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On the Potential of Evolved Parsons Puzzles to Contribute to Concept Inventories in Computer Programming

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Technical Session 1: Issues Impacting Students Learning How to Program

Tagged Division

Computers in Education

Page Count

16

DOI

10.18260/1-2--33142

Permanent URL

https://peer.asee.org/33142

Download Count

123

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Paper Authors

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A.T.M. Golam Bari University of South Florida

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ATM Golam Bari, student member IEEE,
is a Ph.D. student in Computer Science & Engineering Department at University of South Florida, USA. He received the ME and BSc. degree in Computer Science & Engineering from Kyung-Hee University, South Korea and Dhaka University, Bangladesh, in 2013 and 2007, respectively. His main research interest involves Coevolutionary Algorithms, Dynamic Optimization, Bio-data mining.

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Alessio Gaspar University of South Florida

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Dr. Alessio Gaspar is an Associate Professor with the University of South Florida’s Department of Computer Science & Engineering and director of the USF Computing Education Research & Evolutionary Algorithm Laboratory. He received his Ph.D. in computer science in 2000 from the University of Nice Sophia-Antipolis (France). Before joining USF, he worked as visiting professor at the ESSI polytechnic and EIVL engineering schools (France) then as postdoctoral researcher at the University of Fribourg’s Computer Science department (Switzerland). Dr. Gaspar is an ACM SIGCSE, SIGITE and SIGEVO member and regularly serves as reviewer for international journals & conferences and as panelist for various NSF programs. His research interests include Evolutionary Algorithms, Computing Education Research, and applications to Computer-Assisted Teaching & Learning. His technology interests include Linux System Administration, Programming, Web App Development, and open source technologies in general.

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R. Paul Wiegand University of Central Florida, School of Modeling, Simulation, & Training

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Dmytro Vitel

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I was born in Ukraine, 1988. In 2011 I finished Taras Shevchenko National University of Kyiv and obtained degree Master of Science in Applied Physics. In August 2017, I was accepted into MSIT program at University of South Florida. Eventually, program was changed to MSCS.

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Kok Cheng Tan

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Kok Cheng Tan is a present PhD student of Computer Science at University of South Florida. He tends to work toward data science fields such as machine learning and data mining. He has eight-year teaching experiences and interested in exploring academical present trends.

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Stephen John Kozakoff University of South Florida

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Abstract

Our goal is to investigate whether techniques to automatically generate practice problems have also potential to assist in constructing Concept Inventories (CI) for computer programming. More specifically, we focus on a specific type of practice problem, Parsons puzzles, aimed at novice programmers. In this study, we propose EvoParsons - an automated way of evolving Parsons puzzle for newbie computer programmers, and more importantly we establish that EvoParsons can be a stepping stone of automating the process of building CI. EvoParsons is a software tool to improve students’ learning of computer programming. It is developed, maintained and distributed by our team. The state-of-the art techniques of building CI largely depends on several iterations of settings among faculties, interviews and surveys from students. This so called Delphi method largely depends on knowledge of domain experts, feedback from students, surveys etc. EvoParsons goal is to automate this process by applying Competitive Coevolutionary Algorithm (CCoEA) and Interactive Evolutionary Algorithm (IEA). In this paper, we first describe EvoParsons working mechanism, its benefits over other existing systems of generating Parsons puzzle. Second, we use EvoParsons’ interaction data with actual student to describe its potential to contribute for building CI. To do so, we perform data driven analysis of EvoParson’s misconceptions that are found at the last generational puzzle of the experiment. We also analyze its interaction log to investigate pedagogical importance of misconceptions in successive generations. Experimental analysis shows that EvoParsons evolves interesting misconceptions, discards trivial ones, maintains an order of misconceptions in its subsequent generations of evolution.

Bari, A. G., & Gaspar, A., & Wiegand, R. P., & Vitel, D., & Tan, K. C., & Kozakoff, S. J. (2019, June), On the Potential of Evolved Parsons Puzzles to Contribute to Concept Inventories in Computer Programming Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33142

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