Undergraduate Students’ Academic Information and Help-Seeking Behaviours using an Anonymous Facebook Confessions Page
Bibliographic record
Abstract
Best Practitioner Paper / Prix du meilleur article par un professionnelThis research examines undergraduate students’ academic help-seeking behaviours by mining anonymous posts from a university Facebook Confessions page. From a dataset of 2,712 public posts, researchers identified 708 Confessions (26.1%) that supported student-student learning exchanges. Using a mixed methods methodology informed by a social constructivist framework, analysis of these social media interactions demonstrates that students use Confessions posts to legitimately inform their undergraduate learning and support their academic experience. Researchers conclude that Facebook Confessions can enable rich academic help-seeking and other information behaviours, and that these sites should be taken seriously by administrators, faculty, researchers, and students.Cette recherche examine les comportements académiques de recherche d'aide des étudiants de premier cycle en procédant à l’extraction de publications anonymes sur une page Facebook de confessions à l’université. À partir d'un jeu de données de 2 712 publications publiques, les chercheurs ont identifié 709 confessions (26,1%) qui étaient en faveur des échanges entre étudiants visant l’entraide dans les apprentissages. En utilisant une méthodologie de méthodes mixtes guidée par un cadre socioconstructiviste, l'analyse de ces interactions sur les médias sociaux démontre que les étudiants utilisent les confessions pour guider légitimement leur apprentissage de premier cycle et soutenir leur expérience académique. Les chercheurs en tirent la conclusion que les confessions Facebook peuvent permettre une recherche d’aide universitaire approfondie et d'autres comportements informationnels, et que ces sites devraient être pris au sérieux par les administrateurs, les professeurs, les chercheurs et les étudiants.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".