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; Mihaela Cocea; A. Hershkovitz; A. M. J. B. de Caravalho; A. Mitrovic; M. Mathews

doi:10.1007/978-1-4419-5546-3_7

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, Mihaela Cocea, A. Hershkovitz, A. M. J. B. de Caravalho, A. Mitrovic, M. Mathews

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-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 language	English
Title of host publication	International handbook of metacognition and learning technologies
Editors	Roger Azevedo, Vincent Aleven
Place of Publication	New York
Publisher	Springer
Pages	97-115
ISBN (Electronic)	9781441955463
ISBN (Print)	9781441955456
DOIs	https://doi.org/10.1007/978-1-4419-5546-3_7
Publication status	Published - 2013

Publication series

Name	Springer international handbooks of education
Publisher	Springer
Number	28
ISSN (Print)	2197-1951

Access to Document

10.1007/978-1-4419-5546-3_7

http://dx.doi.org/10.1007/978-1-4419-5546-3_7

Impact of off-task and gaming behaviour on learning
Haig, E., Hershkovitz, A. & Baker, R. S. J. D.
1/07/08 → 31/07/08
Project: Research

Cite this

Baker, R. S. J. D., Corbett, A. T., Roll, I., Koedinger, K. R., Aleven, V., Cocea, M., Hershkovitz, A., Caravalho, A. M. J. B. D., Mitrovic, A., & Mathews, M. (2013). Modeling and studying gaming the system with educational data mining. In R. Azevedo, & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 97-115). (Springer international handbooks of education; No. 28). Springer. https://doi.org/10.1007/978-1-4419-5546-3_7

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title = "Modeling and studying gaming the system with educational data mining",

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{\textquoteright} 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.",

author = "Baker, {Ryan S. J. D.} and Corbett, {A. T.} and I. Roll and Koedinger, {K. R.} and V. Aleven and Mihaela Cocea and A. Hershkovitz and Caravalho, {A. M. J. B. de} and A. Mitrovic and M. Mathews",

year = "2013",

doi = "10.1007/978-1-4419-5546-3_7",

language = "English",

isbn = "9781441955456",

series = "Springer international handbooks of education",

publisher = "Springer",

number = "28",

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editor = "Roger Azevedo and Vincent Aleven",

booktitle = "International handbook of metacognition and learning technologies",

}

Baker, RSJD, Corbett, AT, Roll, I, Koedinger, KR, Aleven, V, Cocea, M, Hershkovitz, A, Caravalho, AMJBD, Mitrovic, A & Mathews, M 2013, Modeling and studying gaming the system with educational data mining. in R Azevedo & V Aleven (eds), International handbook of metacognition and learning technologies. Springer international handbooks of education, no. 28, Springer, New York, pp. 97-115. https://doi.org/10.1007/978-1-4419-5546-3_7

Modeling and studying gaming the system with educational data mining. / Baker, Ryan S. J. D.; Corbett, A. T.; Roll, I. et al.
International handbook of metacognition and learning technologies. ed. / Roger Azevedo; Vincent Aleven. New York: Springer, 2013. p. 97-115 (Springer international handbooks of education; No. 28).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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AB - 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.

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Modeling and studying gaming the system with educational data mining

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Impact of off-task and gaming behaviour on learning

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