Rasch Model Analysis on the Effectiveness of Early Evaluation Questions as a Benchmark for New Students Ability
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses the effectiveness of the early evaluation questions conducted to determine the academic ability of the new students in the Department of Electrical, Electronics and Systems Engineering. Questions designed are knowledge based - on what the students have learned during their pre-university level. The results show students have weak basic knowledge and this is in contrast to the results obtained during the application for admission to Year 1 of university. Thus, early evaluation questions were implemented to see the relevance in assessing the student's ability, obtained by the use of Rasch analysis, WinSteps. The findings show that the initial assessment is an effective and appropriate method to assess the ability of students, where the Cronbach-? is 0.69 and achieve the acceptable ranges of PT-Measure, Mean Square Outfit or Outfit Mean Square (MNSQ) and z-standardized values (ZSTD) Outfit. This shows that Rasch analysis can be used to classify the questions and the students according to their performance level accurately and thus, reveal the true level of the students’ ability, despite the small number of samples.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it