Comparative Study on Validity of Paper-Based Test and Computer-Based Test in the Context of Educational and Psychological Assessment among Arab Students
Bibliographic record
Abstract
A prerequisite to evolving the concept of computerized assessment method requires a comparable test scores data based on the comparative study scores of both paper-based test (PBT) and computer-based test (CBT) methods. Previous research studies reported that even though both testing types show significant commonalities in many areas of technical and academic concerns, still there are substantial variances found in test scores. Consequently, in various educational assessment systems this significant inconsistency raises serious question on the rationale of substituting PBT with CBT. This is imperative to compare the both testing modes and to provide a concrete base for this replacement. This research study was aimed at implementing an achievement test and a motivation level checking questionnaire in order to test the effectiveness and validity of the CBT and to measure the level of student motivation that directly controls the performance and results. Researcher aims to get some valid data to provide a solid base for the effective use of CBT in academic and placement modes. Results showed that (a) the pretests improved the results by providing experience for the tests themselves (b) participants in CBT group showed better test performance. In fact, CBT is known to be an efficient tool for assessment.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".