Western Washington Data-Driven Discovery Seminar Series

Western Washington Data-Driven Discovery Seminar Series

Use of Machine Learning to Analyze Chemistry Card Sort Tasks, Logan Sizemore (Western Washington University)

Education  researchers  are  deeply  interested in  understanding  the  way  students  organize their  knowledge,  and card sort  tasks,  which  require  students  to  group concepts,  are one mechanism to infer a student’s organizational  strategy.  However, the limited  resolution of card sort tasks means  they necessarily miss some  of  the nuance  in  a  student’s  strategy. In  this  work, we  propose  new machine learning strategies that leverage a potentially richer source of student thinking:  free-form written  language  justifications  of  sorts. Using  data  from  a  university  chemistry card  sort  task,  we  use vectorized representations of language and unsupervised learning techniques to generate qualitatively interpretable clusters, which can provide unique insight in how students organize their knowledge. 
 
Fine-Grained Classroom Activity Detection, Eric Slyman (Pacific Northwest National Laboratory)
 
Instructors are increasingly incorporating student-centered learning techniques in their classrooms to improve learning outcomes. In addition to lecture, these class sessions involve forms of individual and group work, and greater rates of student-instructor interaction. Quantifying classroom activity is a key element of accelerating the evaluation and refinement of innovative teaching practices, but manual annotation does not scale. In this work, we present advances to the young application area of automatic classroom activity detection from audio. Using a university classroom corpus with nine activity labels (e.g., "lecture,'' "group work,'' "student question''), we propose and evaluate deep fully connected, convolutional, and recurrent neural network architectures, comparing the performance of mel-filterbank, prosodic, and self-supervised acoustic features. We compare 9-way classification performance with 5-way and 4-way simplifications of the task and assess two types of generalization: (1) new class sessions from previously seen instructors and (2) previously unseen instructors. We obtain strong results on the new fine-grained task and state-of-the-art on the 4-way task: our best model obtains frame-level error rates of 6.2%, 7.7% and 28.0% when generalizing to unseen instructors for the 4-way, 5-way and 9-way classification tasks, respectively (relative reductions of 35.4%, 48.3% and 21.6% over a strong baseline) and examine the effects of ensembling and decoding its outputs. When estimating the aggregate time spent on classroom activities, our average root mean squared error is 1.64 minutes, a 54.9% relative reduction over the baseline.

Meeting Information:

Zoom Link: https://wwu-edu.zoom.us/j/97284053416?pwd=V3NJc3NsOUlMaUJMb1pBRkFXQlo4QT09

Meeting ID: 972 8405 3416
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