7:30am – 9:00am Registration and Coffee

9:00am – 9:10am Welcome and Opening Remarks
Speaker: David Demers, CIO, SUNY Buffalo State

9:10am – 10:00am Overview of Learning Analytics; Results from the Marist Open Academic Analytics Initiative (OAAI)
Speaker: Sandeep Jayaprakash, Lead Data Scientist, Marist College

Predictive Learning Analytics is changing the landscape of education. Data gleaned from learning systems act as "power sources" for predictive analytics, enabling institutions to identify student learning patterns and define success measures. Analytics is also helping educators to respond and act to improve these outcomes by intervening in a timely fashion. The presentation will provide a broad overview of the current state of Learning Analytics, emerging trends and their implications. Opening access to Learning Analytics and taking a platform based approach is seen as a strategic imperative by many in the field. This presentation will provide an overview of efforts to develop the world's first Open Learning Analytics platform and the strategic value these systems and their affiliated predictive models provide.

10:00am - 10:10am Break

10:10am – 11:00am Learner Dashboards to Inform Instructional Approaches
Speaker: Peter Shea, Director, Office of Professional Development, Middlesex Community College

This session will focus on a key institutional issue faced by many higher education professionals: how do you successfully engage faculty and staff to consider the value of analytics related to their roles? Peter Shea from Middlesex Community College will discuss this challenge in the context of the initiatives underway on his campus. Elements for discussion will include “dashboards for everyone?” as a method of achieving institutional buy-in and the use of simulations and digital edu-games as a means of collecting critical student performance data previously unobtainable to inform instructional approaches and to accurately measure learning outcomes.

11:00am – 11:10am Break

11:10am - 12:00pm Adaptive Learning and the Power of Analytics
Speaker: Colm Howlin, Principal Researcher, Realizeit

For adaptive learning to be effective it must be data driven. This rich set of behavioral and attainment data has the potential to provide students and educators with previously unobtainable insights into the learning experience. Key requirements of these analytics are that they must be both understandable and actionable. This session will begin by briefly exploring the learning analytics available to both students and instructors as a result of using the Realizeit adaptive learning system. Following this, the session will discuss predictive analytics, and how key metrics can enable construction of predictive models which can function as an early warning system to detect at-risk students based on their learning behavior.

12:00pm – 1:00pm Lunch

1:00pm – 1:50pm Data Mining to Inform Student Advising
Speaker: Amanda Gould, Chief of Operational Effectiveness & Student Success, Bay Path University

Bay Path University strives to place students at the center of the learning experience. Among our key strategies is an analytics-based, predictive case management system characterized by automated alerts, pre-determined risk criteria, and established intervention guidelines which inform our advising model. The primary goal of this strategy is to prompt student outreach in closest proximity to a precipitating risk event, in an effort to impact student outcomes. To better understand learners’ whole needs, Bay Path captures key elements of the student experience throughout students’ educational journeys. Data captured by the various systems is consolidated within Bay Path’s Data Warehouse, thereby comprising a learner profile. Toward the goal of converting analytics to insights, this session will describe a framework for using risk markers to generate interventions in real-time.

1:50pm – 2:00pm Break

2:00pm – 2:45pm Panel Discussion: Ethics of Learning Analytics
Panelists:
David Demers, CIO - SUNY Buffalo State
Amanda Gould, Chief of Operational Effectiveness & Student Success, Bay Path University
Colm Howlin, Principal Researcher, Realizeit
Sandeep Jayaprakash, Lead Data Scientist – Marist College
Peter Shea, Director, Office of Professional Development, Middlesex Community College

Often overlooked when considering the implementation of a learning analytics platform are the ethical ramifications associated with collecting detailed transactional and demographic information on students engaged in learning activities. This panel discussion will provide an opportunity to hear from experts in the field on the use of student-generated data to customize or tailor the delivery of instruction and academic support services and the ethics of providing a differential experience for learners at different levels of preparation and skill. The panel will debate critical issues such as:
• What are the responsibilities of "knowing" a predictive analytic?
• Once something is known about a student, what are the ethical ramifications of action and/or inaction?
• Who owns this information?
• Who decides whether and when to turn predictions into actionable interventions?
• What is the role of student autonomy?
• What is the role of information confidentiality?

2:45pm – 3:00pm Recap and Closing Remarks

3:00pm End