A Systems Approach to Increasing Awareness and Coordination of Student Retention and Completion at a Canadian College
Bibliographic record
Abstract
As competition for students and scarce resources increases due to political and economic pressures, awareness and coordination of student retention and completion become a critical priority for post-secondary institutions. However, despite increased attention on Strategic Enrolment Management (SEM) (Black, 2010; Gottheil & Smith, 2011; Hossler & Bontrager, 2015; Wilkinson et al., 2007) and the growth of retention-focused student services in recent years, retention rates remain unchanged (Habley, Bloom, & Robbins, 2012). Post-secondary students are in the midst of a significant transition period (Tinto, 1993) and face challenges related to mental health and wellness, relationships, rigors of academic life, and personal finances (Braxton, Hirschy, & McClendon, 2006; Tinto, 1993). Furthermore, complex institutional structures are increasingly difficult for students to navigate (Karp, 2011) and are not designed around the needs of Generation Z learners (Seemiller & Grace, 2016). This Organizational Improvement Plan (OIP) describes a “whole-system” solution to the problem that sorts, connects, supports, and transforms students while also transforming the institution (Beatty-Guenter, 1994). This plan describes how the higher education registrar is uniquely positioned within the organization to lead change (Duklas, 2014; Waters & Hightower, 2016) and identifies a compatible systems approach to leadership (Coffey, 2010; Senge, Hamilton, & Kania, 2015) that may be employed. To accelerate and sustain change so that it becomes embedded within organizational culture (Kotter, 2014), key stakeholders representing multiple campus subsystems (Kalsbeek, 2006a, 2006b, 2007) are engaged throughout the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 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".