Student Perceptions of Social Presence and Attitudes toward Social Media: Results of a Cross-Sectional Study
Bibliographic record
Abstract
Establishing and maintaining social presence in an online environment that depends on a learning management system (LMS) can be challenging. While students believe social presence to be important, LMS platforms have yet to discover a way to deliver this expectation. The growth of social media tools presents opportunities outside an LMS to foster social presence in online learning communities. The purpose of this study was to assess perceived levels of social presence in an LMS and willingness to use social media tools outside an LMS among online doctoral students. Student perceptions of social presence and willingness to use a social media tool were examined via a descriptive, cross-sectional survey design. The sample size was 138, representing a 52% response rate. Students reported high levels of social presence in the LMS, but noted important areas in which the LMS was deficient. While all students used at least one social media tool, the modes of communication with other students and instructors were primarily tradition (e-mail, LMS, phone). Despite their busy schedules, 57% of respondents reported having greater than 30 minutes available daily for social connections with other students and instructors, and 43% indicated that they were willing to use a social media tool if one was offered outside of the LMS. Given the importance that students place on social presence, the limitations of the LMS and the willingness of students to experiment with a social media tool in their learning environment, the exploration of adding such a tool is warranted.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".