The Complexities of Using Digital Social Networks in Teaching and Learning
Bibliographic record
Abstract
The aim of this investigation was to gain a broad sense of the implementation of digital social networks for teaching and learning by instructors in higher education. We were particularly interested in examples of instructors' use of digital social networks in their courses, the benefits and challenges of specific platforms for teaching and learning purposes, and the perceived role of digital social networks in teaching and learning during the first years of the COVID-19 pandemic. The participants in this investigation (n=336) were faculty and graduate students. Data was collected via an electronic questionnaire with closed and open-ended questions using Qualtrics. The quantitative analysis included descriptive statistics. The data analysis also included qualitative data analysis of textual responses provided to various questionnaire items. The results illustrate the ways in which instructors successfully apply or integrate digital social networks into teaching and learning, broadly and with specific digital social networks. Yet, the findings also highlight several complexities that instructors have experienced when considering digital social networks in their teaching practices such as data privacy issues, misinformation, user interface issues, and the willingness of students to use less trendy digital networks, among other challenges.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".