Digital School Examinations: An Educational Note of an Innovative Practice
Bibliographic record
Abstract
The aim of this study is was discuss the effectiveness of a digital school examination practice which was developed, and cultivated at a Norwegian university’s business school over a 9-year period. In this innovative practice, we intended to align the digital school examinations constructively into the course design, and we crafted examination questions and problems aiming to motivate students to acquire a deep learning approach. To hinder cheating on examinations where students brought their own devices, they worked with semi-indivual exam papers. The issues were common, but the students worked with specific data sets. Consequently, no solutions were equal. Empirical indications of effectiveness was derived from multiple sources: a survey, grade distributions, exam scores on question/problem types, and strings from the examination marking. The results show that students were satisfied using spreadsheets on the final school examination, which also motivated them to utilize a spreadsheet in their day-to-day learning activities. Moreover, we found it reasonable to affirm that semi-individual examinations hindered digital cheating.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".