Universities and Colleges are increasingly moving to data-driven models for decision making. Changes in the environment put pressure on us to better understand, model, simulate, and predict our institutions. “Big data” offers potential for powering our understanding and decision-making, both administrative and academic, if we can master the tools and mindsets required. The impediment is not the technology, but culture and economics.
Potential Impact of “Big Data” on Students:
The focus on learning outcomes rather than credit hours, the need to reach learners who have disrupted their degree progress, the flex-time and just-in-time offering of courses and programs, and the personalization of learning provide big data opportunities to affect academic progress and outcomes.
Identifying potential students:
Vendors already use big data to analyze pools of potential students, target marketing and recruitment efforts, and shape financial aid offerings. There is huge potential for institutions to analyze the patterns and individuals who interrupt or terminate their degree progress and to provide precisely targeted programs to help those students understand how to return, including alternate degree pathways and financing. At the University of Memphis we are currently focusing intensively on former undergraduates who left in good academic standing with 90+ credit hours, working to bring them back to the university and to degree completion.
Thoughts on Big Data and Higher Education
Universities and Colleges are increasingly moving to data-driven models for decision making. Changes in the environment put pressure on us to better understand, model, simulate, and predict our institutions.
“Big data” offers potential for powering our understanding and decision-making, both administrative and academic, if we can master the tools and mindsets required. The impediment is not the technology, but culture and economics.
Potential Impact of “Big Data” on Students:
The focus on learning outcomes rather than credit hours, the need to reach learners who have disrupted their degree progress, the flex-time and just-in-time offering of courses and programs, and the personalization of learning provide big data opportunities to affect academic progress and outcomes.
Identifying potential students:
Vendors already use big data to analyze pools of potential students, target marketing and recruitment efforts, and shape financial aid offerings. There is huge potential for institutions to analyze the patterns and individuals who interrupt or terminate their degree progress and to provide precisely targeted programs to help those students understand how to return, including alternate degree pathways and financing. At the University of Memphis we are currently focusing intensively on former undergraduates who left in good academic standing with 90+ credit hours, working to bring them back to the university and to degree completion.
Personalized learning and degree pathways:
Many traditional undergraduates initially declare majors in which they are only marginally successful. Big data tools can help us to analyze past classroom performance, predict future academic success, match attained credits with university and major requirements, and recommend both individual courses and alternate majors in which students will have a higher likelihood of success, while minimizing additional courses required when switching majors. Analytics can help us to determine how to modularize or flex course offerings, when to offer courses, and target those students most likely to enroll and succeed in such courses and programs, up to and including degree pathways.
Teaching and learning analytics:
Textbook and learning management vendors are incorporating analytics to provide immediate feedback to students and faculty regarding comprehension, acquisition of learning outcomes, and then customize content to one’s personal learning style. Most of these providers expect access to individual student data on core administrative systems to undergrid their analytics. While our students – and many faculty don’t seem concerned about this access, I think we need to be concerned for them, both in terms of immediate privacy, and in terms of the use to which the data gathered and generated may be put in the future.
We have terabytes – sometimes petabytes – of data about our institutions, students and faculty, but we use limited components of that data in limited ways. Adopting the tools and techniques of big data analytics has the promise of helping us provide answers to questions we are just beginning to learn to ask.
Potential Impact of “Big Data” on Students:
The focus on learning outcomes rather than credit hours, the need to reach learners who have disrupted their degree progress, the flex-time and just-in-time offering of courses and programs, and the personalization of learning provide big data opportunities to affect academic progress and outcomes.
Identifying potential students:
Vendors already use big data to analyze pools of potential students, target marketing and recruitment efforts, and shape financial aid offerings. There is huge potential for institutions to analyze the patterns and individuals who interrupt or terminate their degree progress and to provide precisely targeted programs to help those students understand how to return, including alternate degree pathways and financing. At the University of Memphis we are currently focusing intensively on former undergraduates who left in good academic standing with 90+ credit hours, working to bring them back to the university and to degree completion.
Personalized learning and degree pathways:
Many traditional undergraduates initially declare majors in which they are only marginally successful. Big data tools can help us to analyze past classroom performance, predict future academic success, match attained credits with university and major requirements, and recommend both individual courses and alternate majors in which students will have a higher likelihood of success, while minimizing additional courses required when switching majors. Analytics can help us to determine how to modularize or flex course offerings, when to offer courses, and target those students most likely to enroll and succeed in such courses and programs, up to and including degree pathways.
Teaching and learning analytics:
Textbook and learning management vendors are incorporating analytics to provide immediate feedback to students and faculty regarding comprehension, acquisition of learning outcomes, and then customize content to one’s personal learning style. Most of these providers expect access to individual student data on core administrative systems to undergrid their analytics. While our students – and many faculty don’t seem concerned about this access, I think we need to be concerned for them, both in terms of immediate privacy, and in terms of the use to which the data gathered and generated may be put in the future.
We have terabytes – sometimes petabytes – of data about our institutions, students and faculty, but we use limited components of that data in limited ways. Adopting the tools and techniques of big data analytics has the promise of helping us provide answers to questions we are just beginning to learn to ask.
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