“You Actually Learn Something”: Gathering Student Feedback Through Focus Group Research to Enhance Needs-Based Programming
Bibliographic record
Abstract
In the spring of 2015, the Centre for Academic Communication (CAC) at the University of Victoria began a series of projects aimed at understanding the needs of undergraduate and graduate students with English as additional language (EAL), with the goal of increasing the effectiveness of the Centre’s programming. The first project, detailed in this article, concentrated on identifying student perceptions and use of the Centre’s programming and aimed to elicit suggestions as to how the CAC could better meet student needs. To do this, we facilitated two focus group interviews consisting of EAL graduate (N=6) and undergraduate (N=4) students. Participants responded that the timing of programming should better reflect their schedules, and that programming should be more discipline specific and better designed for graduate students. They also felt that they did not receive enough critical feedback and that there was a lack standardization across tutorials and workshops. However, the participants also felt that the tutors were helpful, the programming provided a good addition to their studies, and the supports increased their confidence. Two unexpected findings were that, generally, students accessed one kind of programming offered by the Centre, rather than taking advantage of the range of offerings, and that students had misconceptions about the Centre’s offerings and how to use them. It is hoped that this study will help inform other student academic support services about focus group research for the purposes of program evaluation and collecting student feedback.
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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 | 0.085 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".