Symposium 5: The Paradox of Social Media and Higher Education Institutions
Bibliographic record
Abstract
This paper explores the paradox that occurs between institutional expectations and expectations held by student regarding the use of social media in support of learning in higher education settings. Specifically, the example is given of a disagreement that took place in a recent conversation in a distributed medical education programme in Canada. The current body of research regarding the incongruity of expectations about integrating social media into a higher education institution framework suggests that a widening gap is emerging and that conflict is taking place. The example from Canada exemplifies the difference that exists in people’s understandings and expectations of how social media can be employed for benefit in education. The paper looks at the principles of social media and the potential impact on many of society’s institutions, including government, commerce, media and education. Interestingly, higher education seems to have fallen behind in adopting and adapting to the new social media reality. The key points of social constructivist thinking are then examined with special attention to the following five points: learning requires active participation by the learner; previous experience is important when reinforcing new learning; individual knowledge construction requires a social interaction element; negotiation within the learning environment is essential; and, learning best takes place within a socio-cultural context. These principles are then addressed in relation to the social media principles of active participation, collaboration and that of reflection. Finally, three points are expanded as to potential sources and reasons why conflict may occur when trying to integrate a popular social media perspective into the established higher education setting. These are: existing hierarchical structure of higher education institutions; accreditation and quality concerns; and, formal and informal learning. Social media is more than computer application and programs and the technology behind them it is about transformation. At its core, social media is a collection of ideas about community, openness, flexibility, collaboration, transformation and it is all user-centred. If education and educational institutions can understand and adopt these principles, perhaps there is a chance for significant change in how we teach and learn in formal and informal settings. The challenge is to discover how to facilitate this change.
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".