Pre-Service Teachers using Social Media: Self-Concept in Online Spaces
Bibliographic record
Abstract
With the expansion of personal interactions to online spaces, specifically through social media, individual identity and self-concept development can be subjected to a variety of interactions, experiences, and comparisons. For pre-service teachers (PTs), interactions through social media can be experienced through a personal and professional lens. This research aimed to understand better the relationship between PT self-concept and social media use. A survey design method with Likert scale instruments was used to determine potential correlations between PT self-concept clarity and self-presentation across personal and professional spheres online. Univariate correlational analyses were run between the four Likert scale tools, and results indicated a weak, positive relationship between self-concept clarity and self-presentation. Self-concept differentiation was addressed by analyzing the open-ended questions at the end of the survey, using a thematic qualitative approach. Results of the qualitative analysis suggested that PTs exhibited a high level of self-concept differentiation as they considered the content of what they posted and presented online for both personal and professional accounts, meaning they accurately utilized the desired self-concept traits for the differing environments. The findings showed that PTs’ self-presentation in online spaces often aligned with their understanding of who they are and who they want to be, and they consider a variety of scenarios when presenting themselves online, including future careers, self-image, and the professionalism of teaching. The findings also showed that PTs compare themselves to others within the program, often feeling a sensation of intimidation, competitiveness, and perfectionism. An implication for teacher education is for programs to provide additional support for PTs who struggle to navigate the competitiveness of a professional program, their own professional identity, and the concept of moral and ethical duties within their roles as PTs and future teachers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".