Modeling and Studying Gaming the System with Educational Data Mining

Ryan S. J. d. Baker, A. T. Corbett, I. Roll, K. R. Koedinger, V. Aleven, M. Cocea, A. Hershkovitz, A. M. J. B. de Caravalho, A. Mitrovic, M. Mathews

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter, we will discuss our work to understand why students game the system. This work leverages models of student gaming, termed “detectors”, which can infer student gaming in log files of student interaction with educational software. These detectors are developed using a combination of human observation and annotation, and educational data mining. We then apply the detectors to large data sets, and analyze the detectors’ predictions, using discovery with models methods, to study the factors associated with gaming behavior. Within this chapter, we will discuss the work to develop these detectors, and what we have discovered through these analyses based on these detectors. We will discuss evidence for how gaming the system impacts learning and evidence for why students choose to game. We will also discuss attempts to address gaming the system through adaptive scaffolding.
Original languageEnglish
Title of host publicationInternational Handbook of Metacognition and Learning Technologies
EditorsRoger Azevedo, Vincent Aleven
Place of PublicationNew York, NY
PublisherSpringer New York
Pages97-115
Number of pages19
Volume28
ISBN (Electronic)9781441955463
ISBN (Print)9781441955463
DOIs
StatePublished - 2013

Keywords

  • Cognitive Tutor
  • Educational Data Mining (EDM)
  • Educational Software
  • Interactive Learning Environment
  • Tutoring System

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