Item Objective Congruence Analysis for Multidimensional Items Content Validation of a Reading Test in Sri Lankan University
Bibliographic record
Abstract
This paper presents the findings of a study that intended to seek the content validity (CV) evidence of an instrument to measure the reading ability of university students in Sri Lanka. The reading passages and items were adapted from CEFR aligned Learning Resource Network (LRN) materials. The items were designed based on the cognitive processing involved in completing each reading task as prescribed by Khalifa and Weir (2009). As a part of collecting evidence for content validation of the instrumentation, Item Objective Congruence (IOC) analysis is used in this study. In IOC, the congruence between the cognitive processing of reading and the test items were studied providing quantified data for CV. A pool of twelve experts examined a total of 41 test items against eight cognitive processing effectively. As the experts had chosen more than one objective for an item, the IOC formula simplified by Crocker and Aligna (1986) for multi-dimensional assessment of multiple combinations of skills was applied in the present study. The findings of the IOC indicate the experts’ varying degrees of agreement in terms of what some of the items were designed to assess. 38 items had acceptable IOC indices, one item was removed from the study and two items were modified. Items having high congruence show that they test only one skill and those indicating low congruence notify that, items assess more than one cognitive processing skill. The study demonstrates the utility of the IOC method in gathering evidence for CV. Test development and validation are crucial in assessment which is the first and foremost process to evaluate educational management.
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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 | 0.024 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".