How core competencies and non-academic skills are used in admissions decisions at British Columbian universities
Bibliographic record
Abstract
British Columbia’s (BC) new curriculum shifts the focus away from purely academic skills towards a more holistic one, emphasising what it calls Core Competencies – a set “of intellectual, personal, and social and emotional proficiencies that all students need in order to engage in deep, lifelong learning” (British Columbia Ministry of Education, n.d.). While many universities in the United States (US) have incorporated some type of holistic assessment as a part of their admissions requirements (Hossler et. al.), the same thing may not be true for British Columbia. The goal of this research is to compare the focus of the high school curriculum in BC with the requirements for admission into the Faculty of Arts and Faculty of Science at major post-secondary institutions as stated on their admissions websites. One of the purposes of high school education should be to prepare students for success in their future pursuits, and the pursuit of post-secondary education is rising in Canada (Usher, 2021). While the idea of providing students with the skills for lifelong learning aligns with this purpose, many high school students will inevitably have to navigate the application process for post-secondary education. The selection process for admission into universities is opaque (Agatep 2018; Bastedo et al., 2018), and whether post-secondary institutions value the same skills as BC’s curriculum is unclear. In this study the admissions websites of the Faculty of Arts and the Faculty of Science at three major universities in BC were analysed through qualitative document analysis. The researcher found that for two of the universities, academic grades were the only deciding factor with respect to admission. One university required all applicants to write a personal essay. Semi-structured interviews with four high school guidance counsellors throughout the province were conducted in the exploratory qualitative research part of this study. The interviews revealed that from the perspective of professionals at the high school level, admission into universities in BC remains primarily focused on academic grades rather than non-academic skills. While all counsellors knew about the personal essay requirements, none could explain how it was used, and to what degree.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.015 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".