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Quantifying the Effects of Concept Maps on Student Learning

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Conference

2025 ASEE Annual Conference & Exposition

Location

Montreal, Quebec, Canada

Publication Date

June 22, 2025

Start Date

June 22, 2025

End Date

August 15, 2025

Conference Session

DSAI Technical Session 9: Student Reflections, Metacognition, and Competency Mapping

Tagged Division

Data Science and Artificial Intelligence (DSAI) Constituent Committee

Page Count

12

DOI

10.18260/1-2--57090

Permanent URL

https://peer.asee.org/57090

Download Count

1

Paper Authors

biography

Paromita Nath Rowan University Orcid 16x16 orcid.org/0000-0001-8955-5579

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Dr. Paromita Nath is an Assistant Professor in Mechanical Engineering at Rowan University. She earned her Ph.D. in Civil Engineering from Vanderbilt University. She is passionate about advancing engineering education through machine learning and data analysis, building on her expertise in uncertainty quantification, Bayesian inference, process design and control under uncertainty, and probabilistic digital twin. Her research spans diverse applications, including additive manufacturing and public health.

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biography

Melanie Amadoro Rowan University

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Professor Amadoro has been teaching undergraduate engineering courses since 2016. She strives to increase student learning experiences through the use of innovative tools and methods. Her goal is to make traditional engineering courses interactive and applicable to real world problem solutions.

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Abstract

Concept maps are valuable tools for visualizing relationships between key ideas, improving students' understanding and retention of concepts, particularly in complex fields such as engineering. This study investigates the impact of iterative concept map creation on student learning in mechanical engineering courses over a 15-week semester. Additionally, a survey was conducted to gather student feedback on how the concept map assignment influenced their engagement, ability to connect ideas, and capacity to apply the concepts learned in class. By integrating both quantitative and qualitative data, the analysis combines statistical methods, correlation analysis, and insights derived from natural language processing tools to provide a comprehensive understanding of the pedagogical impact of concept maps. Findings reveal no significant correlation between students' course grades and their concept map scores. However, survey feedback underscores their positive influence, particularly in fostering connections between ideas and improving the application of course knowledge, suggesting that traditional scoring may not fully capture the educational value of concept maps. Overall, this study highlights the potential of concept maps as an effective pedagogical tool in engineering education.

Nath, P., & Amadoro, M. (2025, June), Quantifying the Effects of Concept Maps on Student Learning Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--57090

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