Kim Arnold
Director, Learning Analytics Center of Excellence,
University of Wisconsin-Madison
Learning Analytics and Student Success
Tuesday, February 10 | 10:15AM–11:00AM | Laguna, Fourth Floor
Session Type:
Professional Development
Differences among Institutional Faculty and Staff in Learning Analytics Readiness Levels
Steven Lonn, Assistant Director, USE Lab and Learning Analytics Specialist, University of Michigan-Ann Arbor
The Learning Analytics Readiness Instrument (LARI) was developed with the idea that institutions need in-depth information to aid in the implementation of learning analytics. During data collection for the beta version of the LARI, over 300 responses were received from 23 diverse institutions. This presentation will focus on the differences observed between various classifications of faculty and staff completing the LARI both within and across institutions when examining readiness for systemic learning analytics initiatives. Participants will be able to add their own insight to the findings, as well as discuss their own challenges with learning analytics readiness.
Steven Lonn, Assistant Director, USE Lab and Learning Analytics Specialist, University of Michigan-Ann Arbor
The Learning Analytics Readiness Instrument (LARI) was developed with the idea that institutions need in-depth information to aid in the implementation of learning analytics. During data collection for the beta version of the LARI, over 300 responses were received from 23 diverse institutions. This presentation will focus on the differences observed between various classifications of faculty and staff completing the LARI both within and across institutions when examining readiness for systemic learning analytics initiatives. Participants will be able to add their own insight to the findings, as well as discuss their own challenges with learning analytics readiness.
OUTCOMES: Understand the underpinnings of the LARI * Recognize the components constituting readiness for learning analytics * Learn about differences in responses from various respondents and institutions * Identify challenges and weaknesses to implementation at your own campus
Creating a Framework for an Institutional, Data-Driven Approach to Student Success
Elizabeth Mulherrin, Assistant Vice Provost for Student Success, University of Maryland University College
Analytic tools such as predictive models, early alert systems, and dashboards require an integrated approach to maximize institutional impact. Disparate initiatives and data sources within an institution can make it challenging to systematically evaluate and understand the impact of various efforts to improve student success. University of Maryland University College (UMUC) is creating an institutional approach to organizing, implementing, and evaluating student support services and interventions to improve outcomes. Learn how UMUC is tackling this challenge by developing a common data framework for identifying at-risk students, coordinating intervention efforts, and systematically evaluating initiatives.
OUTCOMES: Learn about various analytic tools and strategies for evaluating student success initiatives * Determine pathways for creating an integrated, data-driven approach to student success at your institution * Evaluate your institutional capacity to leverage analytic tools in a holistic way
