The Effect of Increasing Student Involvement with Career Development Services: The Integration of Faculty Members & Fostering Student/Faculty Relationships
Bibliographic record
Abstract
The primary goal of this research is to recognize and disseminate the possible components of what makes students more involved with career development services on campus. We examined 25 scholarly articles as part of our initial research to identify possible relationships, which lead us to one major question to answer: “how can we increase the student usage of the MacEwan University Career Development and Experiential learning office by integrating faculty members of various departments?” Some of the largest problems that we found when speaking with the Career Development services and professors is that both students and faculty are either unaware of what they are, and what services they provide. Our qualitative research with faculty members has indicated that they do not know of the availability of career development services on campus, and they do not communicate with the office very frequently. This research allowed us to formulate a well-rounded quantitative survey to be administered to other faculty members that reflects on possible solutions to create more student involvement, and by extension – more student success. Our sample data included 28 responses of our possible 361 survey questionnaires sent. We did not have the greatest response rate; therefore, our findings are not fully generalizable. However, the responses that we did receive are very important and informative to the career development services of MacEwan University, which helps aid in conclusions and recommendations for student involvement.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".