Developing a generalizable detector of when students game the system

Baker, Ryan S.; Corbett, Albert T.; Roll, Ido; Koedinger, Kenneth R.

doi:10.1007/s11257-007-9045-6

Cited by 142 publications

(109 citation statements)

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“…[6]). The experimental procedure was as follows: college students with no college-level physics (1) read a short physics text, (2) took a multiple choice pretest, (3) worked with ITSPOKE, (4) took a survey, and (5) took an isomorphic posttest. The resulting corpus contains 360 spoken dialogs (5 per student) from 72 students (6044 student turns).…”

Section: Computer Tutoring Disengagement Datamentioning

confidence: 99%

“…Student (dis)engagement behaviors have been of particular interest in this research, including displays of gaming, boredom, indifference, (lack of) interest, (low) motivation, curiosity, and flow (e.g. [7,3,9,11,12]). Correlational analyses of student (dis)engagement behaviors in tutoring system corpora have indicated that these behaviors are predictive of learning.…”

Section: Introductionmentioning

confidence: 99%

“…Correlational analyses of student (dis)engagement behaviors in tutoring system corpora have indicated that these behaviors are predictive of learning. For example, gaming [3,1] and boredom [9] have been associated with decreased learning during computer tutoring, while flow [9] and engagement [4] have been associated with increased learning. In addition, a number of automatic gaming detectors have been implemented and evaluated in computer tutors, with results indicating that gaming behaviors can be reliably detected in real-time using features of the tutoring interaction (cf.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, a number of automatic gaming detectors have been implemented and evaluated in computer tutors, with results indicating that gaming behaviors can be reliably detected in real-time using features of the tutoring interaction (cf. [3]). Moreover, controlled experiments using gaming-adaptive computer tutors -i.e., tutors enhanced with interventions that target student gaming -have shown that adapting to gaming can improve student learning [2,3] or other performance metrics (such as reducing gaming) [13,1].…”

Section: Introductionmentioning

confidence: 99%

“…[3]). Moreover, controlled experiments using gaming-adaptive computer tutors -i.e., tutors enhanced with interventions that target student gaming -have shown that adapting to gaming can improve student learning [2,3] or other performance metrics (such as reducing gaming) [13,1].…”

Section: Introductionmentioning

confidence: 99%

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When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?

Forbes-Riley¹,

Litman²

2011

Lecture Notes in Computer Science

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Abstract. We investigate whether an overall student disengagement label and six different labels of disengagement type are predictive of learning in a spoken dialog computer tutoring corpus. Our results show first that although students' percentage of overall disengaged turns negatively correlates with the amount they learn, the individual types of disengagement correlate differently with learning: some negatively correlate with learning, while others don't correlate with learning at all. Second, we show that these relationships change somewhat depending on student prerequisite knowledge level. Third, we show that using multiple disengagement types to predict learning improves predictive power. Overall, our results suggest that although adapting to disengagement should improve learning, maximizing learning requires different system interventions depending on disengagement type.

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Section: Computer Tutoring Disengagement Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?

Forbes-Riley¹,

Litman²

2011

Lecture Notes in Computer Science

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Data mining and education

Koedinger

D’Mello

McLaughlin

et al. 2015

WIRES Cognitive Science

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An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board.

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Guessing Model

Baker¹

2012

Encyclopedia of the Sciences of Learning

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Developing a generalizable detector of when students game the system

Cited by 142 publications

References 20 publications

When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?

When Does Disengagement Correlate with Learning in Spoken Dialog Computer Tutoring?

Data mining and education

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