Expert feature-engineering vs. Deep neural networks: Which is better for sensor-free affect detection?

Yang Jiang, Nigel Bosch, Ryan S. Baker, Luc Paquette, Jaclyn Ocumpaugh, Juliana Ma Alexandra L. Andres, Allison L. Moore, Gautam Biswas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The past few years have seen a surge of interest in deep neural networks. The wide application of deep learning in other domains such as image classification has driven considerable recent interest and efforts in applying these methods in educational domains. However, there is still limited research comparing the predictive power of the deep learning approach with the traditional feature engineering approach for common student modeling problems such as sensor-free affect detection. This paper aims to address this gap by presenting a thorough comparison of several deep neural network approaches with a traditional feature engineering approach in the context of affect and behavior modeling. We built detectors of student affective states and behaviors as middle school students learned science in an open-ended learning environment called Betty’s Brain, using both approaches. Overall, we observed a tradeoff where the feature engineering models were better when considering a single optimized threshold (for intervention), whereas the deep learning models were better when taking model confidence fully into account (for discovery with models analyses).

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part I
EditorsCarolyn Penstein Rosé, Roberto Martínez-Maldonado, H Ulrich Hoppe, Rose Luckin, Manolis Mavrikis, Kaska Porayska-Pomsta, Bruce McLaren, Benedict du Boulay
PublisherSpringer-Verlag Berlin Heidelberg
Pages198-211
Number of pages14
ISBN (Print)9783319938424
DOIs
StatePublished - 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: Jun 27 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science
Volume10947

Other

Other19th International Conference on Artificial Intelligence in Education, AIED 2018
CountryUnited Kingdom
CityLondon
Period6/27/186/30/18

Keywords

  • Affect and behavior detection
  • Betty’s brain
  • Deep learning
  • Deep neural networks
  • Feature engineering
  • Student modeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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