Evaluating the Validity of the Student Perspectives of Teaching Survey: A Network Psychometrics Approach
Bibliographic record
Abstract
Higher education institutions commonly employ student evaluation of teaching (SET) instruments (e.g., course evaluation surveys) to enhance course quality and inform instructional strategies. However, conceptualizing and measuring SET as a unidimensional construct may compromise validity, particularly when represented by a single aggregated score. This study uses a network psychometrics approach to explore the validity of a new instrument that acknowledges the multidimensional nature of SET as an educational construct. The central research question is, “How is the robustness of a multidimensional students’ evaluation of the teaching survey?”. The study sample consists of 649 undergraduate students from a western Canadian university who completed a multidimensional SET instrument. The instrument consists of six subscales corresponding to six aspects of SET (i.e., design, utility of course resources, graded work, course delivery, instructional approach, and class climate). The findings revealed a robust line of evidence that supports the validity of the instrument’s interpretation and usage. This was demonstrated through a high coefficient alpha, good network model fit, and stable survey structure. The study provides evidence supporting the use of a multidimensional SET instrument and offers novel validity support via the structural evidence provided by network 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 | 0.124 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".