An Initial Analysis Of Freshman To Sophomore Retention In A New First Year Engineering Program
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Initial Analysis of Freshman-to-Sophomore Retention in a New First-Year Engineering Program Abstract During the 2007-2008 academic year, the University of Arkansas (UofA) implemented the Freshman Engineering Program (FEP),a new first-year experience program for engineering students. The FEP was originally proposed to the UofA engineering faculty as an effort to improve the retention of new engineering students from their freshman to their sophomore years. As a result, the activities of the Academic and Student Services Sub-Programs executed by the faculty and staff of the FEP are all intended to improve students’ likelihood of academic success and/or to increase students’ desire to pursue an engineering degree. Since improving freshman- to-sophomore retention was a primary goal of the FEP, a significant amount of data has been collected on each of the 343 students enrolled in the first FEP cohort. This data includes demographic information, ACT (or similar) scores, high school GPA, Advances Placement (or similar) scores, scholarship data, fall 2007 class schedule and grades, spring 2008 enrollment data, spring 2008 class schedule and grades, fall 2008 enrollment data, and information related to the process used by students in selecting their engineering major for the sophomore year. Our primary objective in constructing this data set is to facilitate the completion of an exploratory data analysis to examine the interrelationships among the variables in hopes of identifying more effective methods for predicting student success in engineering. The long-term goal is to use the information and models obtained from this analysis to identify intervention programs that will promote increased retention rates for these students. In this paper, we present what we view to be the most interesting results of our initial analysis of this data. These results will range from tabulated counts from selected categories of the data to statistical models of relationships between these categories. We also present a brief synopsis of the activities associated with the executing of the Academic and Student Services Sub-Programs of the FEP. Review of the Literature A plethora of research has been generated regarding the prediction of success in college (Young and Korbin11; Burton and Ramist4; Ting8; Pennock-Roman7; Wilson10; Bamforth et al.1). However, a growing concern among researchers is the ability to retain students in the quantitative fields like math, science, and engineering. Retention of students is defined as either graduation or concurrent enrolment in a specific academic field. Without retention of students in math- and science-based fields, national and local economies suffer due to the increased demand for such research and development professionals (NARSET Report6). Retaining students is a growing concern in many university departments, especially in the field of engineering. According to the National Access and Retention in Science, Engineering and Technology (NARSET) Report6, two factors determine the success of economic development in a country: 1) the amount and quality of human resources available, and 2) the extent of the research and development capacity. Without a retention and attraction program in place, the supply of graduates from the fields of science, engineering, and technology is unlikely to significantly grow. Identification and targeting of factors which influence retention is critical to the future growth of university engineering programs. Through the identification of prediction factors,
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".