FACT2 Learning Analytics Task Group Phase 2 report - CIT2014
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FACT2 Learning Analytics Task Group Phase 2 report - CIT2014
- 1. FACT2 LEARNING ANALYTICS Task Group Report SUNY Conference on Instructional Technologies May 2014
- 2. 2013-14 Task Group Goals • Will be to.. – develop the professional learning opportunities for SUNY faculty and staff – “identify and share known best practices and exemplary uses of Learning Analytics for assessment, and early intervention strategies.”
- 3. Task Group Activities Fall 2013 • Presented first year Task Group findings and best practices about Learning Analytics at several SUNY conferences. – “Using Big Data to Enhance the Student Experience” panel presentation at “Building a Smarter University: Big Data, Innovation and Ingenuity”, October 2013. – “Enhancing Excellence in Assessment: Institutional Effectiveness and Learning Analytics” presented at SUNY Council on Assessment (ScoA) at The College at Brockport, Stony Brook University, and the University at Albany. Spring 2014 • Presented best practices about Learning Analytics at through SUNY webinars. – “Learning Analytics: Best Practices for Student Assessment” – “Learning Analytics: Predicting Student Success in A Course”
- 4. Spring 2014 Pilot • SUNY Oswego has piloted Starfish retention system over one academic year – Nearly 1,000 students targeted in programs with known persistence issues (freshman and transfer students) – 750 courses, 360 instructors, 100 advisors involved • Impact being assessed; compared to previous performance of a similar cohort – scope of impact being measured (effect vs. effort to "scale up" and track against all students)
- 5. SUNY Institutional Level Use Course outcomes Intervention • Student Retention • Degree Completion • More…. • Advising • Placement • Learning outcomes • Degree Completion • Student Support • Persistence • Retention • Degree Completion • Learning outcomes • Student feedback • Instructional effectiveness
- 6. Learning Analytics - Working Definition • Software that collects and analyzes multiple data sets related to the process of learning to PREDICT and IMPACT student success. Learning Analytics Task Group of FACT2 Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014 Online course assignments Social media activities Student data Data potentially collected in… Blended and online learning environments, and other emergent resources connected to the teaching and learning experience.
- 7. Predictive Analytics: Building Models Placement for success and completion.. – which students should be steered toward which courses? Which programs? Can advising leverage student data? – If so, what are the best predictors of performance? Collect Data Data Analysis Actionable Results
- 8. Development of Large Scale Approaches Predictive Analytics Reporting (PAR) Framework and “Data Cookbook”
- 9. Predictive Analytics: Building Models Can we identify characteristics of a successful outcome? an unsuccessful outcome? DATA SOURCES Grade in course Can it be predicted by other data? • Major • High school GPA • English placement exam score • Math placement exam score • HS Regent scores…. • SAT Verbal, SAT math • SAT Writing“every student with a HS average of 83 or less, did not successfully complete the course…”
- 10. Learning Outcomes Teaching & Learning activity Teaching & Learning activity Student assignments Teaching & Learning activity Student assignments Grading Evaluate outcomes met for course? Typically, student assessment data is collected a the end of a course and data is used to report on outcomes. “Learning analytics is not in itself the goal but could provide a basis for decision making for effective action.” Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014
- 11. Prior assessment techniques… Focused on course outcomes, but no real-time data for interventions…. Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014
- 12. What happened? Why did it happen? What will happen? How can we improve learning? Descriptive Analytics Diagnostics Analytics Predictive Analytics Prescriptive Analytics 2014 State of the Art for Learning Analytics Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014
- 13. Learning Analytic Tools need to • Align with learning principles & pedagogy • Robust data analysis • Ethical considerations • Institutional capacity Is there a thoughtful educational plan for interventions and student feedback? Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014
- 14. RISKS & CONSIDERATIONS Ethics of Data Collection. Permissions? What is the educational plan for interventions and student feedback? Learning Analytics Webinar: Tools & Best Practices for Student Assessment, 4/14/2014
- 15. RECOMMENDATIONS Learning Analytics Task Group
- 16. LATG Recommendations • Develop a mechanism to encourage and support the adoption of Learning Analytics – For course placements, assessment, and degree completion – Through • professional education programs • enabling data access • Identify large Learning Analytics systems – for predictive analytics and intervention strategies – Expand & continue Pilots in process for system adoption, such as StarFish. – Identify, pilot and adopt learning analytics assessment tools for course use.
- 17. Recommendations: Specifically: • Establish an ongoing working group to develop Learning Analytic practices, tools and support services. • Develop best practices for campuses interested in adoption – resource allocation, effort involved, faculty development, organizational change management • Develop educational programs to encourage campus adoption
- 18. Recommendation: Data Practices • Establish common data definitions (e.g. definitions of "at risk" students) – leverage national data definition standards, such as the “Data Cookbook” developed by the Predictive Analytics Reporting framework. • Facilitate access to data and develop supporting policies for data access and privacy - ethical considerations. • Develop common indicators and measurements to assess impact across multiple campuses
- 19. QUESTIONS?
Hinweis der Redaktion
- Data potentially collected in… Blended and online learning environments, Online assignments, Online portals, Enrollment data, And other emergent resources connected to the teaching and learning experience.
- Identify the factors that directly and positively influence student success and momentum towards completing a high value postsecondary credential.2 The PAR Framework is predicated on identifying factors that affect success and loss among undergraduate students, with a focus on at-risk, first-time, new, and nontraditional students. While attention had initially been paid only to online students, the sample now includes records of students from on-the-ground, blended, and online programs. Viewing normalized data through a multi-institutional lens and using complete sets of undergraduate data based on a common set of measures with common data definitions provides insights that are not available when looking at records from a single institution. PAR Framework participation depends on having partner institutions (figure 1) provide a full set of de-identified undergraduate student data that allows for comparative investigation of student success trends over time, at the individual student, course, and degree level. PAR members provide incremental data updates at the end of each term/course enrollment period to measure changes over time, evaluate the impact of student success interventions, and enable the PAR predictive models to be adjusted and tuned for current data.
- Typically, student assessment data is collected a the end of a course and data is used to report on outcomes. LA can now enable interventions and feedback at the time students need it. To support personalizing the student experience; customize course materials. develop learning analytics that are learning-centric Can better support a cycle of predicting success, assessment and just in time feedback. (show diagram)
- Ultimately, analysing data to improve learning requires tools that align with pedagogical principles alongside sound data analysis skills, ethical considerations and institutional capacities that can ensure value-driven and valuable interventions.
- Data collection and the tools that measure it are not neutral and instead reflect what we consider to be ‘good’ education, pedagogy, and assessment practices, which are all fundamental issues at the heart of learning analytics development.