Exploring Instructors’ Experiences with Instructional Design Supported Course Design in Higher Education: An Analysis of Three Cases Based on Activity Theory.
Bibliographic record
Abstract
Designing high-quality online courses requires specialized skills and knowledge that instructors may not possess alone. To address this challenge, universities employ instructional design professionals to support course design and development. However, it is important to recognize that instructors are significant in higher education course design. \nThis case study explores instructors’ experiences during the instructional design supported online course designing process. Fifteen instructors from two Canadian universities were interviewed. Three cases were selected based on the ID support modes to allow across-case comparison. \nThe key findings revealed that instructors designing online courses did not explicitly follow standard ID models. Instead, they prioritized adapting existing course content to suit their needs. When working with IDs, instructors valued ID’s expertise in course formatting and structures, and customized support, offering instructional strategies and digital tools for optimized online courses. Yet how often instructors implemented ID suggestions and practices was influenced by several other factors, including course goals, time constraints, previous teaching experiences, design task complexity, and ID support availability. The study also identified challenges in the current course design process, including balancing instructors’ workloads and desired effective course design, building pedagogical content knowledge in online course design and teaching, and bridging the gap between design needs and available ID supports. \nThis study provided an opportunity to understand ID-supported course design and how ID suggestions were implemented from instructors’ viewpoints. The results provided insights on how to improve ID support in higher education and help in better understanding the professional identity of instructional designers.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".