Detecting Gaming the System in Constraint-Based Tutors

Baker, Ryan S.; Mitrović, Antonija; Mathews, Moffat

doi:10.1007/978-3-642-13470-8_25

Cited by 33 publications

(10 citation statements)

References 17 publications

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“…Identifying situations where the system has been gamed has been the focus for many researchers in recent years. Additional discussions on mining student datasets can be found in [156] [157].…”

Section: Data Mining In Itssmentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author's views regarding future work in the field of intelligent tutoring systems. General Terms

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Section: Data Mining In Itssmentioning

confidence: 99%

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan¹,

Kalita²

2019

IJCA

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“…Previous automated detectors of disengaged behavior have largely focused on identifying the specific undesirable behavior studied (Baker & Carvalho, 2008;Baker, Mitrovic, & Mathews, 2010;Cetintas, Si, Xin, & Hord, 2009). By contrast, the rules produced by our detector are targeted more toward identifying what is not DTG behavior than identifying what is DTG behavior.…”

Section: What Does Our Detector Reveal About Disengagement In Inq-its?mentioning

confidence: 93%

Operationalizing and Detecting Disengagement Within Online Science Microworlds

Gobert¹,

Baker

Wixon³

2015

Educational Psychologist

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In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States needs skilled workers. This article lays out some issues important for framing research on this topic and provides a review of some existing work with similar goals on engagement in science learning. Specifically, here we seek to help better concretize engagement, a fuzzy construct, by operationalizing and detecting (i.e., identifying using a computational method) disengaged behaviors that are antithetical to engagement. We, in turn, describe our real-time detector (i.e., machine learned model) of disengaged behavior and how it was developed. Last, we address our ongoing research on how our detector of disengaged behavior will be used to intervene in real time to better support students' science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring

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“…It allows linking learning analytics work that identifies user engagement problems, e.g. gaming the system, mind wandering, confusion, frustration, off-task interaction (Baker et al, 2010;Bosch & D'Mello, 2019;Faber et al, 2018;Hutt et al, 2019;Mills et al, 2020;Paquette & Baker, 2019;Peters et al, 2018) with interaction design to offer choices leading to better engagement with the system to improve learning. Taking a Choice Architecture point of view will allow comparing different interaction features (nudges) and combining them in a flexible way to improve the system.…”

Section: Choice Architecture and Its Implicationsmentioning

confidence: 99%

Choice Architecture for Nudges to Support Constructive Learning in Active Video Watching

Dimitrova

Mitrović

2021

Int J Artif Intell Educ

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Video-based learning is widely used today in both formal education and informal learning in a variety of contexts. Videos are especially powerful for transferable skills learning (e.g. communicating, negotiating, collaborating), where contextualization in personal experience and ability to see different perspectives are crucial. With the ubiquity of widely available video content, video-based learning is seen as one of the main strategies to provide engaging learning environments. However, numerous studies show that to learn effectively while watching videos, students need to engage actively with video content. We have developed an active video watching platform (AVW-Space) to facilitate engagement with video content by providing means for constructive learning. The initial studies with AVW-Space on presentation skills show that only students who commented on videos and who rated comments written by their peers have improved their understanding of the target transferable skill. In order to foster deeper engagement, we designed a choice architecture and a set of nudges to encourage students to engage deeper. We conducted two studies investigating the effect of nudges. The results provide evidence that the initial set of implemented nudges is effective: the students who received nudges wrote more comments, used different aspects, and there were more students who wrote comments. The nudges were particularly helpful for undergraduate students who were less experienced in self-regulated learning. Future work includes designing additional nudges to enhance student engagement by improving the quality of comments and by encouraging participation in collaborative activities.

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Detecting Gaming the System in Constraint-Based Tutors

Cited by 33 publications

References 17 publications

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Operationalizing and Detecting Disengagement Within Online Science Microworlds

Choice Architecture for Nudges to Support Constructive Learning in Active Video Watching

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