Instructional Designers and Their Use of Self-Regulated Learning Practices in Higher Education
Bibliographic record
Abstract
Instructional Designers are informal leaders in higher education and play a critical role in maintaining the high quality of education expected from these institutions. Yet, there is minimal research on Instructional Designers and their integral role in higher education. The purpose of this study was to investigate how Instructional Designers use self-regulated learning (SRL) strategies in their practice. The study used a qualitative approach with multiple methods – semi-structured interviews and review of an online learning module. Five Instructional Designers at a university in Ontario, Canada were interviewed to gain insights about their perceptions of SRL and their use of SRL strategies in instructional materials. Participants reviewed an online learning module to demonstrate and apply their knowledge of SRL, identifying features within the module that would prompt SRL skills in students. Results of the study suggest that Instructional Designers have some form of declarative and procedural knowledge of SRL. Participants described SRL as critical for students in terms of designing personalized learning plans, becoming lifelong learners, and developing critical thinking and problem-solving skills. Participants incorporated SRL into their work through the design of courses and of online learning modules. In addition, participants were able to identify various features within the online learning module that would prompt SRL in students. However, this knowledge of SRL is not the result of instructional design training, but other educational and professional endeavors undertaken before entering the profession (e.g., Bachelor of Education, Master of Education, independent professional development). Most Instructional Designers use their professional experiences, intuition, observations of what they have seen others do, and past experiences as a learner in combination with learning theories and SRL strategies to design courses and instructional materials in higher education.
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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 | 0.058 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".