Organizers:

Collin Lynch is an Associate Professor in the Department of Computer Science at North Carolina State University. His primary research is focused on developing robust ITS and adaptive educational systems for Ill-Defined domains such as scientific writing, law, and software development. His current research includes work on argument mining and natural language processing, real-time support for classroom orchestration and writing to learn tasks, advances in student modeling, the development of embodied cognitive agents for collaborative learning, and scaffolding for CS education.

Paul Deane is a principal research scientist in the Research & Development division at ETS. He is the author of Grammar in Mind and Brain, a study of the interaction of cognitive structures in syntax and semantics, and the second author of Vocabulary Assessment to Support Instruction. His current research interests include formative assessment design in the English language arts, cognitive models of writing skills, automated essay scoring, and vocabulary assessment. During his career at ETS, he has worked on a variety of natural language processing (NLP) and assessment projects, including automated item generation, tools to support verbal test development, scoring of collocation errors, reading and vocabulary assessment, and automated essay scoring.

Piotr Mitros is a Senior Research Scientist at ETS. He is also the original author of the popular Open edX learning platform and the original founder as well as the Chief Scientist for more than five years. He has spent the past few years exploring issues around why educational initiatives go south, and evidence-based practices aren’t adopted and converged on issues around governance, transparency, and incentive structures. His current work focuses on how we develop educational measurements that incentivize and support rich classroom instruction supporting diverse (rather than standardized) students.

Zhikai Gao is a senior Ph.D. student at North Carolina State University. His current research focuses on understanding students’ learning behaviors through traceable log data from ITS, CS education, help-seeking behavior, and LLM usage in education across disciplines.

Damilola Babalola is a second-year Computer Science Ph.D. student at North Carolina State University with a research focus on using Artificial Intelligence (Educational Data Mining and Natural Language Processing) to improve Education. His current work involves research, software development, data mining, and data visualizations aimed at assisting middle-school and high-school students in improving their essay-writing skills. The core of his research centers around the extraction and classification of student essay revisions based on their edit intention, followed by the visualization of student clusters exhibiting similar revision patterns.

Program Committee:

TBA