Building a Better Transfer Community: Improving Engagement and Advising of Prospective Transfer Students
Notice bibliographique
Résumé
The College of Engineering at NC State has consistently admitted 200-300 transfer students each academic year over the past decade.These students ultimately make up about one quarter of the graduating class in the College of Engineering each year.During this time, annual reports from the College of Engineering regularly reported that approximately 50% of engineering transfer students transferred from one of the 58 institutions in the North Carolina Community College (NC CC) system.This data was extracted from the Office of Admissions database, which reports only the last institution attended, as listed by the transfer applicant.Over the summer/fall 2013 (n=259) and summer/fall 2014 (n=293) transfer cohorts, we collected data on all institutions attended by admitted transfers students and learned that 70% of transfer students each year have previously enrolled at a NC CC.Yet, very few resources were devoted specifically to educating and recruiting prospective transfer students from NC CC's.Instead, students and advisors have historically relied almost entirely on information provided on the NC State website.On these websites, transfer requirements and engineering curricula are provided along with a NC CC equivalency worksheet to assist with course selection prior to transfer.The College of Engineering has been the beneficiary of strong 3+2 dual degree and 2+2 partnerships (TPT's) with institutions like Elon University, Meredith College and the University of North Carolina at Wilmington.Students transferring from these institutions are provided high-quality advising and enjoy a high acceptance rate due to the strong communication pathways between the College of Engineering and the pre-engineering advisors at these partner institutions.The students at the local North Carolina Community College, Wake Technical Community College, also benefit from accurate, high-quality advising due in large part to the diligence of their pre-engineering advisor who is able to attend advising meetings and stay in regular contact with Engineering Academic Affairs.Inspired by the success of these strong relationships with our TPT partners and motivated by NC State's strategic plan to expand the proportion of transfer students, the College of Engineering teamed with the College of Education at NC State.We established initiatives aimed directly at NC CC's and their communities: 1) to disseminate accurate information regarding engineering at NC State and 2) to develop a valuable network of higher education institutions in North Carolina.In addition to promoting the transfer of high quality students into engineering at NC State, our goal was to improve their retention and performance.In this paper, we detail the strategies we employed to achieve these goals, including programming and publications created by the College of Engineering, targeted solely to NC CC students and advisors.We describe key components of the efforts, share feedback from our NC CC colleagues and transfer students, and discuss best practices for other institutions seeking to create or strengthen their ties with community colleges. Background and motivation
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 | 0,009 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,011 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,004 |
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 ».