is a highly interactive endeavor, involving the orchestration of multiple dimensions of practice, and demands sophisticated qualitative methods for study. On the other, detecting and understanding learning effects in small samples, even with the availability of comparison groups, poses difficulties to say the least. 1 Paper Prepared as part of Symposium Building Science Assessment Systems That Serve Accountability and Student Learning: The CAESL Model for the annual meeting of the American Education Research Association, Montreal, Canada, April 2005 2 The authors would like to thank Stephen Zuniga and Sam Nagashima, graduate students at CRESST, UCLA for their help with data analysis.
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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 | 0.140 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".