Big Data and Student Retention
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The document discusses using analytics to improve student retention and other outcomes at the University of Kentucky. It describes: 1) Initial steps taken over the past year, including mobile surveys of students, analyzing enrollment, performance, and other data, and using high-speed analytics. 2) Potential future areas of work, such as personalized mobile interactions with students, advanced models of student behavior and performance, improved lecture content discovery and recommendations, and degree planning tools. 3) Various data models already in use, including those tracking enrollment, student demographics and performance, classroom and instructor productivity, and student surveys and interactions.
Big Data and Student Retention
- 1. Using Fast Analytics to Help Improve Student Retention (and more) Vince Kellen, Ph.D. Senior Vice Provost Academic Planning, Analytics and Technologies University of Kentucky vkellen@uky.edu October, 2013
- 2. What we have done and what we would like to do First steps over the past year • Mobile micro-surveys: Learning from the learner • Student enrollment, retention, demographics, performance, K-Score, facilities utilization, instructor workload and more • High speed, in-memory analytics architectural differences • Open data and organizational considerations Coming down the road? • Micro-segmentation tool to enhance user and IT productivity, develop personalized mobile student interaction/intervention • Models for learner technographics, psychographics, in addition to behaviors, performance, background • Advanced way-finding for streaming content like lecture capture • Content metadata extraction and learner knowledge discovery • Real-time measures of concept engagement and mastery • Real-time learner recommendations and support engine • Use graphing algorithms to perform more sophisticated degree audit what ifs 2
- 3. Model Description Enrollment Enrollment in a class, midterm and final grades, credit hours attempted and earned, instructor teaching the class Student retention and graduation Student demographics and cohort identification (e.g., John Doe is in the 2009 entering first-year student cohort) Student demographics Demographics, such as age, high school GPA, entrance test scores (SAT, ACT) and subcomponent scores. Also, in a secure location, additional personally identifiable demographic details such as name, address, email, etc. Student performance Present the enrollment data in such a way as to easily show the student’s performance for each term, including credit hours earned, term GPA, cumulative GPA for that term, etc. Student academic career Keep a list of the majors and minors for each student and degrees awarded. Also, include details on students who transfer in and out, including transfer institution, credit hours transferred in, etc. Productivity The room utilization model contains every building, every potential classroom and lets users analyze the room capacity and enrollments for the class or event in the room at five minute intervals. The faculty stats per term model pulls together the number of students and sections taught per term and will contain other important data such as research expenditures per term and grant proposals submitted and won. Micro-surveys Capture questions and answers from the My UK Mobile micro-survey feature Student involvement Interaction history with various applications including the learning management system, clickers, course capture and playback, academic alerts. Provide the basis for calculating the student’s K-Score. 3
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- 8. Academic Health Notifications: View in student mobile app
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- 13. WAKE UP! GET TO CLASS! Who sets alarms for themselves? Why not automatically set alarms for students around their schedule? Why not have automated wake-up calls? Why not suggest wake up times based on class attendance? Why not consider manipulation of reminders as a form of engagement? Can we ascertain student prospective memory capability and personalize based on it? 13
- 15. Taxonomy? Automatic metadata? Automatic atomic metadata? Let learners navigate an audio/visual stream Let the system learn what are top terms. Let the system map terms to concepts. Let instructional designers lightly ‘bump’ the taxonomy, post production Record student engagement with specific terms / concepts See http://p.uky.edu Deliver personalized messages to students 15
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- 18. Class slides take a central position. The audio/video and slide content is synchronized.
- 19. The note pad allows recording and sharing of notes.
- 20. Personalized recommendations are a guide thru the material. Feedback on engagement and mastery assist in gauging understanding. Resources and tutors are also provided if a little assistance is needed.
- 21. A lecture concept map helps to put the lecture in a visual context. The map is generated from analysis of the text and ‘bumped’ into shape by a course designer or instructor. Concepts can be rated to collect perception of usefulness and improve future versions. Jump to the media segment by clicking on the tag. Test knowledge with small quizzes – ace the quizzes and you’re in good shape!
- 22. A one-stop-shop for searching. Keywords from the video, slides, trends, notes and conversations will appear.
- 23. Key questions • Can the audio and slides be reliably transcribed into ‘useful’ text? • Can a concept map be derived automatically from the text generated or easily edited by an instructor? • How easy will it be for designers-instructors to create a quiz and place it in the right location in the video? • Can we personalize the recommendations to reflect prior knowledge, student ability and individual differences in information processing? • Can the interface support real-time integration with analytic backends (e.g., HANA)? This is just one conceptualization. What other interface designs exist today? How effective are they?
- 25. Organizational considerations Integrated IR and BI teams. Added three Ph.D. level data scientists. Had some turnover. Translated all old architectures to HANA and retiring old IR data warehouse. Making everything ‘automatic’ like IPEDS cohort determinations, enrollment reporting. Moving as much as possible to self-service. Agnostic on the front end tools. Opened data. Many have access. Personal data is protected. Established a data community of practice. Used an ‘always beta’ mode of development. Measuring data quality. Providing data quality process improvements to the teams. Started to raise skill sets in colleges and units. Provide support. Encourage ad-hoc learning opportunities. Help units with job descriptions and selection of talent. 25
- 26. Be safe and secure. Respect the acceptable use of information policies and guidelines the university has in place. Please have good passwords and secure your laptop, desktop and other devices appropriately. Treat private student and UK information appropriately Be collegial. University data is a community asset and a community of people steward the data. Use and share the data with the best interests of the university community in mind. Since parts of our data analysis environment is designed to allow for greater transparency, analysis will potentially be able to see other unit data. While we will make private to a unit what absolutely needs to be private, the way the university runs it's business often involves multiple colleges and units at the same time. Don't use your access to take unfair advantage of another unit. Help improve data quality. If you see data that doesn't appear to be correct, let someone know. We have a team of staff dedicated to helping improve data quality. This team can work with colleges and units on any data entry and data management processes that might need to be changed to improve data quality. Be open-minded and inquisitive. Data can be represented in multiple ways at the same time. While the teams are taking great care to enable multiple views of the data to support the community, you might have a valid and unique perspective. In time, we can accommodate more ways of looking at the same data while not interfering with other views or taxonomies. Share. The main benefit from open analytics is the power of a community of analysts learning from each other rather than a few select individuals hoarding knowledge or access. As the community improves its knowledge and skill with the data, the university can improve accordingly.
- 27. Personalize learning and support in one architecture • Real-time personalized interactions • • • • • • Target on-demand peer tutoring based on student’s profile Deliver micro-surveys and assessments to capture additional information needed to improve personalization Give students academic health indicators that tell students where they can improve in study, engagement, support, etc. Let students opt their parents in to this information so the family can support the student Tailor and target reminder services, avoid over messaging, enable timing of message delivery based on user temporal proclivities Allow for open personalized learning • • • • How content gets matched to students is psychologically complex Several theories of how humans learn give many insights Students differ in the following abilities and attributes: visual-object, visualspatial, reasoning, cognitive reflection, need for sensation, need for cognition, various verbal abilities, confidence, persistence, prospective memory, etc. We need an open architecture to promote rapid experimentation, testing and sharing of what works and what doesn’t University of Kentucky
- 28. How technology can help in student success High effectiveness MOOC + PT + F2F Small F2F class MOOC + PT Current MOOC approach Broadcast class Low effectiveness F2F = Face-to-face PT = Personalization technology, adaptive learning technology Low volume High volume 28
- 29. Questions? 29