Building Communities Through Online Spaces
Bibliographic record
Abstract
This paper explores the perceived information learning needs of students registered in the Teacher-Librarianship by Distance Learning (TLDL) program at the University of Alberta. This paper reports on the findings related to two main questions: 1. To understand the perceived information needs of students who are completing a Master of Education degree completely online. 2. To understand students’ perceptions of “community” in online spaces. To address these questions, an online survey was distributed to current and former students of this online teacher librarianship education program. Respondents indicated that they had a strong sense of community through the program and the online courses. Community within the TLDL program is built through student-to-student and instructor-to-student interactions. Respondents’ perceived sense of community aligned with the existing literature about building online communities. This study indicates that as more students choose to take courses online, instructors need to carefully consider how to make rich learning experiences that are as good as, or even better than face-to-face learning experiences.
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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".