Uncovering Instructors’ Diverse Practices and Perceptions: A Field Deployment of a Customization-Sharing Platform that Supports Course Management
Bibliographic record
Abstract
Instructors regularly learn and customize various feature-rich software applications to meet their unique classroom needs. Although instructors often prefer social help from colleagues to navigate this complex and time-consuming learning process, it can be difficult for them to locate relevant task-specific customizations, a challenge only exacerbated by the transition to online teaching due to COVID-19. To mitigate this, we explored how instructors could use an example-based customization sharing platform to discover, try, and appropriate their colleagues’ customizations within a learning management system (LMS). Our field deployment study revealed diverse ways that ten instructors from different backgrounds used customization sharing features to streamline their workflows, improve their LMS feature awareness, and explore new possibilities for designing their courses to match student expectations. Our findings provide new knowledge about customization sharing practices, highlighting the complex interplay of expertise, software learnability, domain-specific workflows, and social perceptions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".