Undergraduate Research Conference

Undergraduate Research Conference

Author: Jason Morphew
Event Date: April 8, 2025
Undergraduate researchers present their research projects

Strategies for Implementation of Microelectronics in Engineering Education STEM

Author: Camille Leigh Johnson† (Polytechnic)

Abstract: This study investigates the impact of student engagement using microelectronics within engineering education, specifically in the FirstYear Engineering (FYE) curriculum. Using Social Cognitive Career Theory (SCCT), data was collected through 18 semi-structured interviews. These results were interpreted using codes with SCCT as the main guideline for each interview. Results indicate that students taking the course's interest and persistence regarding microelectronics either increased or remained consistent throughout the course. Prior experiences with microelectronics and programming influenced how students navigated the course. A team-based format of the course leads to increased and decreased engagement based on students' prior experiences and the general dynamic of their teams. However, students approached the course with varying levels of knowledge about microelectronics. This shaped the perception of their persistence and interest. This study highlights the potential microelectronic interventions have to create engaging learning opportunities and foster the future engineering workforce. 

Engaging the Mind and Body: A Meta-Analysis of Interactive Learning in Education STEM

Authors: Manas Kathuria† (Engineering); Zhengyi Jiang* (Science); Aashvi Miten Majmundar* (Engineering)

Abstract: Science, Technology, Engineering, and Mathematics (STEM) education disciplines are priorities for economic development in this technologically changing world, but the traditional education processes teaching our next generation of engineers and scientists are lagging behind. Educators have turned towards more interactive and embodied learning techniques with the potential for bridging this gap between abstract concepts and real-world applications. Past research has demonstrated the potential of interactive and embodied learning methods to enhance student engagement and understanding in STEM fields. Studies have shown that incorporating physical activity, virtual technologies, and gamified learning environments can improve spatial reasoning and problem-solving skills. Augmented Reality (AR) and Virtual Reality (VR) have been found to enhance spatial understanding, while embodied design approaches encourage students to actively construct knowledge through sensorymotor experiences. These findings highlight the need for innovative approaches that move beyond traditional lecture-based approaches. In our lab, we are exploring the integration of AR and gesture-based learning to enhance the teaching and understanding of fundamental statistics concepts, creating a more interactive and immersive learning experience. Our research begins with a series of in-depth interviews and observational studies to identify natural gestures that intuitively align with core statistical concepts. These gestures are carefully analyzed for their effectiveness in representing abstract ideas and their potential to improve comprehension. Preliminary findings suggest that students who engage with these immersive environments demonstrate improved ability to reason about concepts. 

Embodied Learning in Statistics: Using Gestures to Enhance Statistical Reasoning of Regression and Correlation STEM

 
Authors(s): Zhengyi Jiang† (Science); Manas Kathuria* (Engineering); Aashvi Miten Majmundar* (Engineering)
 
Abstract: Several students struggle with understanding fundamental statistical concepts such as regression, correlation, distribution and best-fit lines. These concepts are pivotal to success in STEM education, scientific application, and data-driven decision-making. A strong foundation in STEM education is essential for students beginning in K-12 and for matriculation into undergraduate careers in engineering, computer science, and other technological fields. Statistics is a fundamental area of study and cuts across STEM, finance and humanities while playing an important role in developing students’ ability and interest to analyze data, make evidence-based decisions, and solve real-world problems. Prior research in embodied learning highlights the benefits and role of embodied cognition in developing STEM learning, through gestures in teaching and learning. This is because gestural learning shows evidence of supporting students’ understanding, providing additional sensory and cognitive clues and support instructions. However, sparse research delved into the application of cueing gestures and how students’ intuitive gestures connect to their ideas, and reasoning in statistics. This study explores first-year engineering students’ understanding of linear regression, correlation and delving into their interpretation Best-Fit lines through both embodied expressions of gestures and verbal explanations. We analyze students’ reasoning processes in interviews and categorize their spontaneous gestures based on McNeill’s gesture framework. Preliminary results indicate students used embodiment and cueing gestures to convey their understanding of statistical concepts, these findings provide evidence of the potential benefits of embodied learning and teaching in statistics and STEM education and enhancing undergraduate students’ comprehension and applications of important statistical fundamental concepts.
 

Analyzing Gestures in Statistics Instruction: A Study on Digital Video Learning Environments STEM

 
Authors(s): Aashvi Miten Majmundar† (Engineering); Manas Kathuria* (Engineering); Zhengyi Jiang* (Science)
 
Abstract: For this project, I contrasted the use of pedagogical gestures in statistics teaching in digital video learning environments (DVLEs) to ascertain their effect on student learning. As a component of my scholarship in the I² Lab at Purdue University, I analyzed a collection of videos from assorted online sources, including YouTube and TeacherTube, that delivered statistics teaching through both human interaction and computer animation. I focused on 12 human-based videos where instructors used gestures to explain statistical concepts such as central tendency (mean, median, mode), variance, and quartiles.
I analyzed by coding the gestures based on the specific content being explained, discovering common hand movements, positions, and gestures that instructors used to explain key statistical terms. For example, I videotaped gestures such as inward curling of hands to explain central tendency or chopping motions to illustrate variance. I also analyzed how these gestures were related to the verbal explanations provided by the instructors and explored how they helped the students understand and engage with the material. This project is part of a larger project exploring how gestures in DVLEs can aid student learning, especially in self-directed, online learning environments. My study provides an insight into how teachers use bodily gestures to make abstract concepts more concrete, with the potential to inform the design of more effective online learning materials. This research contributes to the ongoing development of digital learning technologies that facilitate STEM learning, particularly in flipped and online learning environments.