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Innovation using Predictive Analytics

Innovation using Predictive Analytics. C hallenges for modern higher education Predictive analytics Questions raised Other means. The parameters within which we educate. Emphasis on retention rates Emphasis on graduation rates Access for students Costs to students

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Innovation using Predictive Analytics

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  1. Innovation using Predictive Analytics • Challenges for modern higher education • Predictive analytics • Questions raised • Other means

  2. The parameters within which we educate • Emphasis on retention rates • Emphasis on graduation rates • Access for students • Costs to students • Assessment of performance and career outcomes (salaries) • Incoming resources to the university • External dimensions: Online education and MOOCs • LDQCs (Locally Delivered Quality Courses)

  3. The educational challenges • Overcoming the framing of questions by definable data measures • Measuring retention and graduation qualitatively • Performance dimensions from incoming clients and how these have changed • Do data include a longitudinal change in populations? • Contributors to cost not directly related to instruction • Reporting requirements, assessment, financial aid issues • The cost/benefit conundrum • Who does it cost? Who benefits? • A specter of homogeneous outcomes • Federal to national?

  4. Civitas Learning, and PARProject: Insight from Big Data Translate complex data into real-time, personalized recommendations to inform decisions that lead to success

  5. Descriptive Analytics Predictive Analytics Differences in level of insight • How many logins, page views, and other metrics have occurred over time? • What were the course completion rates for a particular program over time? What were the attributes of the students who didn’t successfully complete? • Which tools are being used in courses the most? • Which students are exhibiting behaviors early in the semester which put them at risk for dropping or failing a course? • What is the predicted course completion rate for a particular program? Which students are currently at risk for not completing and why? • Which tools and content in the course are directly correlated to student success?

  6. Predictive analytics • The promise: To provide data-based predictive capabilities for a student’s future performance, producing potentially valuable tools to advisers, faculty, and students. • Students are advised to take courses in which they are likely to be more successful, and be more likely to graduate • Advisers will be able to coach students who are less likely to be successful (potentially raising retention and graduation rates) • Administrators and faculty could find patterns of performance, perhaps identifying choke points in degree programs or toxic combinations of courses • Course order • Course combinations • Other factors, such as transfer populations, etc.

  7. Any Trouble in Paradise? Potential problems, assuming it all works as advertised and goes beyond the current human element of advising • Massive data movement with outside vendors • Use of data for purposes not originally intended and associated ethical dimensions • What may work to graduate in a major may not work for a career goal • No grist for the mill • Large scale, quickly implemented experimentation

  8. Predictions TheCoolingWorld Newsweek, April 28, 1975 • “A major climatic change would force economic and social adjustments on a worldwide scale,” warns a recent report by the National Academy of Sciences, “because the global patterns of food production and population that have evolved are implicitly dependent on the climate of the present...” • Climatologists are pessimistic that political leaders will take any positive action to compensate for the climatic change, or even to allay its effects. They concede that some of the more spectacular solutions proposed…might create problems far greater than those they solve. But the scientists see few signs that government leaders anywhere are even prepared to take…simple measures…The longer the planners delay, the more difficult will they find it to cope with climatic change once the results become grim reality…

  9. Other considerations • Technology around Campus • Labs, Library, Living, Lounges • Academic Integrity • The online environment • Vapidity • Traditional means • Medical education, football, extended campuses • Undergraduate research

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