Relationships between Missing Response and Skill Mastery Profiles of Cognitive Diagnostic Assessment
Bibliographic record
Abstract
This study explores the relationship between students’ missing responses on a large-scale assessment and their cognitive skill profiles and characteristics. Data from the 48 multiple-choice items on the 2006 Ontario Secondary School Literacy Test (OSSLT), a high school graduation requirement, were analyzed using the item response theory (IRT) three-parameter logistic model and the Reduced Reparameterized Unified Model, a Cognitive Diagnostic Model. Missing responses were analyzed by item and by student. Item-level analyses examined the relationships among item difficulty, item order, literacy skills targeted by the item, the cognitive skills required by the item, the percent of students not answering the item, and other features of the item. Student-level analyses examined the relationships among students’ missing responses, overall performance, cognitive skill mastery profiles, and characteristics such as gender and home language. \nMost students answered most items: no item was answered by fewer than 98.8% of the students and 95.5% of students had 0 missing responses, 3.2% had 1 missing response, and only 1.3% had more than 1 missing responses). However, whether students responded to items was related to the student’s characteristics, including gender, whether the student had an individual education plan and language spoken at home, and to the item’s characteristics such as item difficulty and the cognitive skills required to answer the item. \nUnlike in previous studies of large-scale assessments, the missing response rates were not higher for multiple-choice items appearing later in the timed sections. Instead, the first two items in some sections had higher missing response rates. Examination of the student-level missing response rates, however, showed that when students had high numbers of missing responses, these often represented failures to complete a section of the test. Also, if nonresponse was concentrated in items that required particular skills, the accuracy of the estimates for those skills was lower than for other skills. \nThe results of this study have implications for test designers who seek to improve provincial large-scale assessments, and for teachers who seek to help students improve their cognitive skills and develop test taking strategies.
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How this classification was reachedexpand
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 | 0.004 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".