Research Approaches in Scholarship of Teaching and Learning Publications: A Systematic Literature Review
Bibliographic record
Abstract
The Scholarship of Teaching and Learning (SoTL) has been described as the fastest growing academic development movement in higher education. As this field of inquiry matures, there is a need to understand how SoTL research is conducted. The purpose of our study was toinform this debate by investigating research approaches used in SoTL publications. We analysed 223 empirical research studies published from 2012 to 2014 in three explicitly focused SoTL journals. We classified the studies as either qualitative, quantitative, or mixed methods using an analytical framework devised from existing literature on research methods. We found that the use of the three research designs was fairly evenly distributed across the papers examined: qualitative (37.2%), quantitative (29.6%), and mixed methods (33.2%). However, there was an over-reliance on data collection from a single source in 83.9% of papers analysed, and this source was primarily students. There was some, but limited, evidence of the use of triangulation through the use of multiple data collection instruments (e.g. survey, assessment tasks, grade databases). Similarly, only one-third of publications classified as mixed methods integrated the analysis and interpretation of the qualitative and quantitative data equally within the study. We conclude that current SoTL research is characterised by methodological pluralism but could be advanced through inclusion of more diverse approaches, such as close reading, and adoption of strategies known to enhance the quality of research, for example, triangulation and visual representation.
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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 | 0.087 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.078 | 0.059 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".