Solution-focused approach in higher education: a scoping review
Bibliographic record
Abstract
As students populating higher education (HE) are becoming increasingly diverse, there is a growing need to equip educators with learner-centred communication skills. A solution-focused (SF) approach represents one attractive option to equip HE instructors and supervisors with strengths-based, goal-oriented communication techniques. This scoping review maps the current use of SF approaches in HE, explores key SF tenets and techniques compatible to HE, identifies knowledge gaps, and suggests recommendations for future research and professional development. From an initial yield of 7941 citations, 17 peer-reviewed articles published between 2001 and 2018 were included in the review. The majority of reviewed articles embraced strengths-based tenet of the SF approach as conducive to enhancing learners’ engagement and self-efficacy, and improving learner–educator collaboration. SF techniques such as scaling and goal settings were used to support learners in setting individualized goals and actionable steps toward the goals. The pragmatic focus of the SF approach on solutions and multiple pathways to goal attainment was also considered helpful for academic and clinical supervisors facing time constraints. More rigorous empirical research is needed on appropriate application of the SF approach in diverse HE contexts, and on evaluation of potential mediators of change between the approach and learner/educator outcomes.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".