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Record 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 on OpenAlexaff
Juan Alvarez, Olga Mironenko, Yang Shao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsInternshipPeer mentoringMedical educationPsychologyWork (physics)Process (computing)PedagogyEngineeringMedicineComputer science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.380
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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