Learners' efficiency prediction using facial behavior analysis

Abstract

In the e-learning context, how much the learner is concentrated and engaged, or the learners' efficiency, is essential for providing adaptive and flexible materials, timely suggestions, etc., which can lead to efficient learning. In this work, we explore to predict learners' efficiency with a realistic configuration, in which we use a webcam or a laptop PC’s built-in camera. Specifically, we first provide a feasible definition of the learners' efficiency, and based on this definition, we predict one’s efficiency from facial behavior. We predict the learners' efficiency using various convolutional neural networks. Results are discussed using different evaluation metrics.

Publication
Proc.~International Conference on Image Processing (ICIP)
Manisha Verma
Manisha Verma
Specially-Appointed Researcher/Fellow

Manisha’s research interest broadly lies in computer vision and image processing. Currently, she is working on micro facial expression recognition using multi-model deep learning frameworks.

Yuta Nakashima
Yuta Nakashima
Associate Professor

Yuta Nakashima is an associate professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.

Noriko Takemura
Noriko Takemura
Guest Associate Professor

She is working on ambient intelligence and gait recognition using pattern recognition and machine learning.

Hajime Nagahara
Hajime Nagahara
Professor

He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.

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