What's in a name? Exploring how voluntary library data literacy workshop titles and descriptions affect learner motivations to enroll
Bibliographic record
Abstract
Due to the nature of voluntary data literacy library instruction, such workshops often struggle to achieve high enrollment. In seeking strategies to mitigate these challenges, this qualitative study attempted to gain a better understanding of learner motivations when enrolling in voluntary data literacy instruction offered by a university data library by exploring the effectiveness of workshop titles and descriptions. Nineteen graduate students were interviewed. This study found four themes, in the form of questions that graduate students would ask about workshop descriptions: is the workshop meant for me; who is the instructor; what is the tone of the description; and are all the key details included in the description and easily identifiable. Based on these findings, several strategies to improve workshop descriptions were suggested, such as providing bios of all instructors, including target audience and prerequisites, and using bulleted lists. • This study involved interviewing graduate students and showing them library workshop descriptions to discuss and rank. • Students were more motivated to enroll if they felt they were the target audience and met the prerequisites. • Students were more motivated to enroll if they felt that the tone of the workshop description was welcoming. • Students wanted to see instructor biographies and photos in workshop descriptions. • Students wanted to see all relevant information in an easy-to-skim format in workshop descriptions.
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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 | 0.001 |
| 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 | 0.001 | 0.014 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".