APPRECIATIVE INQUIRY: DESIGNING FOR ENGAGEMENT IN TECHNOLOGY-MEDIATED LEARNING
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generating and sustaining engagement should be an explicit element of technology- mediated 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 (AI), a process that seeks to discover and build on what works well in existing systems. An evaluation study of the protocol, conducted at a bank learning centre, confirmed that the protocol is 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it