Teaching Software to Diverse UX Design Cohorts: From Flipped Classrooms to Computer-Based Scaffolding
Bibliographic record
Abstract
This paper documents an ongoing curriculum building project at the University of Toronto centered on scaffolding software skill acquisition within a user experience (UX) design classroom. The expansion of UX design as a formal discipline of study has yielded larger and more diverse cohorts of students, many of whom do not hail from STEM or design backgrounds. This shift presents the pedagogical challenge of how to effectively teach relevant software to students who vary vastly in baseline technological proficiency in classroom sizes of 60+, where one-on-one support is at a premium. This project focuses on annual iterations of a required course on user interface design in a UX-aligned professional master's program. This course tasks students with learning Figma, an industry-standard application for designing and prototyping user interfaces. Drawing from existing pedagogical theories, our work aims to improve Figma skill acquisition by adapting a flipped classroom approach that has been successfully used in computer science, alongside associated forms of computer-based scaffolding. In this paper, we summarize progress developing instructional materials for students since 2020, discuss opportunities for computer-assisted evaluation, and introduce a prospective plugin for Figma that could holistically scaffold skill acquisition while providing UX educators with timely and detailed learning metrics.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".