Go Where the Students Are: A Comparison of the Use of Social Networking Sites Between Medical Students and Medical Educators
Bibliographic record
Abstract
BACKGROUND: Medical education has grown beyond the boundaries of the classroom, and social media is seen as the bridge between informal and formal learning as it keeps students highly engaged with educational content outside the classroom. OBJECTIVE: The purpose of this study is to explore the perceptions of medical educators and medical students regarding the use of social media for educational purposes. METHODS: Both groups (medical educators and students) were invited to take a survey. The surveys consisted of 29 questions, including Likert-style, multiple choice, yes/no, ranking, and short answer questions. The survey forms and statistics were built using Google Drive analytics with the free Spanning Stats module. To compare between professors and students, results were exported to a Microsoft Office Excel spreadsheet (Microsoft Corp, Redmond, WA). The study protocol was approved by The Ottawa Health Science Network Research Ethics Board (OHSN-REB:20140680-01H). RESULTS: The overall response rate to the survey was 40.9% (63/154) for students, and 36% (72/200) for medical educators. The majority of educators (79%, 57/72) and students (100.0%, 63/63) had presence on social networking sites (SNSs). Only (33% 19/57) of educators used SNSs with their students, the most used sites were Facebook (52%, 10/19) and Twitter (47%, 9/19), followed by LinkedIn (21%, 4/19), Google+ (16%, 3/19),YouTube (11%, 2/19), and blogs (11%, 2/19). Facebook (100%, 63/63), YouTube (43%, 27/63), Twitter (31%, 20/63), and Instagram (30%, 19/63) were the sites most commonly used by students. The educators used SNSs mainly to post opinions (86%, 49/57), share videos (81%, 46/57), chat (71%, 41/57), engage in medical education (68%, 40/57), take surveys (24%, 14/57), and play games (5%, 3/57). On the other hand, students used SNSs mainly to chat with friends (94%, 59/63), for medical education purposes (67%, 42/63), to share videos (62%, 39/63), to post opinions (49%, 31/63), to take surveys (11%, 7/63), and to play games (6%, 4/63). Most educators (67%, 38/57) do not use social media in their education Although most of the educators (89%, 17/19) and students (73%, 46/63) found the use of social media time-effective, that it offered an inviting atmosphere (89%, 17/19 and 70%, 44/63), and that it enhanced the learning experience (95%, 18/19 and 70%, 44/63), both groups stated that they had colleagues who refused to use social media. The detractors' concerns included privacy issues (47%, 18/38), time-wasting (34%, 13/38), distraction (21%, 8/38), and that these media might not be suitable for education (11%, 4/38). When it came to using SNSs with the students, the educators most often used SNSs to post articles (42%, 8/19), explanatory comments (31%, 6/19), and videos (27%, 5/19).While students preferred the following posts : Quizzes (87% 55/63), revision files (82% 52/63) and explanatory comments (29% 21/63). CONCLUSIONS: Although social media continue to grow, some educators find that they do not offer suitable modes of learning. However, it is important to acknowledge that there are persistent differences in technology adoption and use along gender, racial, and socioeconomic lines; this is often referred to as the "digital divide". The current study shows that students prefer certain posts like quizzes and revision files, while educators are focused on posting videos, articles, and explanatory comments. Medical educators are encouraged to focus on the students in a way to minimize the gap between learners and educators. It will remain our responsibility as educators to focuson the student, use SNSs at their fullest, and integrate them into traditional Web-based management systems and into existingcurricula to best benefit the 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".