Adam Levick, Market Research and Analytics Analyst
Following a great turnout at last year’s Learning Analytics and Knowledge (LAK) conference, U-M will return this spring with 2 full papers, 2 workshops, and 2 doctoral consortium participants. As Learning Analytics grows as a key tool in the future of education, LAK is creating a community of experts to share and create knowledge. Michigan has been a pioneer in the field of learning analytics; DEI uses learning analytics as a key part of personalization at scale, the redesign of the residential education experience, and experimentation with digital learning. Our Academic Innovation at Michigan series organizes AIM Analytics where a community of scholars interested in the application of learning analytics can hear from leaders in the field and workshop together to develop their own projects.
Team members from LED and DEI are involved in all of U-M’s accepted LAK submissions and we are excited to share our knowledge and gain insights to apply in our work at DEI. Explore the list of the research areas U-M will illuminate at the conference, and learn about our attendees to the doctoral consortium. The conference takes place in Edinburgh from April 25th to 29th, 2016:
What and When: The Role of Course Type and Timing in Students’ Academic Performance
Michael G. Brown, R. Matthew DeMonbrun, Steven Lonn, Stephen J. Aguilar, Stephanie D. Teasley
This paper uses classification data from the Student Explorer early warning system and other factors to examine student, organizational, and disciplinary factors that can predict improvement and decline in academic performance over time. Findings from this study have major implications for the future design of early warning systems as well as academic course planning in higher education.
The Learning Analytics Readiness Instrument
Meghan Oster, Steven Lonn, Matthew D. Pistilli, Michael G. Brown
This paper describes the results from the beta version of the Learning Analytics Readiness Instrument, a survey developed to help institutions of higher education discern their readiness to implement learning analytics. The analysis details differences noted between roles of individuals as well as institutional types. Findings from this research situate institutional readiness for learning analytics as a reflective process within a framework of organizational learning, and how related implementation and application needs to consider ethical implications.
Introduction to Data Mining for Educational Researchers
Christopher Brooks*, Craig Thompson and Vitomir Kovanovi
* = LED Lab Members
“The goal of this tutorial is to share data mining tools and techniques used by computer scientists with educational social scientists. We broadly define educational social scientists as being made up of people with backgrounds in the learning sciences, cognitive psychology, and educational research. The learning analytics community is heavily populated with researchers of these backgrounds, and we believe those that find themselves at the intersection of research, theory, and practice have a particular interest in expanding their knowledge of data-driven tools and techniques.”
Learning Analytics for Curriculum and Program Quality Improvement
Jim Greer, Marco Molinaro, Xavier Ochoa and Timothy McKay*
* = DEI Partner
“Much of the research in LAK to date has been “student facing”, that is, using data to better understand learners and their need or to create interventions that directly support or influence learners. This workshop takes the perspective on how Learning Analytics can drive improvements in teaching practices, instructional and curricular design, and academic program delivery.”
This year, two of the ten participants in the doctoral consortium are PhD candidate members of the LED Lab. Michael Brown and Caitlin Holman have been selected to take part in the doctoral consortium where they will receive feedback and insight from an interdisciplinary community of scholars on their dissertations. Michael (School of Education) will share pilot data from his dissertation on how students form learning communities in large lecture hall courses using social network learning analytics (SNLA). Caitlin (School of Information) will share her work analyzing data gathered from courses using the GradeCraft platform to understand how students take multiple pathways through gameful courses. Stephanie Teasley is a faculty lead for the doctoral consortium.
Follow the conversation with @umichdei in April