#Education: The Potential Impact of Social Media and Hashtag Ideology on the Classroom
Bibliographic record
Abstract
Common on social media platforms, the hashtag (#) organizes users’ ideas, emotions, and comments. Originally used to create a searchable platform, the hashtag and its ideology present interesting considerations for changes to education. As students using social media today most certainly use hashtags to converse, hashtag-informed teaching could connect education to students’ worlds instead of forcing students to fit into the pre-defined world of education. Prevalent in post-secondary education, K-12 educators have recently begun to integrate social media tools into their classrooms, but what are the pedagogical implications of the ideologies of these tools? In response, this study asked the following question: “How can the hashtag inform the K-12 classroom?” Using a systematic literature review and thematic analysis, this study analyzed eight articles that discussed the use of hashtags with K-12 students. Findings indicated four themes that could inform the alignment of K-12 classrooms with hashtag ideology: encouraging voice and user-generated content, the potential of self-organization, network hetero/homogeneity, and connecting to space without a common physical space. Suggestions are provided as to how classrooms (and education) may consider restructuring to better reflect hashtag ideology, meeting students in their social media-driven world.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".