Rasch Model Analysis on the Effectiveness of Early Evaluation Questions as a Benchmark for New Students Ability
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".