What makes students tick? Exploring factors that affect learner motivations and challenges when engaging with optional library workshops on data literacy
Bibliographic record
Abstract
Optional data literacy library workshops often struggle to achieve consistent high enrollment and attendance. In seeking strategies to mitigate these challenges, this qualitative study attempted to gain a better understanding of learner motivations and challenges when enrolling and attending optional data literacy instruction offered by a data library at a large university. Nineteen graduate students were interviewed. This study found four factors involved in motivating graduate students to enroll and attend optional library data literacy workshops. These factors, in the form of questions that graduate students would ask about the workshops, were: does the workshop address my need; does the workshop fit into my schedule; does the workshop satisfy my learning preferences; and are there health, emotional, or social aspects that could affect my workshop attendance. Based on these findings, several strategies to improve motivation and attendance were suggested, such as providing workshops at time of need, holding regular repeated sessions, presenting a mix of online and in-person offerings, and making workshops part of a social or networking event. • Interviewed students about their motivations to engage with data workshops • Students wanted workshops that met their research, course, or job hunt needs. • Students wanted online and in-person workshops that fit it into their schedules. • Students wanted workshops if there was a social element, such as food and drink. • Students didn't attend workshops due to illness, timing conflicts, or bad weather.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.018 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".