Bibliographic record
Abstract
This paper investigates barriers for using course evaluation as a tool for improving student learning, through the analysis of course evaluation practices at The Royal Institute of Technology (KTH), a technical university in Stockholm. Although there is a policy on development‐focused course evaluation at KTH, several stakeholders have expressed dissatisfaction with its poor results. Interviews were conducted with faculty and student representatives to investigate the perceived purpose and focus of evaluation and its current utilization. Results show that evaluation is teaching‐ and teacher‐focused. As course development is not in the foreground, evaluations merely have a fire alarm function. It is argued that course evaluation should be regarded as a component of constructive alignment, together with the intended learning outcomes, learning activities and assessment. Finally, the concept system alignment is proposed, extending constructive alignment to the institutional level. The evaluation task can generally be said to be: to describe what actually happens in that which seems to happen to tell why precisely this happens, and to state the possibilities for something else to happen. (Franke‐Wikberg & Lundgren, 1980 Franke‐Wikberg, S. and Lundgren, U. P. 1980. Att värdera utbildning Del 1, En introduktion till pedagogisk utvärdering, Stockholm: Wahlström & Widstrand. [Google Scholar], p. 148)
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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 | 0.049 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".