Development and validation of a social media and science learning survey
Bibliographic record
Abstract
The purpose of this study is to describe the development and validation of a survey that examines science students’ social media learning behaviours. Inherent in critiques regarding ‘digital natives’ is a need to better understand what the current generation of learners actually do in their social media practices for learning. The survey can help us understand how students actually use social media for learning science. Survey development followed an inductive approach [Brinkman, 2013. Qualitative interviewing. Oxford ebook; Mansourian, 2006. Adoption of grounded theory in LIS research. New Library World, 107(9/10), 386–402; Strauss & Corbin, 1998. Basics of qualitative research: Grounded theory procedures and technique (2nd ed.). Newbury Park, CA: Sage], where survey design was informed by results of focus groups with secondary and post-secondary physics students and the survey was iteratively revised after two cycles of administration and validation interviews. The final version of the Social Media and Science Learning Survey can be used by educators and researchers to understand how social media tools can be leveraged in order to allow learning to emerge and to use this knowledge to frame recommendations and methods for integrating these tools into classroom-based environments.
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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 | 0.179 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".