The impact of departmental academic skills provision on students' wellbeing
Bibliographic record
Abstract
Student wellbeing in UK higher education is of serious concern, with high rates of stress and anxiety recorded among students (Pereira et al, 2019). This is compounded for international students who speak English as a second or third language. However, international students are an integral part of higher education in the United Kingdom. Strategies that are specifically designed for international students that support wellbeing are somewhat lacking across the sector (Shu et al, 2020). The aim of this initiative is to embed academic and communication skills into students’ programmes of study in the form of weekly 2-hour academic skills classes. This small-scale study is based on the experience of teaching MA Education students, 95% of whom are Chinese. Classes focus on developing students’ understanding of critical thinking and writing, supporting their academic reading and ensuring that they understand academic conventions in the UK such as referencing and academic writing structure. Classes also provide another layer of support and social interaction for students which we hope support student wellbeing. We surveyed 40 students about how the classes support their participation and interaction, alleviate anxiety and help to develop their sense of belonging. We followed this up with students interviewing each other on their experiences of academic skills development classes. Members of the teaching team observed the interviews and took notes. This paper will report on our findings and make recommendations for how to further improve support for international PGTs.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".