Recruiting and Mentoring the Mentors: Practices from the STEM+C MentorCorps Project

Shaoping Qiu; Francis Quek; Malini Natarajarathinam; Ting Liu

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Conference: 2023 ASEE Annual Conference & Exposition
Location: Baltimore , Maryland
Publication Date: June 25, 2023
Start Date: June 25, 2023
End Date: June 28, 2023
Conference Session: Engineering Technology Division (ETD) Technical Session 5
Tagged Division: Engineering Technology Division (ETD)
Tagged Topic: Diversity
Page Count: 17
DOI: 10.18260/1-2--44035
Permanent URL: https://peer.asee.org/44035
Download Count: 111

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Paper Authors

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Shaoping Qiu Texas A&M University orcid.org/0000-0002-2857-4415

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Shaoping Qiu, Ph.D. is a Research Scientist in The Institute of Technology-Infused Learning (TITIL) at Texas A& M University. His research interests include organizational leadership, organizational change, stress and well-being, STEM education, service-learning, and quantitative methods such as multiple regression, structural equation modelling (SEM), hierarchical linear model (HLM), and item response theory (IRT).

https://scholar.google.com/citations?user=Yth6nlMAAAAJ&hl=en

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Francis Quek Texas A&M University

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Francis Quek is a Professor at Texas A&M University (Visualization, and Computer Science and Engineering and Psychology - by courtesy). He joined Texas A&M University as an interdisciplinary President’s Signature Hire to bridge disparities in STEM. Formerly he has been the Director of the Center for Human-Computer Interaction at Virginia Tech. Francis received both his B.S.E. summa cum laude (1984) and M.S.E. (1984) in electrical engineering from the University of Michigan. He completed his Ph.D. in Computer Science at the same university in 1990. Francis is a member of the IEEE and ACM.
He performs research in Human-Computer Interaction (HCI), learning sciences and technology, embodied interaction, embodied learning, HCI for accessibility (especially for individuals with blindness), multimodal verbal/non-verbal interaction, multimodal meeting analysis, vision-based interaction, multimedia databases, medical imaging, assistive technology for the blind, human computer interaction, computer vision, and computer graphics. http://teilab.tamu.edu

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Malini Natarajarathinam Texas A&M University orcid.org/0000-0003-1684-6476

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Dr. Malini Natarajarathinam joined the faculty of the Industrial Distribution Program at Texas A&M University in 2007. Natarajarathinam received her Ph.D. in Supply Chain Management from The University of Alabama. She received her Bachelor of Engineering

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Ting Liu

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Abstract

Computing has become integral to the practice of science, technology, engineering, and mathematics (STEM). One of our NSF-funded projects, the STEM + Computing program, attempts to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within STEM teaching and learning in early childhood education through high school (preK-12). In this STEM+C project, the 5th and 6th-grade students build science models to learn science and computing. The project is conducted in two intermediate schools in Texas that serve predominantly underprivileged populations. To support teaching computational content, we develop a Mentor Corps of college students to collaborate with intermediate schoolteachers in the classroom. This paper aims to report the practices and processes performed to recruit, select, train, mentor, and evaluate student mentors. After recruitment, student mentors receive professional development using multiple science models and language and pedagogy that highlight the representational power of models. Since computational modeling activities will be integrated into the regular intermediate school science classes, we mainly highlight how we mentor student mentors on computational modeling and science. Also, we intend to mentor the mentors with the purpose that they learn pedagogical methods to teach in the classes in such a way that students' passion and enthusiasm for engineering and science are triggered or enhanced. Further, as a research project, an array of data sources is gathered from intermediate school students, teachers, and mentors. Technical knowledge about data-collecting tools is conveyed to mentors so that they can assist the research team in collecting data in an ideal way. Our mentors are mostly recruited from engineering and science students at a large research university in the USA. The recruitment started in June 2022 and ended in September 2022. From 118 applicants, 40 students were selected. Five students opted out, ending up with a total of 35 mentors. Twenty-three are female students, while 12 are male. Two of them are master's students. The training and mentoring session takes place every Tuesday from 6:30 pm to 7:30 pm. In addition to the mentoring practices and processes, we will report the reflections and suggestions from student mentors to illustrate how they learn and progress. We will also utilize descriptive data and conduct t-tests regarding training and mentoring outcomes to determine whether students master the knowledge and pedagogy, therefore, are confident to teach the 5th and 6th-grade students. This report has several practical implications. Firstly, it will provide a roadmap for practitioners to follow regarding how to mentor engineering and science majors participating in STEM research projects. Secondly, researchers and practitioners in the engineering field will benefit from the practices and challenges faced in interacting with engineering and science major students. Thirdly, this report will assist researchers in cooperating with students and capitalizing on the resources to conduct research, as instructional materials and resources will be freely accessible.

Citation
Format

Qiu, S., & Quek, F., & Natarajarathinam, M., & Liu, T. (2023, June), Recruiting and Mentoring the Mentors: Practices from the STEM+C MentorCorps Project Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44035

TY - CPAPER
AB - Computing has become integral to the practice of science, technology, engineering, and mathematics (STEM). One of our NSF-funded projects, the STEM + Computing program, attempts to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within STEM teaching and learning in early childhood education through high school (preK-12). In this STEM+C project, the 5th and 6th-grade students build science models to learn science and computing. The project is conducted in two intermediate schools in Texas that serve predominantly underprivileged populations.
To support teaching computational content, we develop a Mentor Corps of college students to collaborate with intermediate schoolteachers in the classroom. This paper aims to report the practices and processes performed to recruit, select, train, mentor, and evaluate student mentors. After recruitment, student mentors receive professional development using multiple science models and language and pedagogy that highlight the representational power of models. Since computational modeling activities will be integrated into the regular intermediate school science classes, we mainly highlight how we mentor student mentors on computational modeling and science. Also, we intend to mentor the mentors with the purpose that they learn pedagogical methods to teach in the classes in such a way that students&#39; passion and enthusiasm for engineering and science are triggered or enhanced. Further, as a research project, an array of data sources is gathered from intermediate school students, teachers, and mentors. Technical knowledge about data-collecting tools is conveyed to mentors so that they can assist the research team in collecting data in an ideal way.
Our mentors are mostly recruited from engineering and science students at a large research university in the USA. The recruitment started in June 2022 and ended in September 2022. From 118 applicants, 40 students were selected. Five students opted out, ending up with a total of 35 mentors. Twenty-three are female students, while 12 are male. Two of them are master&#39;s students. The training and mentoring session takes place every Tuesday from 6:30 pm to 7:30 pm. In addition to the mentoring practices and processes, we will report the reflections and suggestions from student mentors to illustrate how they learn and progress. We will also utilize descriptive data and conduct t-tests regarding training and mentoring outcomes to determine whether students master the knowledge and pedagogy, therefore, are confident to teach the 5th and 6th-grade students.
This report has several practical implications. Firstly, it will provide a roadmap for practitioners to follow regarding how to mentor engineering and science majors participating in STEM research projects. Secondly, researchers and practitioners in the engineering field will benefit from the practices and challenges faced in interacting with engineering and science major students. Thirdly, this report will assist researchers in cooperating with students and capitalizing on the resources to conduct research, as instructional materials and resources will be freely accessible.

AU - Shaoping Qiu
AU - Francis Quek
AU - Malini Natarajarathinam
AU - Ting Liu
CY - Baltimore , Maryland
DA - 2023/06/25
PB - ASEE Conferences
TI - Recruiting and Mentoring the Mentors: Practices from the STEM+C MentorCorps Project
UR - https://peer.asee.org/44035
DO - 10.18260/1-2--44035
ER -