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Data-Driven Investigation of Curiosity in Student Text Responses

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Topics in Computing

Tagged Division

Computing and Information Technology

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Paper Authors


Naeem Seliya Ohio Northern University

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Dr. Naeem (Jim) Seliya, PhD., is an Associate Professor of Computer Science at Ohio Northern University, Ada, Ohio, USA, where he currently teaches Mobile App Development, Data Science, Software Engineering, Software Design Patterns, Net-Centric Computing, and Theory of Computation. His key expertise and interests include: Data Science (i.e., Machine Learning, Big Data Analytics, Deep Learning, Data Quality, Data Visualization, Data Wrangling, and Feature Engineering); Software Engineering and Systems Development; Computing Sciences Pedagogy; Assistive Technology for Persons with Disabilities and the Elderly; Cyber Security Analytics, and Interdisciplinary Data Analytics. He has published about 90 peer-reviewed technical articles in international conferences, journals, and book chapters. Dr. Seliya is proactive in computing sciences scholarship and pedagogy enhancement, including grants, undergraduate research, and curriculum and course development. His prior professional endeavors include: Assistant (& Associate) Professor of Computer and Information Science at the University of Michigan-Dearborn; Adjunct Instructor of Computer Science and Technology at the State University of New York, Orange; and, President (Co-Founder) and Senior Software Engineer at Health Safety Technologies, LLC.

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Heath Joseph LeBlanc Ohio Northern University Orcid 16x16

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Heath J. LeBlanc is an Associate Professor in the Electrical & Computer Engineering and Computer Science Department at Ohio Northern University. He received his MS and PhD degrees in Electrical Engineering from Vanderbilt University in 2010 and 2012, respectively, and graduated summa cum laude with his BS in Electrical Engineering from Louisiana State University in 2007. His research interests include cooperative control of networked multi-agent systems, resilient and fault-tolerant control, and networked control systems. His teaching interests include controls and automation, electric circuits, signals and systems, engineering economics, electromagnetics, and integrating the entrepreneurial mindset with an engineering mindset in core engineering courses. He received the Professor Henry Horldt Outstanding Teaching Award in 2015.

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J. Blake Hylton Ohio Northern University Orcid 16x16

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Dr. Hylton is an Assistant Professor of Mechanical Engineering and Coordinator of the First-Year Engineering experience for the T.J. Smull College of Engineering at Ohio Northern University. He previously completed his graduate studies in Mechanical Engineering at Purdue University, where he conducted research in both the School of Mechanical Engineering and the School of Engineering Education. Prior to Purdue, he completed his undergraduate work at the University of Tulsa, also in Mechanical Engineering. He currently teaches first-year engineering courses as well as various courses in Mechanical Engineering, primarily in the mechanics area. His pedagogical research areas include standards-based assessment and curriculum design, including the incorporation of entrepreneurial thinking into the engineering curriculum and especially as pertains to First-Year Engineering.

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Ziad Youssfi Ohio Northern University

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My current research focuses on image processing, GPU, and optimizing computer architecture to reduce chip power consumption. Before joining Ohio Northern University in 2013, I taught digital circuit design for two semesters at the University of Western Ontario. I earned my PhD, MS, and BS in Electrical & Computer Engineering from Michigan State University in East Lansing. Before completing my PhD, I joined Intel Incorporation to work on the P6 line of processors and chipsets. While pursuing my PhD, I developed a financial web application that helped colleges at Michigan State University to streamline their finances.

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Matthew Schweinefuss

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Learning how to learn and apply the new knowledge is a vital skill students need to develop. A student’s curiosity in exploring more about a topic lends toward learning that knowledge. In recent years’ text mining has seen increasing focus on investigating sentiment, feelings, behavior, intent, etc. for various purposes, including linguistic understanding, product marketing, and pedagogical improvements. Our project focuses on a relatively unique area, i.e., Curiosity Detection in Text. This “Work in Progress” paper presents preliminary, but promising, results of empirically data mining curiosity in student-produced text data.

Psychologists have wrestled with understanding the nature of curiosity. Recent work by Grossnickle has provided a framework for understanding facets, factors and dimensions of the construct of curiosity that are relevant to the education audience. The key dimensions identified in the framework for curiosity include: focus of curiosity (with four factors of physical, perceptual, social, and epistemic), scope of curiosity (breadth vs. depth), cause of curiosity (diversive vs. specific or interest vs. deprivation), and consistency of curiosity across situational contexts (state vs. trait).

The success of our project will positively impact efforts to assess both curiosity and its impact on educational outcomes. We applied a tool developed by the Right Question Institute, called Question Formulation Technique (QFT), in an Electric Circuits (EC) course to improve students’ ability to formulate questions and deepen their curiosity on EC. The solutions developed will be useful to detect whether curiosity is demonstrated in the results of the QFT exercises, provide analysis on key dimensions of curiosity, and predict associated behaviors of students’.

The QFT student answers from five labs of the EC course are sentences provided in response to a thought-provoking topic, e.g., “Why doesn’t the U.S. adopt SI units”. As per the QFT approach, students respond using a question formulation in incremental steps. The sentences were converted into a token-based numeric format using a text-to-feature conversion and a stop-words list. Each unique word (token) is a feature in the obtained data set. The target class, “Potential for Exploration”, determined by two experts as a measure for student curiosity, has three categories: 1 for Novice, 2 for Intermediate, 3 for Advanced.

The goal of this phase was to identify important words that effectively capture curiosity using data mining. Feature Selection (FS) was performed to reduce the feature space due to data sparsity. Three Wrapper-based approaches (C4.5 decision tree with BestFirst, GreedyStepwise, and Evolutionary search algorithms) and three Filter-based Rankers (ChiSquare, ReliefF, and GainRatio) were used for FS. The C4.5 decision tree was used to classify the “Potential for Exploration”. The wrappers indicated BestFirst and GreedyStepwise generally yielded similar feature sets, while Evolutionary provided much larger feature set. Filters provided less relative similarity, except for a small group of similar words with ChiSquare and GainRatio rankers.

The experts agreed that key features selected were effective representation of students’ curiosity levels. We plan to continue work with: studying curiosity improvement trends, studying curiosity trends across courses, larger data sets, other learners, using linguistic assessment techniques.

Seliya, N., & LeBlanc, H. J., & Hylton, J. B., & Youssfi, Z., & Schweinefuss, M. (2019, June), Data-Driven Investigation of Curiosity in Student Text Responses Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32577

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