How Social Presence on Twitter Impacts Student Engagement and Learning in a Grade 8 Mathematics Classroom
Bibliographic record
Abstract
Social media for personal use has evolved rapidly among adolescents, changing the way they communicate with each other. However, little research has been conducted about how teachers use social media in the classroom to improve student learning. The purpose of this qualitative study was to describe how social presence on Twitter impacts student engagement and learning when a mathematics teacher integrates this social media tool into an instructional unit. The conceptual framework was based on social presence theory developed by Short, Williams, and Christie. This qualitative study used a single case study design. Participants included 6 students and 1 classroom teacher in a Grade 8 mathematics course at a public middle school in a Canadian province. Data were collected from multiple sources including individual interviews, reflective journal responses from the teacher and students, documents such as course standards, and artifacts such as student tweets. Data were analyzed in the following way: interview and reflective journal data were coded for categories using the constant comparative method, and documents and artifacts were reviewed to identify emergent themes and discrepant data. Findings for this study indicated that Twitter had a positive impact on student engagement and learning of data management concepts. This study contributes to positive social change by providing a deeper understanding of how social media tools such as Twitter encourage students to create communities of learners to support each other during the learning process.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".