Examining social media in the online classroom: postsecondary students' Twitter use and motivations
Bibliographic record
Abstract
As postsecondary education develops to reflect advances in pedagogical research and practice, new technology, and students' changing needs, instructors are adapting to respond to these changes. Social media is one technology that is being adopted more in postsecondary classrooms as a tool to bridge instructors' teaching and learning objectives and students' outside interests. This research explored postsecondary students' social media use, generally and in two online courses, to determine their motivations for using social media and Twitter specifically, and to understand how students engage with each other and course-related content online. Pre- and post-surveys revealed distinctions in students' personal and academic/professional uses of social media; students were active users and were more prolific on certain platforms as compared to others and used social media for varied purposes. These findings can guide instructors' selection and integration of specific social media tools for course activity/evaluation tailored to students' interests and online behaviours.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".