Methodologies for Investigating and Interpreting Student–Teacher Rating Incongruence in Noncognitive Assessment
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 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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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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