An example-based customization sharing platform to support educators with using feature-rich classroom software
Bibliographic record
Abstract
Educators often lack the time and resources to learn, integrate, and customize featurerich software applications for their classes, and many struggle to keep up with the latest educational technologies.This dissertation introduces Customizer, a novel customizationsharing platform designed to streamline how educators can discover, interactively experiment with, and appropriate examples of their colleagues' software customizations in a widely-used learning management system (LMS).To inform the design of Customizer, we carried out two studies that allowed us to gain a deeper understanding of educators' current approaches and challenges in learning and customizing their classroom software.In the first study, we conducted interviews with 20 K-12 teachers revealing the staggering variety of software tools being used in modern classrooms and highlighting the large extent to which educators rely on a social fabric of friends and colleagues for support with software usage and customization.In the second study, we analyzed posts from the Q&A forum for a popular LMS and found that educators' questions about more idiosyncratic customization needs frequently go unanswered.To mitigate the challenges educators face in customizing their LMS, we designed, built, and evaluated the Customizer platform allowing educators to "peek" at their colleagues' setups and "borrow" their customizations.Finally, we carried out a two-week field deployment of Customizer to 10 instructors, which shed light on how instructors might integrate such a tool into their day-to-day workflows to improve LMS feature awareness, test out potential changes, and streamline their knowledge-sharing routines.The central thesis of this dissertation is that providing educators with in-context access to colleague-recommended examples of software customizations, and the ability to interactively experiment with and appropriate those customizations, can provide a useful and usable means to overcome the challenge of learning to customize feature-rich classroom software.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".