Enrollment and Persistence of First-year Students in a Newly Accredited Engineering Program
Bibliographic record
Abstract
Abstract First-Year Engineering Students in Newly Accredited Programs: Enrollment and Persistence DemographicsThis paper examines enrollment and persistence trends among first-year students in recentlyaccredited electrical (EE) and mechanical (ME) engineering programs at a predominantlyundergraduate-oriented, non-research intensive, historically Liberal Arts university where theprograms grew from an existing technology department. Until now, the programs have relied ona convenience sample of students with minimal program promotion or recruitment.Transcript information and surveys of students enrolled in an introductory (first-year)engineering course were collected during the first six years of the programs. Transcript dataincludes: semesters of enrollment and graduation, grades in math courses taken before andduring the first-year course, first-year course grade and current grade-point-average (GPA).Surveys given at the beginning of every semester asked students to rank their top three intendedmajors. Questions asked in surveys at semester completion included previous intended major,ranking of new top three intended majors, reason for the intended major changed. Distributionsof student interest and math preparedness upon enrollment in the introductory course wereanalyzed. Additionally, within-program and university persistence was quantified and comparedto math level and earned first-year course grade.Enrollment increases in the first-year course average 7%. One half and one quarter of thestudents plan to major in ME and EE, respectively, while one quarter plan on a different major.The GPAs and first-year course grades have stayed relatively constant.The academic plan for engineering students recommends taking Calculus I before or during thefirst-year course; students with MathLevels of Calculus or Post-Calculus are deemed “OnTrack”,others are deemed “Behind”. Currently, 48% of the students are OnTrack, and 15% are ahead inmath. For the Behind math students, persistence is poor, but for OnTrack students, persistence isacceptable. Additionally, per year, the number of students at each level is growing, but thedistribution is shifting towards Behind at a rate of 1.4%. This trend is problematic becausedoubling the number of On-Track students would require tripling first-year course enrollment.Results of this study may be informative for universities with new or under-developmentengineering programs. Results imply a need for concerted efforts at recruiting students who arewell prepared for success in engineering programs. Emphasis should be on attractingmathematically strong students to an introductory course, rather than on retaining morecurrently-enrolled freshman students.One way to improve the quality would be to add a precalculus pre-/co-requisite for the first-yearcourse. Doing so may reduce the first-year course enrollment significantly (<5%). Another step to improve qualitythat the School has already implemented is admission requirements with respect to GPA andcourse-specific grades. This step will further reduce the persistence of first-year course students,but presumably affects Behind students more than OnTrack students. The exact outcome ofthese strategies remains to be seen.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".