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Learning Analytics Seminar Series

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Recorded Sessions

Stefan Slater

Graduate School of Education – University of Pennsylvania 

I Have Some Data, Now What? – A Guide To Selecting Research Tools For Learning Analytics

Publication    |    Direct Video Link    |    Presentation Slides

The diversity and complexity of learning analytics research is mirrored in the diversity and complexity of analysis and modeling tools available for use, but not all tools are created equal for a given research question. During this seminar, we’ll be highlighting the utility of particular software platforms and programming tools commonly used in the process of learning analytics, from data acquisition to presentation, and the strengths and weaknesses of these platforms for processing, engineering, and analyzing particular forms of data.

Bio:
Stefan Slater is a PhD student in the Teaching, Learning and Leadership program at the University of Pennsylvania, and a researcher at the Penn Center for Learning Analytics. He studies knowledge inference and natural language processing, and his recent work has involved the use of latent semantic analysis in the automated identification of mathematics concepts and skills in intelligent tutoring systems.

 

Dr. Charles Lang

Dr. Charles Lang
Teachers College – Columbia University

Bringing Design and Analytic Thinking Together

For over a century educational measurement has developed analytical tools designed to maximize the inferential power of limited samples: a biannual state test, a regular accreditation exam, a once in a lifetime SAT. But can this methodology adapt to a world in which previous limitations on data collection have been dramatically reduced? A world with a greater variety of data formats, representing a larger number of conditions, on a finer timescale, with a larger sample of students. Starting from a methodological basis, Charles will discuss the implications that changes in data collection may have on how education is measured and the impact that this might have on the disciplines, institutions, and practitioners that utilize educational measurement.

Bio:
Charles Lang is a Visiting Assistant Professor in Learning Analytics at Teachers College, Columbia. His research interests center on the use of big data in education and the role of online assessment data in accurately determining student learning. Specifically, Charles studies innovative methodologies for understanding student learning through predictive analytics, data mining, and graphical models. His doctoral thesis proposed a novel algorithm for predicting student performance within an electronic tutor based. Charles was previously a Postdoctoral Associate in Learning Analytics at Steinhardt School of Education, Culture & Human Development, NYU and received his Doctorate in Human Development and Education from the Harvard Graduate School of Education where he also taught Data Science in Education. He received his Bachelor of Science in Biochemistry and Bachelor of Arts in political science from the University of Melbourne.
 

Dr. Ryan Bakerunnamed

Graduate School of Education –  University of Pennsylvania

Oct 31, 2016 | 12:30PM - 1:30 PM | Tachers College | Rm: GD545

 

Learning Analytics in a Complex and Changing World

We’ve started to answer the questions of what we can model through EDM, and we’re getting better and better at modeling each year. We publish papers that present solid numbers
under reasonably stringent cross-validation, and we find that our models don’t just agree with training labels, but can predict future constructs as well. We’re making progress as a field in figuring out how to use these models to drive and support intervention, although there’s a whole lot more to learn.

But when and where can we trust our models? One of the greatest powers of EDM models is that we can use them outside the contexts in which they were originally developed, but how can we trust that we’re doing so wisely and safely? Theory from machine learning and statistics tell us about generalizability, and we know empirically that models developed with explicit attention to generalizability and construct validity are more likely to generalize and to be valid. But our conceptions and characterizations of population and context remain insufficient to fully answer the question of whether a model will be valid where will apply it. What’s worse, the
world is constantly changing; the model that works today may not work tomorrow, if the context changes in important ways, and we don’t know yet which changes matter.

In this talk, I will illustrate these issues by discussing our work to develop models that generalize across urban, rural, and suburban settings in the United States, and to study model generalizability internationally. I will discuss work from other groups that starts to think more
carefully about characterizing context and population in a concrete and precise fashion; where this
work is successful, and where it remains incomplete. By considering these issues more thoroughly, we can become increasingly confident in the applicability, validity, and usefulness of our models for broad and general use, a necessity for using EDM in a complex and changing world.

Bio:
Dr. Baker researches how students use and learn from educational games, intelligent tutors, and other kinds of educational software. Drawing on the fields of educational data mining, learning analytics, and human–computer interaction, he develops methods for mining the data that come out of the interactions between students and educational software. He then uses this information to improve our understanding of how students respond to educational software, and how these responses influence their learning.Prior to joining Penn GSE, Dr. Baker was an associate professor in the Department of Human Development at Teachers College, Columbia University. While at Teachers College, he taught the “Big Data and Education” MOOC twice, with total enrollment of more than 50,000 students.He has served as founding president of the International Educational Data Mining Society, where he currently serves on the board of directors. He has been co-author on nine award-winning papers. He serves as co-lead of the Big Data in Education spoke of the NSF Northeast Big Data Hub.