Exploring the Usefulness of Kelly's Personal Construct Theory in Assessing Student Learning in Science Courses
Why this work is in the frame
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Bibliographic record
Abstract
We explore the utility of George Kelly's Personal Construct Theory, specifically his repertory grid technique, to the assessment of student learning in undergraduate science courses. We provide an in-depth review of the assumptions underlying Personal Construct Theory and how these were reflected in the repertory grid technique Kelly developed. We explain how an adapted version of the repertory grid, sharing some yet not all of Kelly's assumptions, was utilised as a research tool in a recent study involving science instructors and their students. We argue that as well as having applicability as an innovative research tool, an adapted version of Kelly's repertory grid is a useful heuristic for university teachers when used as a classroom assessment technique (CAT) and indicate several features it shares with the more widely-known conceptual mapping technique, which has been used in the study of science teaching and learning for many years. We conclude by highlighting several advantages the use of repertory grids has for both students and instructors.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it