Exploring Outcome Expectations in Artificial Intelligence and Internet of Things in First-Year Engineering Students (Work in Progress)

Andrea Ramirez-Salgado; Pavlo Antonenko

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Modern Teaching Strategies in Engineering
Tagged Division: Electrical and Computer Engineering Division (ECE)
Permanent URL: https://peer.asee.org/47422

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Paper Authors

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Andrea Ramirez-Salgado University of Florida orcid.org/0000-0002-0001-2672

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Andrea is a doctoral candidate in Curriculum and Instruction at the University of Florida, specializing in Educational Technology. Her work centers on understanding the dynamics of teaching and learning approaches that shape the identity of computer engineers to support computer engineering career choices, particularly in women first-year engineering students. She is committed to designing inclusive curricula that cater to the needs of diverse learners, guided by principles of Universal Design for Learning and Culturally Responsive/Sustaining Pedagogies.

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Pavlo Antonenko

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Pavlo "Pasha" Antonenko is an Associate Professor of Educational Technology at the University of Florida. His interests focus on the design of technology-enhanced learning environments and rigorous mixed-method research on the effective conditions for tec

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Abstract

For the United States to sustain its competitive edge and leadership in Artificial Intelligence (AI) and its intersection with Internet of Things (IoT) hardware technologies, a vital focus must be placed on fostering the growth and development of its specialized technical workforce in the Electrical and Computer Engineering (ECE) and other related fields. This strategic focus is crucial given the escalating demand for proficiency in critical domains like embedded systems paired with machine learning, sensor-driven big data analytics, edge computing, and cybersecurity. The combination of AI and IoT, known as AIoT, embodies the convergence of advanced technologies that rely on seamless collaboration between AI algorithms and IoT infrastructure. This integration drives innovation and efficiency across various industries, highlighting the urgent need for a skilled computing workforce to propel the nation's technological advancement. However, many engineering students may lack exposure to AIoT concepts and may not fully grasp the potential career opportunities in this field. This lack of awareness could hinder their engagement and limit their ability to contribute effectively. Our study aims to bridge this gap by examining first-year engineering students' outcome expectations (OE) after their participation in an undergraduate AIoT hands-on module. In this context, OE delineates individuals' anticipated beliefs regarding the outcomes or results they envision through their engagement in a specific activity – in this instance, an AIoT module. These expectations would influence individuals' motivation, commitment, and perseverance within the field. Positive outcome expectations bolster motivation and commitment, whereas negative expectations may impede progress and discourage sustained involvement. Our study investigates changes in OE before and after exposure to AIoT concepts and activities. We aim to understand how this exposure influences students' perceptions of career opportunities and motivates them to further explore these concepts throughout their undergraduate studies. In the Fall of 2023, a total of twenty-two first-year engineering students joined an undergraduate and participated in an 8-week AIoT module. This module covered essential topics, including introductory coding and sensor-data-driven machine learning, AIoT, and EdgeAI, with no prerequisites for enrollment. To assess the students' outcome expectations, they completed a pre and post-survey at the beginning and at the end of the semester and participated in semi-structured interviews. Survey results revealed a positive increase in participants' overall OE regarding AIoT technologies after the module. These quantitative findings were supported by the qualitative data collected indicating the ways in which the instructional module impacted their perceptions and attitudes toward AIoT technologies,

Citation
Format

Ramirez-Salgado, A., & Antonenko, P. (2024, June), Exploring Outcome Expectations in Artificial Intelligence and Internet of Things in First-Year Engineering Students (Work in Progress) Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47422

TY  - CPAPER
AB  - For the United States to sustain its competitive edge and leadership in Artificial Intelligence (AI) and its intersection with Internet of Things (IoT) hardware technologies, a vital focus must be placed on fostering the growth and development of its specialized technical workforce in the Electrical and Computer Engineering (ECE) and other related fields. This strategic focus is crucial given the escalating demand for proficiency in critical domains like embedded systems paired with machine learning, sensor-driven big data analytics, edge computing, and cybersecurity. The combination of AI and IoT, known as AIoT, embodies the convergence of advanced technologies that rely on seamless collaboration between AI algorithms and IoT infrastructure. This integration drives innovation and efficiency across various industries, highlighting the urgent need for a skilled computing workforce to propel the nation&#39;s technological advancement. However, many engineering students may lack exposure to AIoT concepts and may not fully grasp the potential career opportunities in this field. This lack of awareness could hinder their engagement and limit their ability to contribute effectively.
Our study aims to bridge this gap by examining first-year engineering students&#39; outcome expectations (OE) after their participation in an undergraduate AIoT hands-on module. In this context, OE delineates individuals&#39; anticipated beliefs regarding the outcomes or results they envision through their engagement in a specific activity – in this instance, an AIoT module. These expectations would influence individuals&#39; motivation, commitment, and perseverance within the field. Positive outcome expectations bolster motivation and commitment, whereas negative expectations may impede progress and discourage sustained involvement. Our study investigates changes in OE before and after exposure to AIoT concepts and activities. We aim to understand how this exposure influences students&#39; perceptions of career opportunities and motivates them to further explore these concepts throughout their undergraduate studies.
In the Fall of 2023, a total of twenty-two first-year engineering students joined an undergraduate and participated in an 8-week AIoT module. This module covered essential topics, including introductory coding and sensor-data-driven machine learning, AIoT, and EdgeAI, with no prerequisites for enrollment. To assess the students&#39; outcome expectations, they completed a pre and post-survey at the beginning and at the end of the semester and participated in semi-structured interviews. Survey results revealed a positive increase in participants&#39; overall OE regarding AIoT technologies after the module. These quantitative findings were supported by the qualitative data collected indicating the ways in which the instructional module impacted their perceptions and attitudes toward AIoT technologies, 
AU  - Andrea Ramirez-Salgado
AU  - Pavlo Antonenko
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Exploring Outcome Expectations in Artificial Intelligence and Internet of Things in First-Year Engineering Students (Work in Progress)
UR  - https://peer.asee.org/47422
ER  -