I am a Ph.D. student in the Department of Computer Science at North Carolina State University. I graduated from Furman University in Greenville, South Carolina with a B.S. in both Mathematics and Computer Science. Although I am originally from South Carolina, I also spent five years living in Clermont-Ferrand, France.

Throughout my time at Furman, I developed an interest in the broad field of artificial intelligence, which is my primary research interest. I enjoy exploring the many applications of machine learning, and I am also interested in its underlying mathematical theory. My current work focuses on machine learning applications in educational technologies, such as intelligent tutoring systems and game-based learning environments. I am particularly interested in multimodal learning analytics, which aims to utilize multimodal data streams (e.g., facial expressions, eye gaze, written text) to better model student learning. To do this, I have leveraged computational techniques including deep learning, natural language processing, and Bayesian statistics. In addition to my Ph.D. work, I spent the summer of 2020 interning with Applied Research Associates, working on computer vision applications (specifically, keypoint detection) with satellite imagery. Outside of academics and research, I enjoy spending time outdoors, watching and playing sports, reading, and exercising.

Education

Ph.D., Computer Science (currently enrolled)
North Carolina State University, Raleigh, NC

M.S., Computer Science (2019)
North Carolina State University, Raleigh, NC

B.S., Computer Science and Mathematics (2017)
Furman University, Greenville, SC

Honors

NCSU Dean’s Doctoral Fellowship (2017-2018)
Furman Bell Tower Scholar (2013-2017)
Furman Shucker Leadership Institute Fellow (2013-2015)
Member: Pi Mu Epsilon, Upsilon Pi Epsilon

Selected Publications

Andrew Emerson, Nathan Henderson, Jonathan Rowe, Wookhee Min, Seung Lee, James Minogue, and James Lester. Early Prediction of Visitor Engagement in Science Museums with Multimodal Learning Analytics. To appear in Proceedings of the Twenty-Second ACM International Conference on Multimodal Interaction, Utrecht, the Netherlands. [PDF]

Andrew Emerson, Michael Geden, Andy Smith, Eric Wiebe, Bradford Mott, Kristy Elizabeth Boyer, and James Lester. Predictive Student Modeling in Block-Based Programming Environments with Bayesian Hierarchical Models. Proceedings of the Twenty-Eighth ACM Conference on User Modeling, Adaptation and Personalization, pp. 62-70, Genoa, Italy, 2020. [PDF]

Dan Carpenter, Andrew Emerson, Bradford Mott, Asmalina Saleh, Krista Glazewski, Cindy Hmelo-Silver, and James Lester. Detecting Off-Task Behavior from Student Dialogue in Game-Based Collaborative Learning. Proceedings of the Twenty-First International Conference on Artificial Intelligence in Education, pp. 56-66, Ifrane, Morocco, 2020. [PDF]

Andrew Emerson, Elizabeth Cloude, Roger Azevedo, and James Lester. Multimodal Learning Analytics for Game-Based Learning. British Journal of Educational Technology, 51(5), 1505-1526, 2020. [PDF]

Andrew Emerson, Andy Smith, Fernando Rodríguez, Eric Wiebe, Bradford Mott, Kristy Elizabeth Boyer, and James Lester. Cluster-Based Analysis of Novice Coding Misconceptions in Block-Based Programming. Proceedings of the Fifty-First ACM Technical Symposium on Computer Science Education, pp. 825-831, Portland, 2020. [PDF]

Michael Geden, Andrew Emerson, Jonathan Rowe, Roger Azevedo, and James Lester. Predictive Student Modeling in Educational Games with Multi-Task Learning. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, pp. 654-661, New York, 2020. [PDF]

Nathan Henderson, Andrew Emerson, Jonathan Rowe, and James Lester. Improving Sensor-Based Affect Detection with Multimodal Data Imputation. Proceedings of the Eighth International Conference on Affective Computing and Intelligent Interaction, pp. 669-675, Cambridge, England, 2019. [PDF]

Andrew Emerson, Andy Smith, Cody Smith, Fernando Rodríguez, Wookhee Min, Eric Wiebe, Bradford Mott, Kristy Elizabeth Boyer, and James Lester. Predicting Early and Often: Predictive Student Modeling for Block-Based Programming Environments. Proceedings of the Twelfth International Conference on Educational Data Mining, pp. 39-48, Montreal, Canada, 2019. [PDF]

A more comprehensive list is available on Google Scholar.