Personalised-adaptive learning - an operational framework for developing competency-based curricula in computer information technology
Bibliographic record
Abstract
In this paper, we explore the intersection of grounded theory in cognition and learning with the operational frameworks needed to develop and evaluate adaptive learning systems. As a test case, we studied an online personalised competency-based CIT curriculum at Northern Arizona University (Flagstaff, Arizona, USA). Our approach focused on strategies for adding adaptive learning capacities to an extant learning management system, with particular attention to cost-effective yet evidence-based approaches for improving learning outcomes. We designed elements that would enhance feedback and remediation for students, which required developing software engines that could integrate data collection and analysis. Such capacities are essential to drive evidence-based educational practices for CIT undergraduate and graduate programmes. Research led to a conceptual model and the operational facets for personalised-adaptive learning CIT educational environments. The conceptual and operational model described herein is called SIGNAL CIT Education - Serial Integration of Guiding Nodes for Adaptive Learning in CIT 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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".