Are Social Media Sites a Platform for Formal or Informal learning? Students’ Experiences in Institutions of Higher Education
Bibliographic record
Abstract
By being oblivious to the recent paradigm shift from formal learning to informal learning platforms, higher education institutions (HEIs) disadvantage student learning in the digital age. With the aim of bringing awareness of the need to shift from the use of learning management systems (LMS) to social media sites (SMS), this study explores students’ experiences of the use of SMS for learning science modules. This qualitative interpretive case study was carried out at two universities, with electronic reflective activities, Zoom focus group interviews and WhatsApp one-on-one semi-structured interviews used to generate data. The sample was a total of 47 students purposively selected from science modules in a teacher education programme at two schools of education, one in South Africa and one in the United States of America. Data were thematically analysed and framed by social constructivism and connectivism. Findings indicated that learning of science modules is mainly through LMS, at the expense of SMS which are preferred by the students. The study concludes that since SMS are used effectively for students’ communication and collaboration outside of the lecture hall, then HEIs need to shift to thinking about bringing these SMS inside and putting them to use for effective learning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".