APPRECIATIVE INQUIRY: DESIGNING FOR ENGAGEMENT IN TECHNOLOGY-MEDIATED LEARNING
Bibliographic record
Abstract
Generating and sustaining engagement should be an explicit element of technologymediated learning (TML) design for adults.Yet, little related guidance exists for practitioners in this field.This thesis investigates design elements that sustain engagement and describes a workshop protocol to help practitioners address engagement in their own context.The protocol and thesis are each framed as an Appreciative Inquiry (Al), a process that seeks to discover and build on what works well i n existing systems.An evaluation study of the protocol, conducted at a bank learning centre, confirmed that the protocol i s viable; participant designers created several engagement strategies.However, the findings also indicate that engagement was not a priority for participants and suggest that practitioners could benefit from a deeper understanding of engagement design.Finally, the thesis offers engagement design guidelines that advocate using: cognitive conflict, challenge, relevance, goals, experiential learning, interactivity, control, support, collaboration, uninterrupted time and fun.
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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 | 0.038 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".