Social media in undergraduate teaching and learning: A scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: To conduct a scoping review that systematically examines the body of research on social media in undergraduate teaching and learning in order to identify key issues, trends, gaps, and needs. Our objectives include determining what methods have been commonly used to study social media in undergraduate teaching and learning, and to synthesise insights from published research findings within the fields of higher education, educational technology, and the scholarship of teaching and learning. INTRODUCTION: The use of social media technologies in post-secondary environments has been increasing over time, and especially following the shift to remote teaching and learning during the COVID-19 pandemic, this growth has continued. This review addresses a need to analyse and understand the body of research on the use of social media across undergraduate contexts for teaching and learning. INCLUSION CRITERIA: This scoping review includes peer-reviewed journal articles on social media in an undergraduate teaching or learning context published at any time, in English. In addition to including concepts and terms related to social media broadly, based on global social media usage, we include within our search the most commonly used social media platforms. We excluded items from the grey literature (such as reports, dissertations, and theses), and studies that focus on groups outside of the undergraduate population of interest (e.g., in elementary, secondary, or graduate settings, etc.). METHODS: Systematic searching will be conducted in relevant subject and multidisciplinary databases: Education Database, Education Research Complete, ERIC, British Education Index, Australian Education Index, Academic Search Complete, and Scopus. Records will be deduplicated and screened using Covidence software, with each record independently reviewed by two researchers in both rounds, screening titles and abstracts in the first round, and full-text of articles in the second. Researchers will meet to discuss discrepancies and make decisions using a consensus model, and a third researcher will be independently tasked with resolving any conflicts. Data extraction will also use two independent researchers to review each article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.158 | 0.126 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.028 | 0.021 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.056 | 0.016 |
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".