Abstract
Models of self-regulated learning (SRL) describe the complex and dynamic interplay of learners’ cognitions, motivations, and behaviors when engaged in a learning activity. Recently, researchers have begun to use fine-grained behavioral data such as think aloud protocols and log-file data from educational software to test hypotheses regarding the cognitive and metacognitive processes underlying SRL. Motivational states, however, have been more difficult to trace through these methods and have primarily been studied via pre- and posttest questionnaires. This is problematic because motivation can change during an activity or unit and without fine-grained assessment, dynamic relations between motivation, cognitive, and metacognitive processes cannot be studied. In this chapter we describe a method for collecting fine-grained assessments of motivational variables and examine their association with cognitive and metacognitive behaviors for students learning mathematics with intelligent tutoring systems. Students completed questionnaires embedded in the tutoring software before and after a math course and at multiple time points during the course. We describe the utility of this method for assessing motivation and use these assessments to test hypotheses of self-regulated learning and motivation. Learners’ reports of their motivation varied across domain and unit-level assessments and were differently predictive of learning behaviors.
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Bernacki, M.L., Nokes-Malach, T.J., Aleven, V. (2013). Fine-Grained Assessment of Motivation over Long Periods of Learning with an Intelligent Tutoring System: Methodology, Advantages, and Preliminary Results. In: Azevedo, R., Aleven, V. (eds) International Handbook of Metacognition and Learning Technologies. Springer International Handbooks of Education, vol 28. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-5546-3_41
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