In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also beginning to affect how we teach and learn in this increasingly interconnected and information-rich world. The panelists will discuss their ongoing work that seeks to understand the affordances and potential roles of social media in learning, as well as to determine and provide methods that can help researchers and educators evaluate the use of social media for teaching and learning based on automated analyses of social media texts and networks. The panel will focus on the first phase of this five-year research initiative "Learning Analytics for the Social Media Age" funded by the Social Science and Humanites Research Council of Canada (2013--2018).

In support of our research projects in information retrieval, we have developed an integrated multi-process software system that shepherds research data from induction through aggregation, analysis, and presentation. We combine public-domain code libraries with our own software to provide a flexible, easily- configured modular system that exposes data online for easier collaboration. The goal is to create a single online infrastructure that allows colleagues to submit, process, analyze and visualize data, and discuss and prioritize issues through a single integrated interface. We demonstrate our system within the context of the large data set provided by the Indexer's Legacy project [1].

Our Meta-dex software suite extracts content and index text from a corpus of PDF files, and generates a meta-index that references entries across an entire domain. We provide tools to analyze the individual and integrated indexes, and visualize entries and books within the meta-index. The suite is scalable to very large data sets.

As research includes more and larger user studies, a significant problem lies in combining the many types of data files into a single table suitable for analysis by common statistical tools.

Much of human activity defines an information context. We awaken, start work, and hold meetings at roughly the same time every day, and retrieve the same information items (day planners, itineraries, schedules, agendas, reports, menus, web pages, etc.) for many of these activities. Information retrieval systems in general lack sensitivity to such recurrent context, requiring users to remember and re-enter search cues for objects regardless of how regularly or consistently the objects are used, and to develop ad-hoc storage strategies.