Dr Una-May O’Reilly from Massachusetts Institute of Technology describes how she and her research group build accurate predictive models of MOOC (Massive Open Online Courses) student stopout via a scalable, prediction methodology, end to end, from raw source data to model analysis.
The seminar is free and open to all. Seating is on a first-come, first-served basis.
Understanding why students stop out will help in understanding how students learn in MOOCs (Massive Open Online Courses). In this seminar the speaker will describe how she and her research group build accurate predictive models of MOOC student stopout via a scalable, prediction methodology, end to end, from raw source data to model analysis. The research group attempted to predict stop-out for the Fall 2012 offering of MIT’s 6.002x.
This involved the meticulous and crowd-sourced engineering of over 25 predictive features extracted for thousands of students, the creation of temporal and non-temporal data representations for use in predictive modeling, the derivation of over 10 thousand models with a variety of state-of-the-art machine learning techniques and the analysis of feature importance by examining over 70000 models. The research group found that stop-out prediction is a tractable problem. Their models achieved an AUC (receiver operating characteristic area-under-the-curve) as high as 0.95 (and generally 0.88) when predicting one week in advance. Even with more difficult prediction problems, such as predicting stop-out at the end of the course with only one weeks' data, the models attained AUCs of ~0.7.
About the speaker
Dr Una-May O’Reilly obtained her PhD in 1995 from Carleton University. She joined the Computer Science and Artificial Intelligence Laboratory (CASIL) at Massachusetts Institute of Technology as a Postdoctoral Associate in 1996 and is currently the Principal Research Scientist of CASIL. She also leads the AnyScale Learning For All (ALFA) group at CASIL which focuses on scalable machine learning and frameworks for knowledge mining, prediction, analytics and optimization.
Dr O’Reilly was a co-founder of the Association for Computing Machinery's Special Interest Group on Genetic and Evolutionary Computation (ACM SigEVO) in 2004 and now serves as the Vice-Chair. In 2013, she inaugurated the Women in Evolutionary Computation group at Genetic and Evolutionary Computation Conference (GECCO). She was awarded the EvoStar Award for Outstanding Contribution of Evolutionary Computation in Europe in the same year.
Dr O’Reilly is the area editor for Data Analytics and Knowledge Discovery for Genetic Programming and Evolvable Machines (Kluwer), and editor for Evolutionary Computation (MIT Press), and action editor for the Journal of Machine Learning Research.
The seminar is free and open to all. Seating is on a first-come, first-served basis.
