A Learning, Research and Development Framework to Design for a ‘Holistic’ Learning Experience
Bibliographic record
Abstract
The design of experiences and, in particular, educational experiences is a complex matter and involves not only using effective technologies and applying cognitive triggers, but there is a need to think outside the box in order to also design for the affective dimension of human experiences; the impressions, feelings and interactions that a learner might/could have with the online content and technology. The purpose of this article is to delve deep into this complex entity and, in doing so, to identify how one might approach designing for ‘holistic’ educational experiences. The article presents a case study describing the journey of a group of learning technologists and educators through the design and development phases of an action research online ABCD module, and it highlights the learner's experiences. It discusses the development of a learning, research and development framework to support the ABCD learning experience and, in particular, what was actually required to undertake the design for this learning experience. In summary, the article reports on a learning, research and development framework that provides solutions and support to a number of aspects involved in the design of holistic learning experiences and, in particular, the often neglected, yet complex, issues around experience design.
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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 | 0.039 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".