Schedule

Location

The colloquium will be held on the Tenth Floor of Weiser Hall

Map to the Weiser Hall

Talk abstracts

Group Communication Analysis:

A Method for Modeling Socio-Cognitive Identity in a Digital Age

Nia Dowell

School of Information & Academic Innovation, University of Michigan

Abstract: Educational environments have become increasingly reliant on computer-mediated communication, relying on video conferencing, synchronous chats, and asynchronous forums, in both small (5-20 learners) and massive (1000+ learner) learning environments. These platforms, which are designed to support or even supplant traditional instruction, have become commonplace across all levels of education, and as a result created big data in education. In order to move forward, the learning sciences field is in need of new automated approaches that offer deeper insights into the dynamics of learner interaction and discourse across online learning platforms. This talk will present results from recent work that uses language and discourse to capture social and cognitive dynamics during collaborative interactions. I will introduce group communication analysis (GCA), a novel approach for detecting emergent learner roles from the participants’ contributions and patterns of interaction. This method makes use of automated computational linguistic analysis of the sequential interactions of participants in online group communication to create distinct interaction profiles. We have applied the GCA to several collaborative learning datasets. Cluster analysis, predictive, and hierarchical linear mixed-effects modeling were used to assess the validity of the GCA approach, and practical influence of learner roles on student and overall group performance. The results indicate that learners’ patterns in linguistic coordination and cohesion are representative of the roles that individuals play in collaborative discussions. More broadly, GCA provides an interdisciplinary framework for researchers to explore the micro intra- and inter-personal patterns associated with the participants’ roles and the sociocognitive processes related to successful collaboration.

"The neural dynamics of naturalistic language comprehension"

Jonathan R. Brennan

Abstract: The hierarchical syntax of human language sets it apart from other communicative and cognitive systems, yet there is significant debate about the role that this syntax plays in how the brain speaks and understands language in real-time. A chief question has been how to define a rigorous link between the properties of syntax on the one hand, and of neural systems on the other. This project seeks to bridge this gap using insights from the psychology of language and computational neural networks. We define a family of computational models that approximate aspects of what people do as they comprehend the syntax of natural language. We compare these models against human electroencephalography signals recorded while participants simply listen to an audiobook story. This comparison shows a striking match between the dynamic changes in ongoing human neural activity, and word-by-word changes in the state of the computational model as it navigates the hierarchical structure of language. This match offers insight into the nature of the cognitive representations in play as humans comprehend every-day speech.

Understanding Language Learning

Nick Ellis