Wednesday

Wednesday's Recap

Read the Summary of the Day >
 

Key Takeaways
 

Cognitive theory will make learning analytics systems more relevant and effective.

Initial work using cognitive theory and sophisticated statistics to frame learning analytics models has yielded promising results for Carnegie Mellon University. In evaluation studies, courses using the Learning Dashboard resulted in student learning gains of up to 18% that persisted months afterwards. These gains were achieved with approximately half the course contact hours of a conventional course.

Students and instructors both benefit from access to better information about the state of learning.

Today, most instructors rely on a combination of experience, instinct, and grade distributions to understand how their class is doing. Students may have nothing more than their grades to go on. Data hold the potential to provide much finer-grained insight for both groups. For students, details about their personal strengths and difficulties as learners, alerts to patterns in their learning, and personalized learning success strategies are all useful. Faculty can benefit from snapshots of class progress, details on where students are having trouble or sailing through the material, alerts to noteworthy patterns in class learning behavior, and recommendations for adapting of their teaching to the specific needs of the class.

Analytics is here is to stay. Higher education cannot ignore it.

Analytics yet is another way station in the evolution of information technology—think back to the microcomputer, the Internet, or cloud-based computing. Analytics is advancing rapidly, with nearly every sector using sophisticated analysis of “digital breadcrumbs” to inform decision making. Ignoring analytics will not shelter higher education from its impact. Lack of engagement may only result in analytics products ill-suited to the needs of colleges and universities; third parties will develop solutions with or without higher education’s input. Higher education’s engagement can ensure the best possible toolset supports students and faculty.
 

Wednesday's Activities

How does analytics affect faculty and students? Explore early warning systems, educational pathways, and use of student data to improve courses.
 

Analytics offers a way to monitor learner activity and progress as well as to predict learning outcomes. Learning analytics enables effective interventions and decision making for instructors and students alike. Analytics provides feedback to:
 

• faculty and course designers, allowing them to make targeted improvements to course material

• students, alerting them when their patterns indicate a risk of poor performance and helping them adjust their behaviors

• faculty and advisors, enabling them to identify potential problems and allowing them to intervene with specific types of assistance
   

2:00–3:30 p.m. ET (UTC-4); convert to your time zone
 

Analytics for Teaching, Learning, and Student Success

Archive of recorded webinar, presentation slides, and resources

Overview
Learning analytics holds multiple promises: to empower learners to understand and manage their academic progress and performance, to improve faculty visibility into the learner experience, and to better match instructional resources to learner characteristics. This session will take us “under the hood” of two successful but entirely different learning analytics initiatives. Professor Marsha Lovett will open by sharing Carnegie Mellon's groundbreaking analytics work and how it has been received by and influenced faculty. Next, Ellen Wagner from WCET will share lessons learned from a "big data" learning analytics initiative, the multistate Predictive Analytics Reporting Framework.

Featuring
Marsha Lovett, Director, Eberly Center for Teaching Excellence and Teaching Professor of Psychology, Carnegie Mellon University
Ellen Wagner, Executive Director, WICHE Cooperative for Educational Technologies (WCET)

Hosted by
Diana Oblinger, EDUCAUSE President and CEO

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