The User Experience Design for Learning (UXDL) Framework: The Undergraduate Student Perspective
Bibliographic record
Abstract
The User Experience Design for Learning (UXDL) Honeycomb is an online learning design framework aimed at creating valuable online learning experiences, which some post-secondary institutions have started to use to guide the design of their online courses. While each of the principles are supported by psychological research, this framework has not been directly validated or corroborated by the student experience. The present study aims to address whether the UXDL framework aligns with students’ preferences, beliefs, and behaviours in online learning in a post-secondary context. This research adds to the growing literature on students’ preferences, beliefs, and experiences in online learning, focusing specifically on second-year Canadian undergraduate students at a mid-sized, research-intensive university. Using a three-pronged methodological approach, we explore not only students’ implicit beliefs (via open-ended surveys, N = 805), but also their experiences (in-depth interviews, N = 36), and impressions and behaviours while working in an online course (two user experience sessions, N = 36). Our qualitative analyses of these data reveal 4 prominent themes in online design that students find particularly valuable: (a) Accessible: flexible; (b) Useful: modes of design and delivery, (c) Intuitive: usable and findable, and (d) Desirable: affective design and humanizing learning.
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.
How this classification was reachedexpand
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.036 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".