The Use of Twitter in Large Lecture Courses: Do the Students See a Benefit?
Bibliographic record
Abstract
The purpose of this two-year quantitative study was to determine the usefulness of the micro-blogging tool Twitter in large classes for improving the students’ sense of community and belonging. Three instructors of large classes were recruited to test the outcomes of using Twitter as a learning tool, one each from the Departments of Geography and Psychology, and the College of Nursing. Twitter was used as a learning tool to allow students to engage in discussion and ask questions in real time during class as well as outside of class. The method used by the authors included surveys that measured students’ perception of their sense of community and belonging, their engagement with the Twitter portion of the course, and their thoughts on the use of Twitter for academic purposes in a higher-education classroom setting. Data about students’ use of Twitter was further collected using the Twitter Archiving Google Spreadsheet tool. The authors conclude this study showed that Twitter, if integrated into the course and supported by instructor and/or assistants who are familiar with the use of Twitter, improved the sense of community reported by students.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".