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Enregistrement W4401312740 · doi:10.18260/1-2--48389

Board 89: Work in Progress: Promoting Undergraduate Student Success through Faculty Mentoring in Engineering Education

2024· article· en· W4401312740 sur OpenAlexaff
Juan Alvarez, Olga Mironenko, Yang Shao

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePsychology
ThématiqueMentoring and Academic Development
Établissements canadiensYork UniversityUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésInternshipPeer mentoringMedical educationPsychologyWork (physics)Process (computing)PedagogyEngineeringMedicineComputer science

Résumé

récupéré en direct d'OpenAlex

Abstract The transition from high school to college can present significant challenges for students, creating a need for a strong support system. In modern engineering education, mentoring has emerged as an important component in supporting the growth and success of undergraduate students. It is generally recognized that relationships with faculty members impact student success[1]. Mentoring has gained significant attention for its role in providing personalized guidance and fostering a sense of belonging within the community. Mentors can help students to deal with academic challenges and make informed decisions[2]. Furthermore, the mentor-mentee relationship establishes a nurturing atmosphere dedicated to enhancing academic performance. Most of the research in this area focuses on mentoring research activities between students and advisors, as well as peer advising. However, there has been limited attention given to a more general advising role. This role includes assisting in course selection, technical interests, finding internships or research opportunities, graduate school applications, extracurricular activities, study abroad, offering support for personal or mental health concerns, etc. Some work has been done in this direction[3]. This work in progress aims to understand the needs and expectations of students who are supported by a faculty mentoring process in an Electrical and Computer Engineering department in a large public university. Currently, the program involves students meeting their assigned faculty mentors once per semester. However, the approach varies among different faculty members. Meetings can take the form of one-on-one private conversations or group sessions, allowing for peer mentoring. They can also occur either in person or online. The topics covered during these sessions are diverse, as previously mentioned. One-on-one mentoring can provides highly personalized guidance and support, while group mentoring can offer diverse perspectives from the student peers and provide networking opportunities. Peer mentoring has been shown to increase both retention and self-esteem among college students [4]. These mentoring meetings are mandatory for students, and failing to attend the meeting results in a hold on their upcoming semester's class registration. Students are responsible for scheduling appointments based on faculty members' availability calendars. Three faculty members within the department requested their mentees to voluntarily participate in a survey aimed to understand their experiences and preferences regarding various aspects of the mentoring process. This includes topics covered during the meetings or that would be beneficial to cover, resources provided or that would be beneficial to provide, as well as the duration and frequency of these meetings, among other aspects. Additionally, these three faculty members themselves completed a survey to gain a better understanding of their perspective on the mentoring process. In this work, we discuss the findings from these surveys and include recommendations for enhancing the efficiency and effectiveness of the mentoring process. References: [1] M. S. Jaradat, and M. B. Mustafa, "Academic Advising and Maintaining Major: Is There a Relation?," Social Sciences, vol. 6, no. 4, pp. 151, 2017. [2] Lucietto, A. M., & Dell, E., & Cooney, E. M., & Russell, L. A., & Schott, E. (2019, June), Engineering Technology Undergraduate Students: A Survey of Demographics and Mentoring Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. [3] Banerjee, J. K. (2020, June), Mentoring Undergraduate Students in Engineering Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34968 [4] R. Collings, V. Swanson, and R. Watkins, "The impact of peer mentoring on levels of student wellbeing, integration and retention: a controlled comparative evaluation of residential students in UK higher education," Higher Education, vol. 68, no. 6, pp. 927- 942, 2014.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,190
Score d'incertitude au seuil0,703

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,380
Écart entre enseignants0,347 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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