EBD-enabled Approach to Improving the Efficiency of Developing Information Literacy Assessment Criteria
Bibliographic record
Abstract
This study proposed an Environment-Based Design (EBDEA) to develop a draft of information literacy assessment criteria (ILAC), to improve the efficiency of developing ILAC. The approach is validated using two methods. Firstly, a case study is conducted to create ILAC for K-12 students by the EBDEA, resulting in four first-tier and 21 s-tier criteria. These were compared with the ILAC from the International Association for Evaluation of Educational Achievement (IEA). The comparison revealed a high degree of consistency between the two sets of ILAC, with the EBDEA-generated ILAC including several additional items that are integral to the criteria but absent in IEA's version. Subsequently, expert evaluation was employed to affirm the effectiveness of the EBDEA, with the majority of experts expressing satisfaction with the ILAC developed via this method. The findings indicate that EBDEA is an efficient approach for developing ILAC, requiring less time and fewer human resources.
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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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".