Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

Ryan Hare, Ying Tang, Wei Cui, Joleen Liang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

Original languageEnglish (US)
Title of host publication2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
PublisherIEEE Computer Society
Pages792-797
Number of pages6
ISBN (Electronic)9781728169040
DOIs
StatePublished - Aug 2020
Event16th IEEE International Conference on Automation Science and Engineering, CASE 2020 - Hong Kong, Hong Kong
Duration: Aug 20 2020Aug 21 2020

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2020-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Country/TerritoryHong Kong
CityHong Kong
Period8/20/208/21/20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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