An Initial Analysis Of Freshman To Sophomore Retention In A New First Year Engineering Program
Notice bibliographique
Résumé
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,
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».