Rubric authoring tool for supporting the development an assessment of cognitive skills in higher education
Bibliographic record
Abstract
This paper explores a method to support instructors in assessing cognitive skills in their course, designed to enable aggregation of data across an institution. A rubric authoring tool, ‘BASICS’ (Building Assessment Scaffolds for Intellectual Cognitive Skills) was built as part of the Queen’s University Learning Outcomes Assessment (LOA) Project. It provides a workflow for assessment choices and generates an assessment rubric that can be tailored to individual needs based on user input. The dimensions and criteria in BASICS were adapted from the Valid Assessment of Learning in Undergraduate Education (VALUE) rubrics, and drew on annotations from over 900 work samples from the LOA project. This paper summarizes the development of the tool, and presents initial reliability and validity data from a pilot study. The pilot found that the BASICS developed rubric was consistent for the assessment of critical thinking and problem solving. The pilot compared assessment data derived from course Teaching Assistants with that of trained Research Assistants. Analysis found moderate intraclass correlation coefficients between the BASICS rubric and corresponding VALUE rubric dimensions, suggesting that the BASICS rubric aligned with the VALUE criteria. Preliminary findings suggest that BASICS is an effective tool for instructors to author rubrics, tailored to their own specifications for assessment of cognitive skills in a course. It is also promising as a method for aggregation of data across the institution. Researchers are conducting further investigation to evaluate the reliability of BASICS rubrics over multiple work samples from a range of disciplinary contexts.
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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 | 0.013 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".