1. Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review
Sandy
Center for Educational and Instructional Technology Research (CEITR)
University of Phoenix School of Advanced Studies

2. Presented by: Dr. Mansureh Kebritchi, Research Director. mansureh
Presented by: Dr. Mansureh Kebritchi, Research Director Dr. Sandra G. Nunn, Research Fellow / Faculty
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3. Use of learning analytics (LA) in education
Introduction
Use of learning analytics (LA) in education
Key issue: How can we better identify methods, benefits, and challenges in LA for higher education?
This study reveals:
LA methods, benefits, and challenges through a systematic literature review
How these components affect educators and students in higher education
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4. Comprehensive literature review
Overview
Comprehensive literature review
Cooper (1988) method used to synthesize the literature
Formulate problem, collect data, evaluate data, analyze data, organize/present results
Focused on articles regarding: LA methods, benefits, and challenges
Revealed insights relative to how these three components affect educators and students in higher education
Michelle

5. Problem
Embracing LA in evaluating data in higher education diverts educators’ attention from identifying methods, benefits, and challenges
Few studies have synthesized prior studies or provided combined overview of LA in higher education
Need for further clarification of three components so educators can better apply LA in higher education.
Michelle

6. RQ2: What are the benefits of using learning analytics in education?
Research Questions
RQ1: What are the methods for conducting learning analytics in education?
RQ2: What are the benefits of using learning analytics in education?
RQ3: What are the challenges of using learning analytics in education?
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7. Purpose
LA uses predictive models that can help improve educational decision making / performance (Campbell, De Blois, & Oblinger, 2007)
Increases learner awareness to help improve the educational process (West, 2012)
Tracks and predicts learner performance and potential issues such as students at risk (EDUCAUSE, 2010; Johnson, Smith, Willis, Levine, & Haywood, 2011)
Michelle

8. Significance of Study
LA is an emerging field of education; therefore, stakeholders need to better understand LA methods and application in higher education (Scheffel, Drachsler, Stoyanov et al., 2014)
Providing an overview is critical to understanding LA and its applications in higher education
Michelle

9. Literature Review Method to Synthesize the Literature (Cooper, 1988)
Formulate the problem
Collect data
Evaluate the appropriateness of the data
Analyze and interpret relevant data
Organize and present the results
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10. Literature Review
Focused on peer-reviewed journals since 2000 to identify learning analytics methods, benefits, and challenges
Keywords included:
Learning analytics and methods
Learning analytics and benefits
Learning analytics and challenges
Data mining and education
Learning analytics and education
Learning analytics
Michelle

11. Results Methods Prediction Clustering Relationship mining
Discovery with models
Separation of data for use in the process of human judgment (Baker, 2010; Baker & Yacef, 2009; Romero & Ventura, 2010)
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12. Results Benefits Identification of target courses
Curriculum improvement
Student learning outcomes
Personalized learning
Improved instructor performance
Benefits to the research community
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13. Results Challenges Data tracking Data collection Data analysis
Learning environment optimization
Emerging technology
Ethical issues
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14. Implications
As a result of greater availability and access to data, LA will increase understanding of patterns of:
Learner behavior
Networks
Interactions
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15. Use of performance systems allows for:
Conclusion
Schools must recognize importance of a data-driven approach to education
Use of performance systems allows for:
Better decision-making
Identification of trends and problematic issues
Ability to allocate resources more efficiently
Using LA ensures greater outcomes to improve quality of teaching and learning
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16. References
Baker, R. (2010). Data mining for education. International Encylopedia of Education, 7, Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–16. Campbell, J. P., De Blois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. Educause Review, 42(4), Retrieved from EDUCAUSE website located at: Cooper, H. (1988). The structure of knowledge synthesis: A taxonomy of literature reviews. Knowledge in Society, 1, EDUCAUSE. (2010). Next generation learning challenges: Learner analytics premises. EDUCAUSE Publications. Retrieved from EDUCAUSE website located at: Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K., (2011). The horizon report: 2011 edition (Report). Retrieved from Romero, C., & Ventura, S. (2010). Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), Scheffel, M., Drachsler, H., Stoyanov S., & Specht, M. (2014). Quality indicators for learning analytics. Educational Technology & Society, 17(4), 117–132.
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17. References
West, D. M. (2012, September). Data mining, data analytics, and web dashboards. Governance Studies at Brookings, Retrieved from Brookings.edu website at:
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