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Big Data in the Online Classroom

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Online classrooms are de facto rich data gathering platforms. Educators can collect this data and use it to improve student outcomes through predictive analytics.

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Big Data in the Online Classroom

  1. 1. BIG DATA IN THE ONLINE CLASSROOM JAMES P. HOWARD, II FEBRUARY 13, 2016
  2. 2. OUTLINE • Big data has changed and continues to change almost every business • It has the potential to radically transform education and its processes to benefit students • In this presentation, we examine four questions at the heart of big data and online education: 1. What are the data that you could or should collect when you are teaching, either in the learning management system or performance measures? 2. How would you collect, manage, and utilize the data? 3. Are there any concerns or issues of these learning analytics? 4. Any other tips/recommendations when using the analytics?
  3. 3. PERFORMANCE MEASURES WHAT ARE THE DATA THAT YOU COULD OR SHOULD COLLECT WHEN YOU ARE TEACHING, EITHER IN THE LEARNING MANAGEMENT SYSTEM OR PERFORMANCE MEASURES?
  4. 4. STUDENT INTERACTION DATA • How often the student logs in, even for passive reading and review • How often the student interacts with the course materials • What types of interactions the student has • When the student interacts (day, time) CollegeDegrees360 / Flickr / CC-BY Baepler & Murdoch (2010)
  5. 5. GRADE DATA • Student grades by assignment delivery mechanism • Homework, timed quizzes, proctored exams • Discussion and interactive • Student grade trends, going up or down • Student grades in other program courses • Especially prerequisites Mathieu Plourde / Flickr / CC-BY Arnold (2010)
  6. 6. TEXT ANALYTICS • Text analysis can be used to study a student writing • Used in cheating detection • Used to automatically grade the SAT’s essay • Text analysis looks at the words used and sentence structure to • Classify text by topic area • Identify the sentiment or “feeling” of the text Nick Ares / Flickr / CC-BY Hara, Bonk, & Angeli (2000)
  7. 7. SOCIAL NETWORK ANALYSIS • Social network analysis studies the interactions among individuals in a group • Can be applied to student interactions • Can show how information flows around the classroom Christiane Birr / Flickr / CC-BY D’Andrea, Ferri, & Grifoni (2010)
  8. 8. DATA REQUIREMENTS HOW WOULD YOU COLLECT, MANAGE, AND UTILIZE THE DATA?
  9. 9. BETTER GUIDANCE AND COUNSELING • Student data from prior students and courses can help with course selection • Students can be placed into the right level of English or mathematics depending on proficiency • Students will achieve more based on correct placements Lenarc / Wikimedia Commons / CC-BY Amey & Long 1998
  10. 10. EARLY INTERVENTION • Early intervention can come from grade data • Students who are underperforming early can be treated quickly to improve retention and success • Early treatment can be targeted what students need most Eastern Mennonite University / Flickr / CC-BY Amey & Long (1998)
  11. 11. AUTOMATED GRADING • Advanced text analysis can be used for automated grading • Hand-scoring can be needed frequently • Automated grading can increase the student/faculty ratio • Cuts costs to the institution Mixabest / Wikimedia Commons / BY-CC Hara, Bonk, & Angeli (2000)
  12. 12. POTENTIAL PITFALLS ARE THERE ANY CONCERNS OR ISSUES OF THESE LEARNING ANALYTICS?
  13. 13. EQUITY Michael Coghlan / Flickr / CC-BY • Some students may receive intervention while others do not • Students who need intervention may not be successfully identified • Students who do not qualify for intervention may receive insufficient support if instructors are spending too much time with those who do
  14. 14. DEPERSONALIZATION Keir Mucklestone-Barnett / Flickr / CC-BY • Instruct runs risk of losing connection to individual students • Scorecard-approach reduces student learning to box-checking • Automated grading increases this disconnect • Students run risk of losing connection to instructor
  15. 15. PRIVACY Mike Mozart / Flickr / CC-BY • Student data, once collected, may be shared internally • Student data may be leaked or stolen • Data security cannot be guaranteed • Students may be put off by intervention attempts
  16. 16. ADDITIONAL THOUGHTS ANY OTHER TIPS/RECOMMENDATIONS WHEN USING THE ANALYTICS?
  17. 17. ADDITIONAL THOUGHTS AND SUMMARY • Big data has a place in online education • The place is still poorly defined by lack of access, training, and tools • Better integration will lead the way • There are risks from adopting too much data reliance for both students and institutions
  18. 18. REFERENCES Amey, M. J., & Long, P. N. (1998). Developmental course work and early placement: Success strategies for underprepared community college students. Community College Journal of Research and Practice, 22(1), 3-10. Arnold, K. E. (2010). Signals: Applying Academic Analytics. Educause Quarterly, 33(1), n1. Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). ACM. Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 17. D’Andrea, A., Ferri, F., & Grifoni, P. (2010). An overview of methods for virtual social networks analysis In Abraham, Ajith et al. Computational Social Network Analysis: Trends, Tools and Research Advances, (pp. 3-25). Springer London. Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28(2), 115-152. Timetrax23 / Flickr / BY-CC

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