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Consolidates current Learning Analytics research into one volume
Includes numerous examples of Learning Analytics implementations
Provides strategies for integrating Learning Analytics at the classroom, departmental, and institutional level
In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics.
Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world.
Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to:
Enhance student and faculty performance.
Improve student understanding of course material.
Assess and attend to the needs of struggling learners.
Improve accuracy in grading.
Allow instructors to assess and develop their own strengths.
Encourage more efficient use of resources at the institutional level.
Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success.
Content Level »Research
Keywords »Data Mining and Education - Knowledge Assessment in Education - LA and Education - Learning Analytics and Education - Learning Analytics in CSCL - Predicitve Modeling and Education
Introduction.- Designing Learning Analytics Experiences.- Harnessing the Currents of the Digital Ocean.- Educational Data Mining and Learning Analytics.- Analytics through an Institutional Lens: Definition, Theory, Design and Impact.- A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering.- The Data-Assisted Approach to Building Intelligent Technology Enhanced Learning Environments.- Identifying Points for Pedagogical Intervention Based on Student Writing: Two Case Studies for the "Point of Originality."