A Polytomous Scoring Approach to Handle Not-Reached Items in Low-Stakes Assessments
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Bibliographic record
Abstract
In low-stakes assessments, some students may not reach the end of the test and leave some items unanswered due to various reasons (e.g., lack of test-taking motivation, poor time management, and test speededness). Not-reached items are often treated as incorrect or not-administered in the scoring process. However, when the proportion of not-reached items is high, these traditional approaches may yield biased scores and thereby threatening the validity of test results. In this study, we propose a polytomous scoring approach for handling not-reached items and compare its performance with those of the traditional scoring approaches. Real data from a low-stakes math assessment administered to second and third graders were used. The assessment consisted of 40 short-answer items focusing on addition and subtraction. The students were instructed to answer as many items as possible within 5 minutes. Using the traditional scoring approaches, students' responses for not-reached items were treated as either not-administered or incorrect in the scoring process. With the proposed scoring approach, students' nonmissing responses were scored polytomously based on how accurately and rapidly they responded to the items to reduce the impact of not-reached items on ability estimation. The traditional and polytomous scoring approaches were compared based on several evaluation criteria, such as model fit indices, test information function, and bias. The results indicated that the polytomous scoring approaches outperformed the traditional approaches. The complete case simulation corroborated our empirical findings that the scoring approach in which nonmissing items were scored polytomously and not-reached items were considered not-administered performed the best. Implications of the polytomous scoring approach for low-stakes assessments were discussed.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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