Experiential Learning Practices and Career Courses: Predictors of First Destination Outcomes
Bibliographic record
Abstract
This study examined connections between experiential learning practices, mandatory career courses, and positive first destination outcomes of graduates from a large public institution prior to and during the COVID-19 pandemic. The rising cost of education and competition among universities has led to consumer demands for information pertaining to their return on investment. Universities have been encouraged to provide first-destination outcomes for graduates as a way to show the value of their degree programs. To strengthen these outcomes, many programs encourage or mandate experiential learning known as high-impact practices (HIPs) and career courses to prepare emerging adult learners for the transition to employment or further education. However, little is known as to the impact these practices have on the first-destination outcomes of these graduating students. This study identified specific experiential practices which proved beneficial to securing employment or acceptance to continued education, with an important caveat that career courses added only limited benefit for some respondents. These findings will better inform the allocation of university teaching resources and are a basis for further study of career practices in higher 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".