Impact of Outcome-Based Assessment on Student Learning and Faculty Instructional Practices
Bibliographic record
Abstract
Increased accountability has been a catalyst for the reformation of curriculum and assessment practices in postsecondary schools throughout North America, including veterinary schools. There is a call for a shift in assessment practices in clinical rotations, from a focus on content to a focus on assessing student performance. Learning is subsequently articulated in terms of observable outcomes and indicators that describe what the learner can do after engaging in a learning experience. The purpose of this study was to examine the ways in which a competency-based program in an early phase of implementation impacted student learning and faculty instructional practices. Findings revealed that negative student perceptions of the assessment instrument's reliability had a detrimental effect on the face validity of the instrument and, subsequently, on students' engagement with competency-based assessment and promotion of student-centered learning. While the examination of faculty practices echoed findings from other studies that cited the need for faculty development to improve rater reliability and for a better data management system, our study found that faculty members' instructional practices improved through the alignment of instruction and curriculum. This snap-shot of the early stages of implementing a competency-based program has been instrumental in refining and advancing the program.
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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 | 0.061 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".