Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues

By Roger Azevedo. 2015

399 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Education

View Paper: https://www.semanticscholar.org/paper/664e2a7406ba8b5c0f402d79ec2b0b21433cd626

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In Computer Science Applications

In Developmental and Educational Psychology

In Education

Citations in the corpus

Mobile technology, learning, and achievement: Advances in understanding and measuring the role of mobile technology in education (2020, background): “Likewise, the engagement often assumed in studies and theories of informal learning has numerous aspects (i.e., cognitive, behavioral, motivational, emotional or affective; Azevedo, 2015) that could and should be measured and understood to best leverage the affordances of informal environmental for ”
Understanding student behavioral engagement: Importance of student interaction with peers and teachers (2018, background): “…theoretical conceptualizations of student engagement have raised questions about how to measure student engagement and how engagement varies not only across schools, but within schools and within classrooms (Azevedo, 2015; Cooper, 2014; Fredericks et al., 2004; Sinatra, Heddy, & Lombardi, 2015).”
Student Engagement: Current State of the Construct, Conceptual Refinement, and Future Research Directions (2021, methodology): “However, as we reviewed above, research on this topic of interest has been impeded by various conceptual, theoretical, methodological, measurement, and analytical issues (Azevedo, 2015).”
Academic Emotional Learning: A Critical Component of Self-Regulated Learning in the Emotional Learning Cycle (2019, background): “Taking a fine-grained approach focused on specific tasks, Azevedo (2015) proposed an array of behavioral indicators to decipher cognition, affect, metacognition, and motivation.”
What multimodal data can tell us about the students’ regulation of their learning process? (2019, methodology): “…have been useful to explain learning trajectories in self-regulation and (Blikstein & Worsley, 2016) and the role of in key processes of metacognition, cognition, motivation and emotion in self-regulated learning (Azevedo, 2015), SRL in social context is still difficult to explain and understand.”
Measuring Student Engagement in the Online Course: The Online Student Engagement Scale (OSE). (2015, background): “Student engagement has been conceptualized in multiple ways across researchers and disciplines (Azvedo, 2015).”