Relationships Between Student Characteristics, Academic Advising and College Student Success
Bibliographic record
Abstract
This dissertation is about the student success problem: institutional retention rates have remained low for decades and clear evidence about why and how to support more students to persist and graduate is elusive. In Canada, and within Ontario Colleges specifically, there is a dearth of research on this topic. Performance-based funding and the global pandemic has increased the need to improve these outcomes. The three studies that comprise this dissertation investigate the relationships between the characteristics of students at the time they enter college, participation in academic advising, and student success. In the first study, an integrated literature review method was used to analyze two decades of peer-reviewed research related to five constructs – program fit, career clarity, academic self-efficacy, educational commitment, and friend and family support – and the relationships with both advising and student success. Results showed positive relationships. New working definitions for each construct were developed. The second and third studies used a unique administrative dataset from Mohawk College. In the second study, a series of factor analyses identified three latent variables – Career and Program Clarity, Friend and Family Support, and Positive Academic Attitudes – within the Mohawk College student entrance survey. The latent variable measurement model was used as the foundation of a structural equation model (SEM) as part of the third study to analyze the relationships between the latent variables, advising participation, and student success. The SEM did not produce an acceptable fit or find any significant relationships. The concluding chapter used an integrated discussion method to summarize the overall findings. The contributions to the literature include an example of unique methods; new findings related to student success; and evidence of the practical use of college administrative data. Seven implications and next steps for researchers, practitioners and leaders are identified: improving institutional data collection practices; more focused evaluation of academic advising; purposeful outreach to students who do not engage early in admissions or transition processes; continued efforts to work with students to (re)define student success; new investments in research infrastructure; stronger emphasis on good research methods; and new campus commitments to relationship rich education.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".