Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

Merten, Christina; Conati, Cristina

doi:10.1145/1111449.1111465

Cited by 35 publications

(18 citation statements)

References 8 publications

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“…[20]. However, existing approaches [3,17,22,23,28] concentrate mainly on computer-driven educational activities. This work broadens the perspective by employing attention monitoring in a real classroom and incorporating a mechanism for suggesting improvements for the learning process; most importantly though, it empowers educators to customize or even create from scratch new inattention detection rules (e.g., "if the students whisper while the educator is writing to the whiteboard…") and intervention strategies.…”

Section: Background Theorymentioning

confidence: 99%

LECTOR: Towards Reengaging Students in the Educational Process Inside Smart Classrooms

Korozi

Leonidis

Antona

et al. 2017

Intelligent Human Computer Interaction

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Abstract. This paper presents LECTOR, a system that helps educators in understanding when students have stopped paying attention to the educational process and assists them in reengaging the students to the current learning activity. LECTOR aims to take advantage of the ambient facilities that "smart classrooms" have to offer by (i) enabling educators to employ their preferred attention monitoring strategies (including any well-established activity recognition techniques) in order to identify inattentive behaviors and (ii) recommending interventions for motivating distracted students when deemed necessary. Furthermore, LECTOR offers an educator friendly design studio that enables teachers to create or modify the rules that trigger "inattention alarms", as well as tailor the intervention mechanism to the needs of their course by modifying the respective rules. This paper presents the rationale behind the design of LECTOR and outlines its key features and facilities.

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Section: Background Theorymentioning

confidence: 99%

LECTOR: Towards Reengaging Students in the Educational Process Inside Smart Classrooms

Korozi

Leonidis

Antona

et al. 2017

Intelligent Human Computer Interaction

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“…Many of these attentive or gaze-aware interfaces are also quite domain-specific, with examples including reading, menu selection, scrolling, or information presentation (Bolt, 1981;Starker and Bolt, 1990;Sibert and Jacob, 2000;Hyrskykari et al, 2003;Vertegaal, 2004, 2005;Iqbal and Bailey, 2004;Ohno, 2004Ohno, , 2007Spakov and Miniotas, 2005;Hyrskykari, 2006;Merten and Conati, 2006;Kumar et al, 2007;Bulling et al, 2011).…”

Section: Gaze Control: Computer Robot and Swarm Controlmentioning

confidence: 99%

Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location

Taylor

Bilgrien

et al. 2015

Front. ICT

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Objective: We aimed to elucidate how our domain-general cuing algorithm improved multitasking performance and changed behavioral strategies in human operators.Background: Though many gaze-control systems have been designed, previous realtime gaze-aware assistance systems were not both successful and domain-general. It is largely unknown what constitutes optimal search efficiency using the eyes, or ideal control using the mouse. It is unclear what the best coordinating strategies are between these two modalities. Our previously developed closed-loop multitasking aid drastically improved multitasking performance, though the behavioral mechanisms through which it acted were unknown. Methods:We performed in-depth analyses and generated novel eye tracking and mouse movement measures, to explore the complex effects of our helpful system on gaze and motor behavior.Results: Our overlay cuing algorithm improved control efficiency and reduced wellknown biases in search patterns. This system also reduced micromanaging behavior, with humans rationally relying more on imperfect automation in experimental assistance cue conditions. We showed that mouse and gaze were more independently specialized in the helpful cuing condition than in control conditions. Specifically, with our aid, the gaze performed more global movement, and the mouse performed more local clustered movement. Further, the gaze shifted toward search over processing with the helpful cuing system. We also illustrated a relationship between the mouse and the gaze, such that in these studies, "the hand was quicker than the eye." Conclusion:Overall, results suggested that our cuing system improved performance and reduced short-term working memory load on humans by delegating it to the computer in real time. Further, it reduced the number of required repeated decisions by an estimate of about one per second. It also enabled the gaze to specialize for improved visual search behavior, and the mouse to specialize for improved control.

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“…They were originally developed to support learners with disabilities (i.e., assistive technologies); however, many are being created or repurposed to support learner models for both cognitive and noncognitive student data. As an example, obtaining information about where on the computer the learner is looking during learning provides evidence about the learner's current state and attentiveness (for good reviews of eye-tracking research, see Conati et al, 2005;Merten and Conati, 2006). This information can inform the system about what is the next optimal path to take for this particular learner.…”

Section: Biologically Based Devicesmentioning

confidence: 99%

Adaptive Educational Systems

Shute¹,

Zapata‐Rivera²

2012

Adaptive Technologies for Training and Education

145

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This paper elaborates on the need for adaptive educational systems, describes an evidence-based, four-process framework for analyzing adaptive educational systems, and reviews several adaptive technologies and types of adaptive learning environments. In addition, interviews with experts in the field inform our discussion on what to adapt, how to adapt, and the future of adaptive educational systems.

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Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

Cited by 35 publications

References 8 publications

LECTOR: Towards Reengaging Students in the Educational Process Inside Smart Classrooms

LECTOR: Towards Reengaging Students in the Educational Process Inside Smart Classrooms

Human Strategies for Multitasking, Search, and Control Improved via Real-Time Memory Aid for Gaze Location

Adaptive Educational Systems

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