Bibliographic record
Abstract
Over the past 20 years, the Organization for Economic Co-operation and Development has reported a median of 50% for dropout rates in doctoral programs, all disciplines combined (OECD, 2019). Among reasons for not graduating, PhD students identify a lack of experience and competencies with academic writing, impeding on their progression as students, but also as novice scholars (Litalien & Guay, 2015). Indeed, graduate students are required to undergo professional socialization, by engaging with other scholars, to learn the norms and practices of their respective research fields (Skakni, 2011). This paper aims at communicating preliminary results from a doctoral research to provide a greater understanding of peer learning in academic writing groups organized by Master’s and PhD students. The social learning theory developed by Bandura (1971) is used as a foundation to our study, with its self-efficacy concept at the forefront of our theoretical framework. In that regard, PhD students can develop confidence in their abilities to successfully complete writing projects based on four sources of influence: mastery experiences; vicarious experiences; social persuasion; and physiological and emotional states (Bandura, 2019). While studying a learning community composed of 4,000 graduate students, as an instrumental case study (Stake, 1995), we conducted semi-structured interviews with 25 PhD students, followed by a content analysis of transcripts using a qualitative data analysis software (NVivo12). Participants representing 12 Canadian universities and 14 scholarly disciplines shared significant learning experiences related to all four self-efficacy sources of influence. Of particular interest, findings revealed that PhD students gathering in public places (cafes, libraries, coworking spaces, museums, parks) increased their self-efficacy through peer learning (exchanging, observing, modelling). These results are presented with a view of recommending valuable strategies to develop academic writing competencies through social actions led by graduate students, in conjunction with institutional support in the context of higher education.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.048 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".