Methodologies for Investigating and Interpreting Student–Teacher Rating Incongruence in Noncognitive Assessment
Bibliographic record
Abstract
Abstract Numerous studies merely note divergence in students’ and teachers’ ratings of student noncognitive constructs. However, given the increased attention and use of these constructs in educational research and practice, an in‐depth study focused on this issue was needed. Using a variety of quantitative methodologies, we thoroughly investigate student–teacher in congruence with two commonly assessed noncognitive constructs: intrinsic motivation and time management. We present ways to describe, visualize, and predict differences between student and teacher ratings and discuss implications for interpretation. We show how descriptive and predictive analyses that consider the nesting of students within teachers expand our understanding of the incongruence. We demonstrate the importance of considering ancillary variables in predictive analysis, and latent variable methods for comparing measurement models. We found that student and teacher factors exhibited only small‐to‐moderate correlations, reinforcing the need for more measurement research in this area. Further, we report that teachers tended to rate students more favorably than students rate themselves, and teachers’ ratings were more related to student performance. We discuss how these methodologies can be used to better understand the incongruence between students and teachers and how they can be incorporated into construct validation studies.
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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 | 0.137 | 0.363 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".