Digital Examination in Higher Education--Experiences from Three Different Perspectives.
Bibliographic record
Abstract
Assessment through new technology has gained a firm foothold within the university system in the last decade. This paper summarizes the experiences that have been made during the introduction of digital examination over the past two years at the Royal Institute of Technology in Stockholm, Sweden. The experiences are divided between three different perspectives; the teachers, the students and the administrators. From the teachers perspective the experiences have been very positive – less time have been allocated to grading written exams, the grades are perceived as more just and the saved time can be spent on increasing the quality on other parts of the courses. From a student perspective the experiences have been very positive as well – most students are enjoying the fact that they get the results much quicker, that they can edit their answers on the exam easier and that the grades are more just. The experiences from the final perspective – that from the administrators’ point of view – are far more complex. Some parts of the administrative system encouraged the introduction of digital examination, whereas other parts tried to stop it, using different measures. The paper concludes with some advice on implementing changes in written exams, based on the experiences from the Swedish case.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".