Broadening the Definition of ‘Research Skills’ to Enhance Students’ Competence across Undergraduate and Master’s Programs
Bibliographic record
Abstract
Undergraduate and master’s programs—thesis- or non-thesis-based—provide students with opportunities to develop research skills that vary depending on their degree requirements. However, there is a lack of clarity and consistency regarding the definition of a research skill and the components that are taught, practiced, and assessed. In response to this ambiguity, an environmental scan and a literature search were conducted to inform the creation of a comprehensive list of research skills that can be applied across programs and disciplines. Although published studies directly comparing research skills in thesis and non-thesis programs were limited, the specific skills reported in each program type were similar. This viewpoint article identifies the following seven research skills that were most frequently reported across both thesis and non-thesis programs: critical appraisal, information synthesis, decision making, problem solving, data collection, data analysis, and communication. When contextualized appropriately, these skills can be useful for a student during their academic program and are transferable across a range of future career pathways. Broadening the definition of “research skills” can inform curricular updates and program development, independent of their program type, to ensure that students are presented with explicit opportunities to develop the skills needed to succeed in their educational and occupational endeavours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.091 | 0.156 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".