Open AccessBook
Learning Analytics: From Research to Practice
Johann Ari Larusson,Brandon White +1 more
- 06 Jul 2014
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TL;DR: Learning Analytics: From Research to Practice as mentioned in this paper provides a broad overview of the latest in theories, findings, strategies, and tools from across education and technological disciplines to evaluate the efficacy of educational practices in their own right.
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Abstract: 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.
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Citations
Big Data and analytics in higher education: Opportunities and challenges
TL;DR: The paper outlines a number of opportunities and challenges associated with the implementation of Big Data in the context of higher education and outlines future directions relating to the development and implementation of an institutional project on Big Data.
590
Identifying significant indicators using LMS data to predict course achievement in online learning
TL;DR: The results demonstrated that students' regular study, late submissions of assignments, number of sessions, and proof of reading the course information packets significantly predicted their course achievement.
379
Designing pedagogical interventions to support student use of learning analytics
Alyssa Friend Wise
- 24 Mar 2014
TL;DR: Four principles of pedagogical learning analytics intervention design that can be used by teachers and course developers to support the productive use of learning analytics by students are introduced and three core processes in which to engage students are described.
Use of the ARCS model in education: A literature review
Kun Li,John M. Keller +1 more
TL;DR: The findings indicate that the ARCS model was applied to a variety of countries and educational settings and four major research outcomes were found in regard to participants' affective domain, cognitive domain, learning behaviors, and psychological traits.
243
Educational Data Mining and Learning Analytics: differences, similarities, and time evolution
TL;DR: This paper aims to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data.
References
The State of Educational Data Mining in 2009: A Review and Future Visions
Ryan S. Baker,Kalina Yacef +1 more
- 01 Oct 2009
TL;DR: This paper reviewed the history and current trends in the field of EDM and discussed trends and shifts in the research conducted by this community, and discussed the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries, and the reduction in the frequency of relationship mining within the EDM community.
Data mining in education
TL;DR: Key milestones and the current state of affairs in the field of EDM are reviewed, together with specific applications, tools, and future insights.
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Learning analytics and educational data mining: towards communication and collaboration
George Siemens,Ryan S. Baker +1 more
- 29 Apr 2012
TL;DR: This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
988
Educational data sciences: framing emergent practices for analytics of learning, organizations, and systems
Philip J. Piety,Daniel T. Hickey,M. J. Bishop +2 more
- 24 Mar 2014
TL;DR: A conceptual framework for organizing emerging analytic activities involving educational data that can fall under broad and often loosely defined categories, including Academic/Institutional Analytics, Learning Analytics/Educational Data Mining, Learner Analytics/Personalization, and Systemic Instructional Improvement is developed.
Educational data scientists: a scarce breed
Simon Buckingham Shum,Martin Hawksey,Ryan S. Baker,Naomi Jeffery,John T. Behrens,Roy Pea +5 more
- 08 Apr 2013
TL;DR: This panel will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem.
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