Nourishing STEM Student Success via a TEAM-Based Advisement Model
Bibliographic record
Abstract
LaGuardia Community College is an international leader recognized for developing and successfully implementing initiatives and educating underserved diverse students. LaGuardia’s STEM students are holistically advised by a team of dedicated faculty and staff members from different departments and divisions. As an innovative approach to advisement, students are first connected to an advising team member in their discipline-based first-year seminar and consequently guided by other cross-institutional advisement team members to ensure their continued success. In this article, we share our policies, processes, and promising practices in advising STEM student at an urban public institution. We present arguments that address and support five pillars for student success: 1) the student matters, 2) supportive culture matters, 3) effective communication matters, 4) data matters, and, 5) clear pathways and effective advisement matters. Finally, we present empirical evidence that show positive results in terms of students’ retention. Specifically, there was an improvement in the actual Fall 2015 to 2016 return rate of STEM students, from 62.9% to 64.6%. Our scaled practice demonstrates the value of collaborative team-based advisement efforts as supported through professional development can improve community college STEM student persistence when the above five pillars are fully espoused by the institution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".