Creating an Experiential Learning Based Multi-Disciplinary Program
Bibliographic record
Abstract
For many years, curriculum development has considered learning outcomes at the program level largely via learning outcomes at the course level. Some programs have modified their designs to use different structures such as condensed courses or project based learning. Recently, there has been an increased interest in experiential learning as a way to facilitate student acquisition of real-world applicable capabilities while enhancing student learning of ‘soft skills’ such as professionalism, communication, and team management. Historically, such engagement including complexities of real-world problems has been accomplished through internships, co-op, capstone courses, or project based learning. In this paper we present an innovative model for experiential curriculum design based on skill requirements and learning outcomes derived from industry needs combined with technology enabled learning. The curriculum has been designed in a highly modular approach to ensure flexibility in student learning pathways to meet the requirements of the work related learning projects that are integrated as part of the program design. The conceptual model of this approach to curriculum design will be presented through a case study of the development of the informatics program at UOIT. Areas of caution are explored to identify recommendations for risk mitigation when developing a program utilizing this type of learning environment. In particular, student selection, technical infrastructure requirements, learning outcome measurement, faculty scheduling, and program management are considered.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".