Online Learning

Study on bias in learning analytics earns Brooks Best Full Research Paper Award at LAK conference

By | General Interest, Happenings, News, Research

A paper co-authored by University of Michigan School of Information research assistant professor Christopher Brooks received the Best Full Research Paper Award at the International Conference on Learning Analytics & Knowledge (LAK) Conference in Tempe, Arizona. The award was announced on the final day of the conference, March 7, 2019.

The paper, “Evaluating the Fairness of Predictive Student Models Through Slicing Analysis,” describes a tool designed to test the bias in algorithms used to predict student success.

The goal of the paper, Brooks says, was to evaluate whether the algorithms used to predict whether students would succeed in massive online courses (MOOCs) was skewed by the gender makeup of the classes.

“We were able to find that some have more bias than others do,” says Brooks. “First we were able to show that different MOOCs tend to have different bias in gender representation inside of the MOOCs.”

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AIM Analytics Seminar – Dan Davis, PhD Candidate, TU Delft, the Netherlands.

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Improving Online Learning Outcomes Using Large-Scale Learning Analytics

Abstract: This talk will cover a holistic approach to improving learning outcomes and behavior in large-scale learning environments—namely MOOCs. I begin by sharing the results of a study exploring the extent to which learners follow (or deviate from) the designed learning path and the impact this behavior has on eventual learning outcomes. We next take a deeper dive into the design of online courses with a large-scale learning design approach, where I’ll present an automated method developed to categorize courses based on their design. With these trends in learning & teaching behavior in mind, the talk will conclude with the results of a series of randomized experiments (A/B tests) carried out in live MOOCs designed to provide additional support to learners. From these experiments we arrive at a better understanding of which instructional & design strategies are most effective for improving learning outcomes and behavior at scale.

Bio: Dan’s research uses and advances learning analytics techniques in open, online education at scale by pushing the boundaries towards personalized & adaptive learning environments. Dan develops methods to gain a deeper understanding about how the design of online learning environments affects learner success and engagement, often by implementing and testing instructional interventions at scale using randomized controlled experiments. Dan earned his BA in English, Writing & Mass Communication with a minor in Graphic Design from Assumption College in Worcester, Mass. His MA is from Georgetown University in Communication, Culture & Technology, and he is currently finishing his PhD in Computer Science, Learning Analytics from TU Delft in the Netherlands.

Lunch will be provided.

AIM Analytics is a bi-weekly seminar series for researchers across U-M who are interested in learning analytics. The field of learning analytics is a multi and interdisciplinary field that brings together researchers from education, learning sciences, computational sciences and statistics, and all discipline-specific forms of educational inquiry.