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Board # 146 : Early Predicting of Student Struggles Using Body Language

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

NSF Grantees Poster Session

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NSF Grantees Poster Session

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Paper Authors


Matthew L Dering Penn State University

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Matthew Dering is a PhD student at Penn State University studying computer vision and deep learning.

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Conrad Tucker Pennsylvania State University, University Park

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Dr. Tucker holds a joint appointment as Assistant Professor in Engineering Design and Industrial Engineering at The Pennsylvania State University. He is also affiliate faculty in Computer Science and Engineering. He teaches Introduction to Engineering Design (EDSGN 100) at the undergraduate level and developed and taught a graduate-level course titled Data Mining–Driven Design (EDSGN 561). As part of the Engineering Design Program’s “Summers by Design” (SBD) program, Dr. Tucker supervises students from Penn State during the summer semester in a two-week engineering design program at the École Centrale de Nantes in Nantes, France.

Dr. Tucker is the director of the Design Analysis Technology Advancement (D.A.T.A) Laboratory. His research interests are in formalizing system design processes under the paradigm of knowledge discovery, optimization, data mining, and informatics. His research interests include applications in complex systems design and operation, product portfolio/family design, and sustainable system design optimization in the areas of engineering education, energy generation systems, consumer electronics, environment, and national security.

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The accuracy of RGB-D sensing has enabled many technical achievements in applications such as gamification, task recognition, as well as pedagogical applications. The ability of these sensors to track many body parts simultaneously has introduced a new data modality for analysis. By analyzing body language, this work can predict if a student will struggle in the future, and if an instructor should intervene. To accomplish this, a study is performed to determine how early (after how many seconds) does it become possible to determine if a student will struggle. A simple neural network is proposed which is used to jointly classify body language and predict task performance. By modeling the input as both instances and sequences, a peak F Score of 0.459 was obtained, after observing a student for just two seconds. Finally, an unsupervised method yielded a model which could determine if a student would struggle after just 1 second with 59.9% accuracy.

Dering, M. L., & Tucker, C. (2017, June), Board # 146 : Early Predicting of Student Struggles Using Body Language Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27764

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