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A Random Forest Model for Personalized Learning in a Narrative Game

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

NSF Grantees: Learning Tools (Virtual)

Tagged Topics

Diversity and NSF Grantees Poster Session

Page Count

13

DOI

10.18260/1-2--34041

Permanent URL

https://cms.jee.org/34041

Download Count

36

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Paper Authors

biography

Ying Tang Rowan University

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Ying Tang received the B.S. and M.S. degrees from the Northeastern University, P. R. China, in 1996 and 1998, respectively, and Ph.D degree from New Jersey Institute of Technology, Newark, NJ, in 2001. She is currently a Professor of Electrical and Computer Engineering (ECE) at Rowan University, Glassboro, NJ. Her research interests include virtual reality and augmented reality, artificial intelligence, and modeling and scheduling of computer-integrated systems. Dr. Tang is very active in adapting and developing pedagogical methods and materials to enhance engineering education. Her most recent educational research includes the collaboration with Tennessee State University and local high schools to infuse cyber-infrastructure learning experience into the pre-engineering and technology-based classrooms, the collaboration with community colleges to develop interactive games in empowering students with engineering literacy and problem-solving, the integration of system-on-chip concepts across two year Engineering Science and four year ECE curricula, and the implementation of an educational innovation that demonstrates science and engineering principles using an aquarium. Her work has resulted in over 100 journal and conference papers and book chapters.

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biography

Ryan Hare Rowan University

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Ryan Hare received his B.S. in Electrical and Computer Engineering from Rowan University in 2019. He is currently pursuing his M.S. in Electrical and Computer Engineering at Rowan University. His current research focus is applying machine learning and games to enhance student education, particularly in STEM fields.

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Abstract

In student education, learning styles can vary wildly from one student to the next. While students should receive support tailored to their specific learning style, this type of personalized support can often not be realized due to resource constraints. This paper presents an implementation of personalized learning support utilizing a random forest machine learning model built on top of an existing narrative game environment. The existing game, Gridlock, is a domain-specific narrative game that implements metacognitive strategies to assist students in learning sequential logic design, a core topic in Computer Engineering and Computer Science. The metacognitive strategies featured in the game are Roadmap, What I Know-What I Want to Know-What I Need to Solve (KWS), and Think-Aloud-Share-Solve (TA2S). Roadmap provides students with an idea of what they have learned and what they still need to learn. KWS prompts students to remember what they already know, what they want to know, and what they are trying to solve to keep them focused and on task. TA2S encourages students to think aloud by communicating with fellow classmates to share their solutions and collaborate to solve problems. On top of existing learning strategies within the game, a random forest machine learning model is used to classify students into various categories based on their learning path. To train this model, a large dataset was generated based on previously gathered information from tests of the game as well as in-classroom observations of students playing through the game. The model was verified through multiple runs with students of varying levels of subject knowledge. As they play through the game, students are classified based on their perceived knowledge of the subject matter presented to them. From this classification, students can be provided individualized assistance in the form of tutorials, hints, prompts, or even videos of experts solving similar problems. These tailored prompts provide students with immediate feedback in their areas of difficulty, maintaining the momentum of the learning process and improving student comprehension.

Tang, Y., & Hare, R. (2020, June), A Random Forest Model for Personalized Learning in a Narrative Game Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34041

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