Investigation of Rater Tendencies and Reliability in Different Assessment Methods with Many Facet Rasch Model
Bibliographic record
Abstract
One of the most commonly used methods for measuring higher-order thinking skills such as problem-solving or written expression is open-ended items.Three main approaches are used to evaluate responses to open-ended items: general evaluation, rating scales, and rubrics.In order to measure and improve problem-solving skills of students, firstly, an error-free measurement process should be performed.Errors caused by raters such as bias, high or low tendency to score is a common problem in the evaluation of open-ended items as they adversely affect the accuracy of decisions to be made.This study utilized open-ended items to evaluate the raters' tendencies in terms of general evaluation, rating scale, and rubric conditions.The raters' behaviours in each assessment method and their opinions about the assessment methods were determined.The participants of the study consisted of 12 different mathematics teachers and the Many Facet Rasch Model was adopted for the analyses.The scoring reliability of each method was estimated.The findings of the rating scale revealed that the raters had a more homogeneous scoring tendency.In addition, while the majority of raters stated that they prefer to use a rubric, they also stated it is the most difficult method to use.
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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 | 0.160 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".