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Meetings Stub Page [mx-stub]

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

9:00am - 10:15am   Introduction to Analytics: Today...Tomorrow
Dr. Mamta Saxena, Director of Assessment at the College of Professional Studies in the Academic Quality Assurance unit, Northeastern University
Melanie Kasparian, Associate Director of Assessment at the College of Professional Studies, Northeastern University
Peter Hess, Learning Technologies Administrator, Boston College

To set the stage for our discussion of where learning analytics are now and where they’re headed, we'll begin with a simple model that takes account of available data sources, techniques we can use to see patterns and make inferences from the data we collect, how that information might be communicated to people who can use it, and what actions they might take as a result.  We'll consider some complications to our simple model, in terms of its internal logic, the ethical issues it might raise, and institutional realities.   Representatives from Northeastern University, who attended the 9th annual International Learning Analytics and Knowledge (LAK) Conference in Arizona, will share perspectives from that event.  Together, we’ll consider the overarching question: what are the possible benefits (and to whom) and what are the possible negative effects (and for whom) of harvesting, selecting, and analyzing learning data?

10:15am - 10:30am   Break

10:30am - 11:00am    Institutional Connections: Collaborations and Barriers
Speaker: Bryan Blakeley, Director of Operations and Learning Design of the Center for Teaching Excellence, Boston College

 This presentation will discuss institutional relationships and barriers involved with getting a learning analytics effort up and running. We’ll consider how many groups at the university are involved, the politics of data, and how difficult getting developer time truly is, not to mention the (completely necessary) security reviews, external vendor relationships and contracts, and concerns over the "black box" algorithms baked into most commercially-available analytics software.

11:00am - 11:30am Open Discussions

11:30am-12:15pm  Data Analytics & Tools: Examples from the LMS
Speakers:  Peter Hess, 
Learning Technologies Administrator, Boston College
Lauren Hankin,  Associate Director of Technical Services, Northeastern University

In this session, we’ll take a look at implementations of learning analytics  bundled in two widely used platforms, Canvas and Blackboard.  With speakers from Boston College and Northeastern U., both of whom are in the early stages of investigation the tools provided in those LMSes, will  take a look at Canvas's "Analytics BETA" and Blackboard Learn's "A4L"  as examples of platforms that provide both learning-related data and basic tools for analyzing it.  The goal is not to provide a tutorial, but to arrive at a general sense of what teachers, students, and other users of those platforms can look forward to in terms of getting actionable information from them.

12:15pm-1:00pm  Lunch

1:00pm - 2:00pm   How Large Institutions Can Use Large Data Sets and Tools

Agasthya Shenoy, Researcher for Vice Provost/Learning Research, Harvard University
Zachary Wang, Manager for Resources Adoption and Impact, Harvard’s Initiative for Learning and Teaching 

In this session, we will discuss how large institutions can utilize the rapidly growing set of tools from the field of data science to improve the entire teaching and learning lifecycle. Using tools developed at Harvard as examples, we will look at where such tools can make an impact for faculty, administrators, and students. Given the massive amount of data universities now have access to with respect to teaching and learning, we will also consider what guiding principles will foster innovative and responsible use of such data. 

2:00m-2:45pm   Visualizing Data with Tableau

Speakers:  Jamie Oh, Enterprise Account Manager, Tableau
Michael Tashakkori, Enterprise Sales Manager, Education, Tabeleau
Jeremy Weatherall, Senior Solutions Architect

This presentation will center on the Tableau platform showcasing some very common use cases with our education customers. The focus is to show different ways a Student Admissions dataset can be analyzed, ending up with a dynamic dashboard that enables users to interact and filter the data.

2:45pm-3:30pm  Data analytics with Learning Data at MIT
Speaker: Anindya Roy, Learning Engineer OpenMIT Open Learning

MIT runs hundreds of courses on the edX platform, as MOOCs as well as for the students on campus. As the learners attempt problems, watch videos, and participate in educational activities on the platform, their activity logs tell us about the time-on-task behavior and engagement aspect of individual course components (e.g., do students attempt this problem?). Such basic analytics are more powerful when combined with a strategic course design process in place (e.g., do students attempt this problem more when it’s placed between highly watched video segments?). In this presentation, I will talk about how we handle data (collection, storage, processing), and how we are collaborating with the instructors to use the learning data for improved course design and better pedagogy.


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