Academic success courses at Ontario colleges: a qualitative analysis of syllabi
Bibliographic record
Abstract
Ontario Colleges expanded enrolment and reached into populations that traditionally did not attend post-secondary (Rae, 2005). The challenge has been to support students who were not prepared for college-level academic work (Habley, Bloom & Robbins, 2012). Lennon, Skolnik and Jones (2015) pointed out that colleges have been providing curriculum arguably of high school equivalency. First-semester academic success courses have been a curricular response to these challenges. Academic success courses are a combination of learning skills, involvement, metacognition, motivation, and self-regulated learning (Tebe, 2007; Burchard & Swerdzewski, 2009; Rasmussen, 2013; Hoops, Yu, Burridge & Wolters, 2015). There does not exist a set or taxonomy of skills and student development concepts described in academic success course outlines from across the Ontario colleges. To fill this gap, I qualitatively categorized skills and student development concepts described within academic success course outline documents from across Ontario. The first step was a comprehensive scan of college program websites to determine the programs that incorporated an academic success course. Fifty-nine course outline documents were acquired for a content analysis of course descriptions and learning outcomes. The categorization of skills and concepts was based on a synthesis of literature on academic success, academic competencies, and the demands of industry. 304 programs incorporated an academic success course. Academic success courses were used in all college credentials. Results suggested that courses are widely used, though less so in the advanced credentials. Some courses were structured generically and applied across a range of programs while other courses were offered within programs using discipline specific language (e.g., business, heath). A qualitative content analysis revealed dominant course themes of academic skills and personal development. Learning outcomes seldom expressed reading and writing skills. Learning outcomes seldom expressed connecting to the college environment or services. Learning outcomes seldom expressed aspects of resourcefulness or resiliency. Recommendations for curriculum designers to address gaps in learning outcomes are offered. Further research is suggested to clarify the nature and use of academic success courses at the Ontario colleges.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".