Board 59: Coevolutionary-aided Teaching: Leveraging the Links Between Coevolutionary and Educational Dynamics

Alessio Gaspar; A.T.M. Golam Bari; Dmytro Vitel; Kok Cheng Tan; Jennifer Albert; Rudolf Paul Wiegand

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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: NSF Grantees Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 14
DOI: 10.18260/1-2--32383
Permanent URL: https://peer.asee.org/32383
Download Count: 423

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Paper Authors

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

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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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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 University of South Florida

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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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Jennifer Albert The Citadel

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Rudolf Paul Wiegand III University of Central Florida

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R. Paul Wiegand is an Associate Research Professor in the University of Central Florida's School of Modeling, Simulation, and Training. He directs the Advanced Research Computing Center at UCF and directs the Natural Computation & Coadaptive Systems lab. His research interests primarily focus on methods of natural computation, theory of coadaptive and coevolutionary computation, application of coadaptive methods for multiagent learning, and high performance / high throughput computing. More generally, he is interested in designing and applying effective learning algorithms and representations for generating and modeling robust heterogeneous, multiagent team behaviors.

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Abstract

The idea that a wide variety of domain-specific dynamics may be studied with a common set of mathematical tools can be traced back to Alexander Bogdanov's Tektology, Norbert Wiener's Cybernetics, and Ludwig Von Bertanlaffy's General Systems Theory. In this NSF IUSE project, we propose to leverage recent theoretical advances in the field of {\it Coevolutionary Computation} to study, from a novel perspective, student vs. practice problems interactions. Using what we learned from bridging the above-mentioned fields, we also propose an algorithmic approach to generate pedagogically-sound practice problems.

In a seminal coevolutionary computation work by Hillis, a population of sorting networks was evolved against a population of sequences to be sorted. The algorithm searched for sorting networks able to correctly sort sequences of numbers, while it also searched for sequences able to reveal flawed sorting networks. Similar competitive interactions are also common in educational scenarios. For instance, a group of students might work on improving their skills with practice problems that are designed by educators to be increasingly challenging.

This similitude enables us to consider two novel research agendas.

First, we may leverage coevolutionary computation theories to provide a new perspective on educational research. A large body of research in the field of coevolutionary computation has indeed been dedicated to investigating the reasons for which an ideal "arms race", in which both populations push each other to constantly improve, is actually difficult to obtain. The so-called pathological coevolutionary dynamics that have been identified have direct counterparts in the educational domain. It is therefore relevant to investigate the relevance of the solutions used to mitigate them in coevolutionary computation, and how they might apply to the educational domain.

Second, we may design new algorithms to coevolve practice problems against a population of learners. We describe such an approach and discuss the roles played in it by various pathological coevolutionary dynamics, as well as how we mitigated them.

Even more interestingly, rather than only adapting the learning experience to each learner individually, such a system has the potential to identify misconceptions plaguing students at large, thus potentially assisting educators in engaging in Computing Education research on what makes a given topic difficult to learners.

We discuss the above points in the context of the specific system that has been developed in this NSF award. Based on preliminary evaluations on both simulated benchmarks and actual students, we then make recommendations for the next step of a research agenda focused on what we termed {\it Coevolutionary-Aided Teaching} systems, and their potential to contribute to discipline-based educational research.

Citation
Format

Gaspar, A., & Bari, A. G., & Vitel, D., & Tan, K. C., & Albert, J., & Wiegand, R. P. (2019, June), Board 59: Coevolutionary-aided Teaching: Leveraging the Links Between Coevolutionary and Educational Dynamics Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32383

TY - CPAPER
AB - The idea that a wide variety of domain-specific dynamics may be studied with a common set of mathematical tools can be traced back to Alexander Bogdanov&#39;s Tektology, Norbert Wiener&#39;s Cybernetics, and Ludwig Von Bertanlaffy&#39;s General Systems Theory.
In this NSF IUSE project, we propose to leverage recent theoretical advances in the field of {\it Coevolutionary Computation} to study, from a novel perspective, student vs. practice problems interactions. Using what we learned from bridging the above-mentioned fields, we also propose an algorithmic approach to generate pedagogically-sound practice problems.

In a seminal coevolutionary computation work by Hillis, a population of sorting networks was evolved against a population of sequences to be sorted.
The algorithm searched for sorting networks able to correctly sort sequences of numbers, while it also searched for sequences able to reveal flawed sorting networks.
Similar competitive interactions are also common in educational scenarios. For instance, a group of students might work on improving their skills with practice problems that are designed by educators to be increasingly challenging.

This similitude enables us to consider two novel research agendas.

First, we may leverage coevolutionary computation theories to provide a new perspective on educational research.
A large body of research in the field of coevolutionary computation has indeed been dedicated to investigating the reasons for which an ideal &quot;arms race&quot;, in which both populations push each other to constantly improve, is actually difficult to obtain.
The so-called pathological coevolutionary dynamics that have been identified have direct counterparts in the educational domain.
It is therefore relevant to investigate the relevance of the solutions used to mitigate them in coevolutionary computation, and how they might apply to the educational domain.

Second, we may design new algorithms to coevolve practice problems against a population of learners.
We describe such an approach and discuss the roles played in it by various pathological coevolutionary dynamics, as well as how we mitigated them.

Even more interestingly, rather than only adapting the learning experience to each learner individually, such a system has the potential to identify misconceptions plaguing students at large, thus potentially assisting educators in engaging in Computing Education research on what makes a given topic difficult to learners.

We discuss the above points in the context of the specific system that has been developed in this NSF award.
Based on preliminary evaluations on both simulated benchmarks and actual students, we then make recommendations for the next step of a research agenda focused on what we termed {\it Coevolutionary-Aided Teaching} systems, and their potential to contribute to discipline-based educational research.
AU - Alessio Gaspar
AU - A.T.M. Golam Bari
AU - Dmytro Vitel
AU - Kok Cheng Tan
AU - Jennifer Albert
AU - Rudolf Paul Wiegand III
CY - Tampa, Florida
DA - 2019/06/15
PB - ASEE Conferences
TI - Board 59: Coevolutionary-aided Teaching: Leveraging the Links Between Coevolutionary and Educational Dynamics
UR - https://peer.asee.org/32383
DO - 10.18260/1-2--32383
ER -