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Understanding Reading Behaviors of Middle School Students

Published: 12 August 2020 Publication History
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  • Abstract

    Rich models of students' learning and problem-solving behaviors can support tailored interventions by instructors and scaffolding of complex learning activities. Our goal in this paper is to identify students' reading behaviors as they engage with instructional texts in domain-specific activities. In this work, we apply theory and methodology from the learning sciences to a large-scale middle school dataset within a digital literacy platform, Actively Learn. We compare students' reading behaviors both within and across domains for 12,566 science and 16,240 social studies students. Our findings show that higher-performing students in science engaged in more metacognitively-rich reading activities, such as text annotation; whereas lower-performing students relied more on simple highlighting and took longer to respond to embedded questions. Higher-performing students in social studies, by contrast, engaged more with the vocabulary and took longer to read before attempting question responses. Our finding may be used as recommendations to help both teachers and students engage in and support more effective behaviors.

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    This study focuses on identifying domain-specific reading and self-regulated learning (SRL) behaviors for middle school science and social studies students. We utilize log trace data from an online reading platform, Actively Learn (AL). We compare students? reading and SRL behaviors both within and across domains for 12,566 science and 16,240 social studies students. After consulting previous SRL literature, we identify three AL reading-support features as SRLs: highlighting, annotating, and vocabulary lookups. Our findings show that higher-performing students in science engaged in more text annotation; whereas lower-performing\r\nstudents relied more on highlighting and took longer time to respond multiple choice questions. Higher-performing students in social studies, by contrast, engaged more with the vocabulary and took longer to read before\r\nattempting question responses. Our findings may be used as recommendations to help both teachers and students engage in and support more effective behaviors.\r\n

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      cover image ACM Other conferences
      L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale
      August 2020
      442 pages
      ISBN:9781450379519
      DOI:10.1145/3386527
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      New York, NY, United States

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      Published: 12 August 2020

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      1. learner behavior analysis
      2. sequence mining

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