Using Reading Times and Eye-Movements to Measure Cognitive Engagement

Miller, Brian

doi:10.1080/00461520.2015.1004068

Cited by 113 publications

(70 citation statements)

References 71 publications

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“…These include analyzing machine-readable aspects of a learning session, such as log files recorded during interactions with digital learning environments, facial features, eye gaze, and physiology. Several of these signals have a long history in the psychological sciences, including the measurement of cognitive engagement (Miller, 2015). However, they have mainly been used as passive data sources that humans analyze offline.…”

Section: The Advanced Analytic Automated (Aaa) Measurement Approachmentioning

confidence: 99%

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

D’Mello

Dieterle²,

Duckworth

2017

Educational Psychologist

187

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It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present15 case studies that illustrate the potential of the AAA approach for measuring engagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.

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Section: The Advanced Analytic Automated (Aaa) Measurement Approachmentioning

confidence: 99%

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

D’Mello

Dieterle²,

Duckworth

2017

Educational Psychologist

187

View full text Add to dashboard Cite

show abstract

“…Additionally, scientists have studied image interpretation and its effect on student cognitive load (Mills et al, 2016) by examining pupil dilatation in students (Stuijfzand et al, 2016) and studied cognitive engagement of students during science instruction (Miller, 2015). Often in conjunction with the variable of cognitive load, scientists explored mental rotation because of the manifestation of mental rotation in the participants' eyes (Roach, Fraswer, Kryklywy, Mitchell, & Wilson, 2015;Xu & Franconeri, 2015).…”

Section: Resultsmentioning

confidence: 99%

“…However, despite recent developments and applications for eye-tracking technology, limitations still exist. Miller (2015) found eye-tracking measures were still insufficient to gauge mind-wandering and emotional arousal, which are two areas in need of further exploration in studies pertaining to cognitive engagement and cognitive load.…”

Section: Resultsmentioning

confidence: 99%

Applying Eye-Tracking Research in Education and Communication to Agricultural Education and Communication: A Review of Literature

Leggette¹,

Rice²,

Carraway³

et al. 2018

JAE

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The purpose of this integrative literature review was to synthesize the eye-tracking literature related to education and communication in the Social Sciences Citation Index (SSCI) to formulate a conceptual model and develop a research agenda that guides eye-tracking research in agricultural education and communication. To do so, we reviewed the literature to identify basic elements of eye-tracking research, examined specific variables related to eye-tracking research, and synthesized the findings into a conceptual model. We found and reviewed 77 articles published between 2010 and 2016 within education (56) and communication (21). Research implications for agricultural education included an examination of classroom management practices, a comparison of novice and experienced instructors, and an exploration of educational settings. Research implications for agricultural communication included tracking fixation and frequency of eye movement when viewing advertisements and understanding the relationship between brand placement and buying decisions. Synthesizing the research in education and communication, we identified the audiences, concepts, environments, and variables related to eye tracking that could be investigated within agricultural education and communication. In conclusion, we found a lack of agricultural education and communication studies cited in the SSCI, eye-tracking technology provides variables to support a multivariate approach in agricultural education and communication, and eye-tracking equipment is expensive, which may limit diffusion in some settings.

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“…In the education literature these components are often termed affective learning, cognitive engagement, or simply engagement (Bloom and Krathwol ; Greene ; Krathwohl et al. ; Miller ). Falk and Dierking () connected these features of learning to the free‐choice nature of museums; visitors choose which exhibits to explore and for how long based on their own internal motivations.…”

Section: Identifying the Edge Design Attributesmentioning

confidence: 99%

Straight From the Girls: The Importance of Incorporating the EDGE Design Attributes at Exhibits

Garcia‐Luis¹,

Dancstep²

2019

Curator The Museum Journal

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This paper describes a follow‐up focus group study for the larger Exhibit Designs for Girls’ Engagement (EDGE) project. Grounded in Culturally Responsive Pedagogical theory (CRP), the project aimed to understand the relationship between female responsive designs and girls’ engagement at STEM exhibits. After developing a Female‐Responsive Design (FRD) Framework and conducting a large‐scale study to determine the most important design attributes for engaging girls at exhibits, the final step involved a qualitative investigation into those design attributes. Four focus groups with 22 girls aged 8–13 explored their exhibit experiences and discourse. Participants were videotaped while using four exhibits, half of which incorporated the EDGE Design Attributes. Focus group results replicate the previous quantitative study: participants had a largely positive response to the EDGE Design Attributes and evinced greater engagement when the attributes were present at exhibits. As expected, girls’ responsiveness to the design attributes varied— aligning with the CRP approach and highlighting the important fact that not all attributes will work for all girls. To share girls’ voices more broadly with practitioners we provide direct quotes for each design attribute. Finally, of the kinds of learning expressed, girls more often practiced scientific skills by repeating or varying activities at the EDGE exhibits. The results of this study, and the larger project, provide evidence that CRP works well when designing for females and serves as a valuable theory for creating museums that are truly inclusive.

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Using Reading Times and Eye-Movements to Measure Cognitive Engagement

Cited by 113 publications

References 71 publications

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning

Applying Eye-Tracking Research in Education and Communication to Agricultural Education and Communication: A Review of Literature

Straight From the Girls: The Importance of Incorporating the EDGE Design Attributes at Exhibits

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