Attitudes Toward and Implications of the Computer-Based Exams at Arab American University of Palestine
Bibliographic record
Abstract
We examine the computer-based exams system at Arab American University-Palestine in terms of teacher and student attitudes as well as the quality of the test items. A three-pronged approach to data collection was used. First, we elicited answers to questionnaires from 704 faculty and student respondents. Second, we conducted eight individual interviews with students and instructors, as well as three focus groups—each comprising 8–10 students—from different majors in the university. Third, we had access to the records of the registrar on the grades of students in different years prior to and after introducing the system. We utilized descriptive statistics to examine the quantitative data and qualitative methods to analyze the interviews. The results suggested that the attitudes of instructors and students, as well as the quality of the exams were not adequately considered by AAUP when it introduced the computer-based exams system. We found significant differences between grades prior to and after adopting the system; yet the changes are not necessarily positive, at least from an academic point of view.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".