Disrupting the norms : towards new understandings of persistence and success in postsecondary education
Bibliographic record
Abstract
The purpose of this research study was to explore longitudinally the academic outcomes and academic pathways of one cohort of students (N=790) in four general business diploma programs within one Ontario college. Informed by a life course perspective and utilizing the visual methodology of sequence analysis with optimal matching and cluster analysis, and discrepancy analysis, using R, the study maps individual student enrolment and achievement patterns beyond the expected time frame for graduation; presents diverse academic achievement measures including total courses enrolled, total courses passed, and course completion rate; and explores relationships between academic achievement measures and academic pathways, and student demographic, prior secondary school academic, and behavioural and academic characteristics related to the student-institution interaction at the time of entry. The study contributes to the research on postsecondary education student success in three ways. First, findings suggest the historical student success frameworks of Tinto (1975, 1993) and Metzner and Bean (1987) do not adequately represent the diverse academic pathways and outcomes for many students today. Second, the research supports Finnie, Childs, and Qui (2012) that reliance on student group level identification as a predictor of pathway or persistence is of limited use and therefore student success initiatives should be directed at individual students. Third, research findings suggest institutional and system structures and policies such as differentiation of enrolment status and curriculum and tuition fee structure may impede student success, particularly for those students whose lives do not align with the traditional and expected postsecondary education pathway. Although significant relationships were found between some of the student characteristics and individual academic outcomes and pathways, the effect size of these relationships is too small to be helpful from either a practical or a policy perspective to differentiate in advance students who will be successful under traditional measures and those who will not. Results suggest that early educational trajectories do not determine later ones and the transition into postsecondary education has the potential to alter prior pathways. The study also highlights the usefulness and challenges of utilizing historical institutional administrative student-level data.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".