Assessment in Higher Education and Student Affairs Graduate Education
Bibliographic record
Abstract
Courses focused on assessment within higher education have proliferated across Higher Education and Student Affairs (HESA) graduate programs, tied to an emphasis on using data and evidence in decision making in the field. The intended outcomes and curriculum of these courses vary widely between institutions, at times producing confusion over the competencies needed in student affairs assessment. This project evaluated syllabi from over 100 HESA graduate assessment and evaluation courses to develop a more robust understanding of the skill sets of entry-level student affairs practitioners entering the field from HESA graduate programs and the core outcomes and texts of student affairs assessment education. We describe student affairs as a field engaged in the process of professionalization (Perozzi & Shea, 2023, McGill et al., 2021) through the development of standardized knowledge and the ongoing integration of community-driven standards. Study findings illustrate that courses tended to focus on technical knowledge, such as data collection methods and analysis, over the political and contextual dimensions of assessment. Extant standards and competencies, as well as emerging topics and methods, were also incorporated inconsistently. Implications for faculty, practitioners, early career professionals, and professional associations are discussed.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".