Sustaining Education by Actualizing Affordances of Social Media Platforms During the Pandemic
Bibliographic record
Abstract
This paper draws on the affordances theory and investigates the ways in which pre-service teachers are sustaining social media uptake in learning by taking advantage of the opportunities afforded by the new delivery methods during the pandemic. This research is part of a descriptive mixed methods study. A questionnaire and in-depth, qualitative interviews were used to explore pre-service teachers’ perceptions of the affordances of social media platforms for learning and the actualization of those affordances. The survey and interview data were analyzed through the theoretical lens of affordance theory. The results suggest that the pre-service teachers actualized the affordances of social media by their knowledgeable use in a dynamic learning environment. The key findings are that the teachers perceived the social media applications to be flexible, useful and practical (functional affordances), bridges formal and informal learning and supported self-regulated learning (cognitive affordances), allowed pre-service teachers to assume new identities and discover learning as independent learners who had control over their learning (identity creation affordances), and fostered interaction and collaborative feedback (social affordances). Lack of guidance or lack of teacher presence was the main constraint. This research produces new knowledge and analysis and makes new theoretical contributions on the specific affordances of social media technologies. This study adds to and extends existing literature by contributing to an understanding of the actualization of the affordances of social media platforms.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".