User Experience and Design in a First Year Brightspace Course
Bibliographic record
Abstract
Power of Persuasion is a first year seminar (FYSM) course in Rhetoric. Student resources are important in this course because FYSMs are meant to introduce new students to university and develop academic skills. In this FYSM, students practice their reading and analytical skills in the context of rhetorical concepts with current examples of persuasion and argument. Therefore, much of the course content is renewed annually to stay relevant with events and student interests. Subsequently, the Brightspace course site has become overwhelmed with course material, becoming cluttered and distracting. Our project worked to improve the user experience (UX) of the Brightspace page by updating the content and design. Addressing the content-related needs, we provided rhetorical analysis material by sourcing up-to-date opinion articles of relevance to students, and strategized how to guarantee the students would gain knowledge and give informed agreement to the Academic Integrity policies before beginning the course. Particularly, the student-partner was able to offer a valuable student and peer lens in searching for articles with a range of topics. For the design, we resolved to improve the UX for both the students and the instructor. To do so, we decluttered the interface so the students could find what they needed with more ease, and organized the platform and backend of the course site to create a simpler experience for the instructor. The student-partner’s role was to generate and test possible solutions to the design problems, and to implement the ones that best suit our goal to improve UX.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".