OVERCOMING OBSTACLES TO IMPLEMENTING AN OUTCOME-BASED EDUCATION MODEL: TRADITIONAL VERSUS TRANSFORMATIONAL OBE
Bibliographic record
Abstract
Attempts to introduce a new outcome- based curriculum in the Mechatronic Systems Engineering (MSE) Program at Simon Fraser University (SFU) have posed a range of challenges to teaching staff and students in terms of the most effective and efficient means for transitioning the program and achieving the expected improvements in educational outcomes. However, the mechanical process of pursuing outcomes without the deliberate revision of the pedagogy, attitudes and forms of assessments fails to attain the continuous improvement concept that OBE implies. This paper analyses MSE faculty interview responses to approaches they incorporate in their teaching practices and the effect these practices have on student learning. Class size, expectations of learner characteristics and reality, teaching practice and evaluation, and student motivation were the most commonly discussed challenges. Self-reported instructor characteristics and the perceived role of the instructor often contradicted the OBE model of learning. The results inform a critical discussion of the pedagogical aspects involved in reshaping existing curriculum to satisfy the needs of the 21st century learner. The process of transitioning from the content-driven to the outcome-based curriculum is revealing opportunities in terms of transformative teacher education as well as challenges that warrant further analysis.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".