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

ABSTRACT: 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.

SUBMITTER: D'Mello S

PROVIDER: S-EPMC5640167 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

D'Mello Sidney S Dieterle Ed E Duckworth Angela A

Educational psychologist 20170221 2

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 ...[more]

PMID: 29038607

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Project description:Student engagement in online learning enhance students performance and the outcomes of the learning process in online learning environment. The existed literature revealed various factors influencing student engagement in online leaning, however these studies were before the COVID-19 crisis. The purpose of the current paper is to explore the factors that influence student engagement in online learning during the COVID-19 crisis in middle school settings in developing countries where is a lack of studies about the factors influencing student's engagement in emergency remote learning during the crisis. A qualitative approach was used for data collection and analysis. Semi-structured interviews with 34 participants (14 students, 13 teachers, and 7 parents) were conducted for 20-30 min. Furthermore, online class observations were used for data collection; 13 online classes were observed. Each class was 40 min. A thematic analysis was used to categorize the findings into themes and subthemes. The findings of the study revealed that various factors influence student engagement in online learning during the crisis including infrastructure factors, cultural factors, digital inequality, and the threat to digital privacy. Cultural factors were the important factor that influences females because of parents' culture and their bias against females using online learning compared to male students. Teachers' presence and quality of content were the major factors that influence student engagement, where parental concerns, norms, and traditions emerged as the major factors in the crisis, influencing engagement. Most of the participants reported that teaching and learning online during the crisis has broadened the digital inequality and threatened their digital privacy which influenced negatively student engagement. The limitations of this research included the limited number of participants covering a large geographic area, and the research design using diverse and often limited educational software and delivery methods. Future studies could utilize a mixed-method approach and include more participants.Supplementary informationThe online version contains supplementary material available at 10.1007/s10639-021-10566-4.

Dataset Information

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

Publications

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

Similar Datasets

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