Where Low and High Inference Data Converge: Validation of CLASS Assessment of Mathematics Instruction Using Mobile Eye Tracking with Expert and Novice Teachers

Cortina, Kai S.; Miller, Kevin F.; McKenzie, R P; Epstein, Alanna

doi:10.1007/s10763-014-9610-5

Cited by 99 publications

(150 citation statements)

References 19 publications

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“…Fourthly, the criterion for choosing a steering group is not clear-cut across teachers: again, teachers' amount of experience better predicted their steering groups-related behavior than the characteristics of their students, in terms of behavior or performance. Eventually, we found a small relationship between teachers' gazing time and the quality of the classroom climate, replicating Cortina et al's [15] results, as well as a more obvious relationship between the teachers' attentional lability and many of their CLASS scores.…”

Section: Discussionsupporting

confidence: 89%

“…Cortina et al [15] used a mobile eye-tracker to study the gaze behavior of 24 teachers (12 novices and 12 experienced). They analyzed the relationship between the quality of the classroom climate (using the CLASS, see below for more information), and the level of attention teachers devoted to each student of the classroom, computed with the Gini coefficient (ranging from 0: all students have the same number of fixations, to 1: only one student gets all the fixations).…”

Section: Eye-tracking Devices and Teacher Decision Makingmentioning

confidence: 99%

“…We computed Gini coefficients to measure teachers' attention distribution in interactive exercises [15], appropriate when the variable (in our case, attentional focus, or gazing time) is not independently distributed among students: if a given student is subject to focus, there leaves less chances of attentional focus to the others). The Gini coefficient ranges from 0 (all students get the same number of fixations) to 1 (one student gets all the fixations).…”

Section: Relationship Between the Attention Focus And The Classroom Cmentioning

confidence: 99%

See 2 more Smart Citations

“Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students

Dessus

Cosnefroy

Luengo

2016

Lecture Notes in Computer Science

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International audienceThis study aims at investigating which cues teachers detect and process from their students during instruction. This information capturing process depends on teachers' sensitivity, or awareness, to students' needs, which has been recognized as crucial for classroom management. We recorded the gaze behaviors of two pre-service teachers and two experienced teachers during a whole math lesson in primary classrooms. Thanks to a simple Learning Analyt-ics interface, the data analysis reports, firstly, which were the most often tracked students, in relation with their classroom behavior and performance; secondly, which relationships exist between teachers' attentional frequency distribution and lability, and the overall classroom climate they promote, measured by the Classroom Assessment Scoring System. Results show that participants' gaze patterns are mainly related to their experience. Learning Analytics use cases are eventually presented, enabling researchers or teacher trainers to further explore the eye-tracking data

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Section: Discussionsupporting

confidence: 89%

Section: Eye-tracking Devices and Teacher Decision Makingmentioning

confidence: 99%

Section: Relationship Between the Attention Focus And The Classroom Cmentioning

confidence: 99%

See 1 more Smart Citation

“Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students

Dessus

Cosnefroy

Luengo

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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“…Researchers have studied how beginning and experienced teachers showed different teaching performance (Borko & Livingston, 1989;Cardona, 2008;Cortina, Miller, McKenzie, & Epstein, 2015;Leinhardt, 1989), and there have been many studies comparing experienced and beginning teachers in general. However, there were very few studies contrasting experienced and beginning teachers in terms of teaching performance in classroom.…”

Section: Beginning and Experienced Teachersmentioning

confidence: 99%

“…Experience and beginning teachers showed differences in the dimensions of content knowledge, pedagogical knowledge, and pedagogical content knowledge (Cortina et al, 2015). Among these dimensions, the differences in pedagogical knowledge and pedagogical content knowledge between experience and beginning teachers have been illuminated (Berliner, 2001;Cortina et al, 2015).…”

Section: Beginning and Experienced Teachersmentioning

confidence: 99%

Examining the Pattern of Middle Grade Mathematics Teachers’ Performance: A Concurrent Embedded Mixed Methods Study

Han

Cetin²,

Matteson³

2016

EURASIA J MATH SCI T

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What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

Kröger

Lutz

Müller

2020

IFIP Advances in Information and Communication Technology

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Technologies to measure gaze direction and pupil reactivity have become efficient, cheap, and compact and are finding increasing use in many fields, including gaming, marketing, driver safety, military, and healthcare. Besides offering numerous useful applications, the rapidly expanding technology raises serious privacy concerns. Through the lens of advanced data analytics, gaze patterns can reveal much more information than a user wishes and expects to give away. Drawing from a broad range of scientific disciplines, this paper provides a structured overview of personal data that can be inferred from recorded eye activities. Our analysis of the literature shows that eye tracking data may implicitly contain information about a user's biometric identity, gender, age, ethnicity, body weight, personality traits, drug consumption habits, emotional state, skills and abilities, fears, interests, and sexual preferences. Certain eye tracking measures may even reveal specific cognitive processes and can be used to diagnose various physical and mental health conditions. By portraying the richness and sensitivity of gaze data, this paper provides an important basis for consumer education, privacy impact assessments, and further research into the societal implications of eye tracking.

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Where Low and High Inference Data Converge: Validation of CLASS Assessment of Mathematics Instruction Using Mobile Eye Tracking with Expert and Novice Teachers

Cited by 99 publications

References 19 publications

“Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students

“Keep Your Eyes on ’em all!”: A Mobile Eye-Tracking Analysis of Teachers’ Sensitivity to Students

Examining the Pattern of Middle Grade Mathematics Teachers’ Performance: A Concurrent Embedded Mixed Methods Study

What Does Your Gaze Reveal About You? On the Privacy Implications of Eye Tracking

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