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Estimating the Concentration of Students from Time Series Images

6 pagesPublished: March 9, 2020


In this study, we build a system that is able to estimate the concentration degree of students while they are working with computers. The purpose of learning is to gain knowledge of a subject and to reach sufficient performance level about the subject. Concentration is the key in the successful learning process. But the concept of concentration includes some ambiguity and lacks the clear definition form an engineering point of view, and it is difficult to measure its degree by observation from outside. We in this paper begins with a discussion of the concept of concentration, and then a discussion of how to measure it by using standard devices and sensors. The proposed system investigates the facial images of students recorded by the PC webcams attached to the computers to infer their concentration degree. In this study, we define the concentration degree over a short time interval. The value takes continues value from 0 to 1, and is determined based on the efficiency of simple work performed over the interval. We convert the continuous values into three discrete values: low, middle and high. In the first approach in this study, we apply deep learning algorithm with only the facial images. In the next, we obtain the data of face moves as a set of time series, and run the learning algorithm using both of the data. We explain an outline of the methods and the system with several experimental results.

Keyphrases: CNN, Concentration degree, facial detection, Facial Landmark Detection, machine learning

In: Gordon Lee and Ying Jin (editors). Proceedings of 35th International Conference on Computers and Their Applications, vol 69, pages 224--229

BibTeX entry
  author    = {Hoang Son Nguyen and Yu Takahata and Masaaki Goto and Tetsuo Tanaka and Akihiko Ohsuga and Kazunori Matsumoto},
  title     = {Estimating the Concentration of Students from Time Series Images},
  booktitle = {Proceedings of 35th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {69},
  pages     = {224--229},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/gl3b}}
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