Hart

Ocumpaugh, Jaclyn; Baker, Ryan S.; Rodrigo, Ma. Mercedes T.; Salvi, Aatish; Velsen, Martin van; Aghababyan, Ani; Martin, Taylor

doi:10.1145/2775441.2775480

Cited by 14 publications

(3 citation statements)

References 23 publications

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“…Although this study did not implement association analyses to identify the relationship between game mechanics and learning engagement, it attempted to use behavioral analyses to quantify students’ learning engagement when performing various gameplay actions. Furthermore, Ocumpaugh, Baker, and Rodrigo (2015) introduced a behavior-analysis protocol, demonstrating the ways to quantify students’ occurrences of behaviors and affective states. This behavior analysis protocol was employed by several empirical studies to investigate associations between students’ learning engagement and interactive stimuli in GBL environments.…”

Section: Literature Reviewmentioning

confidence: 99%

In-Game Actions to Promote Game-Based Math Learning Engagement

Moon

2019

Journal of Educational Computing Research

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Game-based learning (GBL) has increasingly been used to promote students’ learning engagement. Although prior GBL studies have highlighted the significance of learning engagement as a mediator of students’ meaningful learning, the existing accounts failed to capture specific evidence of how exactly students’ in-game actions in GBL enhance learning engagement. Hence, this mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement). This study used and examined the game E-Rebuild, a single-player three-dimensional architecture game that requires learners’ application of math knowledge. Using in-depth gameplay behavior analysis, this study sampled a total of 92 screen-recorded and video-captured gameplay sessions attended by 25 middle school students. We adopted two analytic approaches: sequential analysis and thematic analysis. Whereas sequential analysis explored which in-game actions by students were likely to promote each type of learning engagement, the thematic analysis depicted how certain gameplay contexts contributed to students’ enhanced learning engagement. The study found that refugee allocation and material trading actions promoted students’ content engagement, whereas using in-game building tools and learning support boosted their cognitive engagement. This study also found that students’ learning engagement was associated with their development of mathematical thinking in a GBL context.

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Section: Literature Reviewmentioning

confidence: 99%

In-Game Actions to Promote Game-Based Math Learning Engagement

Moon

2019

Journal of Educational Computing Research

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“…They collected training data from 229 students who used ASSISTments in their school computer lab as part of their mathematics classes. Researchers made online observations (annotations) of students’ boredom, frustration, engaged concentration, and confusion using the Baker-Rodrigo Observation Method Protocol (BROMP) (Ocumpaugh, Baker, & Rodrigo, 2012), recently renamed the Baker-Rodrigo Observation Monitoring Protocol (Ocumpaugh, Baker, & Rodrigo, 2015). The observations were based on explicit actions towards the interface, interactions with peers and teachers, body movements, gestures, and facial expressions.…”

Section: Case Studiesmentioning

confidence: 99%

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

D’Mello

Dieterle²,

Duckworth

2017

Educational Psychologist

182

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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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“…Research assistants were trained using the BROMP (Ocumpaugh, Baker, & Rodrigo, 2015) and the Human Affect Recording Tool (HART) app (Ocumpaugh, Baker, Rodrigo, Salvi, et al, 2015b), which enables quantitative observations of behavior in field settings using momentary time sampling (see Baker, Ocumpaugh, & Andres, 2020). The BROMP has been used successfully in prior research with elementary, middle, and high school students (e.g., Baker et al, 2004; Godwin et al, 2016; Godwin et al, 2021; Ocumpaugh, Baker, & Rodrigo, 2015; Pardos et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

Classroom Design and Children's Attention Allocation: Beyond the Laboratory and into the Classroom

Godwin

Leroux

Scupelli

et al. 2022

Mind Brain and Education

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Prior laboratory research suggests the visual environment can be a source of distraction for children, reducing attention to instructional tasks and learning outcomes. However, systematic research examining how the visual environment relates to attention in genuine classrooms is rare. In addition, it is unknown what specific aspects of the environment pose a challenge for attention regulation. This observational study aims to (1) provide a nuanced examination of specific elements of the classroom visual environment (e.g., visual noise, display quantity, color variability) by analyzing panoramic classroom photographs (N = 58) and (2) investigate whether specific visual environment elements are related to children's rates of on‐task behavior. Results indicate on‐task behavior was lower in classrooms containing greater quantities of visual noise and color variability, and in classrooms with either relatively small or large amounts of displays (controlling for observation session, school type, student gender, grade‐level, and instructional format). Implications for creating more optimal visual learning environments are discussed.

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Hart

Cited by 14 publications

References 23 publications

In-Game Actions to Promote Game-Based Math Learning Engagement

In-Game Actions to Promote Game-Based Math Learning Engagement

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

Classroom Design and Children's Attention Allocation: Beyond the Laboratory and into the Classroom

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