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Is Self-Regulated Learning Instruction Predictive of Self-Regulated Learning Skills, Math Achievement and Learning Motivation in a Web-Based Learning Environment?
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, Arizona State University, United States

Society for Information Technology & Teacher Education International Conference, in Jacksonville, Florida, United States ISBN 978-1-939797-07-0 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA

Abstract

This evaluative study used predictive analyses to determine the effectiveness of using self-regulated learning instruction to improve a web-based remedial, high school Algebra course designed to remediate students that have failed the course previously. The study used multiple regression and found that predictive models based on independent variables aligned to student demographics, mastery learning skills, and ARCS course design factors are illustrative in defining how to design remediation courses as well as improve evaluations of these courses to more fully scaffold student use of self-regulated learning principles and practices that assist teachers with individualizing student instruction by assisting students in knowing how to be more responsible for their own learning processes.

Citation

Barrus, D.A. (2014). Is Self-Regulated Learning Instruction Predictive of Self-Regulated Learning Skills, Math Achievement and Learning Motivation in a Web-Based Learning Environment?. In M. Searson & M. Ochoa (Eds.), Proceedings of SITE 2014--Society for Information Technology & Teacher Education International Conference (pp. 1172-1180). Jacksonville, Florida, United States: Association for the Advancement of Computing in Education (AACE). Retrieved November 15, 2019 from .

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