Constructing a Design Framework and Pedagogical Approach for Adaptive Learning in Higher Education: A Practitioner's Perspective
Bibliographic record
Abstract
While adaptive learning is emerging as a promising technology to promote access and quality at a large scale in higher education (Becker et al., 2018), the implementation of adaptive learning in teaching and learning is still sporadic, and it is unclear how to best design and teach an adaptive learning course in a higher education context. As early adopters, a team of instructors, instructional designers, and administrators at the University of Central Florida (UCF) identified five key design features as an adaptive learning design framework to guide the unique course design process. These five features involve deliberate design and development efforts that could bring significant benefits to student learning. The purpose of this field note is to present a design framework and best practices for teaching from both a systems and a pedagogical approach in the context of implementation at UCF. We also share the rationale and classification framework UCF has adopted to ensure the term “adaptive learning” is universally understood across campus. This paper offers insights into the design, delivery, and implications of utilizing adaptive learning systems in higher education courses at a public research university and attempts to capture the intimacy of lessons learned and best practices gathered since the project’s inception in 2014.
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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 | 0.073 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".