Preferences for course delivery in library and information science programs: a study of master's students in Canada and the United States
Bibliographic record
Abstract
Objectives: This paper reports on Master of Library and Information Science (MLIS) students' preferences for course delivery (online, blended or face-to-face) and how their preferences differ based on demographic variables. This research is part of a bi-national study that investigated the motivations and experiences that MLIS students had with online education, while completing their graduate degree in an American Library Association (ALA)-accredited institution.Methodology: The study used an online survey to gather data from Master's degree students enrolled in LIS programs accredited by ALA, a professional association which accredits programs in the US, Puerto Rico, and Canada. The online questionnaire was administered with the assistance of the administration and their student associations of LIS programs. Thirty-six programs from Canada and the US were represented by the 1,038 students who responded to the online survey. Respondents who had taken and completed at least one online course constituted the sample (n=910) that was used for analysis and the reporting of the results.Results: The findings show that there were five statistically significant indicators associated with preferred instructional delivery for MLIS core courses: age (generational cohort), employment status, metro status, commute distance, and program modality. The results show that younger students who had part-time employment, resided in urban areas, and lived closer to the campus showed greater preference for a course delivery mode that required some form of in-person instruction (face-to-face or blended) than their older peers who had full-time employment, resided in rural areas, and lived farther from campus.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".