Factors Associated with Post-Secondary Student Retention at a Technical Campus
Bibliographic record
Abstract
As post-secondary institutions struggle to create conditions that support student success, they also struggle to maintain and successfully manage student retention. The study of retention within post-secondary education is complex, and significant models have been constructed in an effort to further understand the retention of students in post-secondary institutions (Astin, 1984, 1993; Bean, 1980, 1981, 1982, 1983, 1990; Bean & Metzner, 1985; Cabrera, Castañeda, Nora, & Hengstler, 1992; Cabrera, Nora, & Castañeda, 1993; HeavyRunner & DeCelles 2002; Spady, 1970, 1971; Tinto, 1975, 1987, 1993; Webb, 1989). In an effort to further the study of student retention, the integrated model of student retention (Cabrera et al., 1992; Cabrera et al., 1993) identified the overlap in the student attrition model (Bean, 1980, 1982) and the student integration model (Tinto, 1975, 1987). The results highlighted that incorporating the two models provided an improved explanation of retention (Cabrera et al., 1993), and the integrated model was used as the theoretical foundations for this work. Considering students’ institutional and goal commitments, with an emphasis on both their career and major certainty, this research aimed to determine the factors associated with the first- to second-year retention of students enrolled at the Marine Institute campus of Memorial University. For institutions that offer direct-entry, career-focused programs or for those of a technical nature, this research has the potential to add value to the existing work on student retention. Using pragmatism as the philosophical approach to the research, an explanatory mixed-methods design was implemented, collecting quantitative and qualitative data (Creswell, 2014; Creswell & Plano Clark, 2010). Survey data were collected from first-year students at the Marine Institute to investigate the factors they identified as associated with their post-secondary experience, certainty of their choices, and retention. In the second phase, focus groups were divided into four career disciplines and conducted with the intent to further explain the survey data. A principal component analysis was conducted and established nine factors from the survey data. Further analysis confirmed that in this specific sample, both major and career certainty contributed to students’ institutional commitment and that first- to second-year retention is most significantly impacted by institutional commitment, goal commitment, and intent. The student voice highlighted the value in the contribution and insight of faculty in the students’ commitment to the institution, including the certainty of their major and career. Obligation was expressed as a distinct element of goal commitment in this specific population and is one of the study outcomes that led to productive recommendations for future research and practice. This research will contribute to the available Canadian retention research and, more specifically, contribute to the development of improved retention and support practices in institutions that seek to support students studying in technical, career-oriented programs.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".