Implementation of a convolutional neural network method to predict university students’ emotional extent in online learning

  • Authors

    • Baharuddin . Universitas Negeri Medan
    • S Gultom Universitas Negeri Medan
    • M Restuati Universitas Negeri Medan
    • A Mansyur Universitas Negeri Medan
    • H Fibriasari Universitas Negeri Medan
    • I I Pane Universitas Negeri Medan
    • S Pujia Universitas Negeri Medan
    • V S G Galamgam Pangasinan State University
    • R P Hacla Pangasinan State University
    • M A Rais Universitas Negeri Medan
    • R Effendi Universitas Negeri Medan
    • M R Pane7 Universitas Negeri Medan
    2022-04-05
    https://doi.org/10.14419/ijet.v11i1.31981
  • Emotional Extent, Convolutional Neural Network, Learning, Face Detection.
  • A learning system that was directly face-to-face in class, had to be substituted with a learning system that is virtually integrated with the inter-net (online learning). The system uses online learning that connects teaching staff with the learning participants. Tiredness and laziness start-ed to be felt among the participants. This is worried to affect nonoptimal learning outcomes. During lessons, teaching staff often focuses on learning materials without knowing the emotions of their learning participants. Indirect online learning becomes a new challenge in adaptation; teaching staff and learning participants are required to adapt to existing conditions. With the problems above, the writer has an idea in devel-oping a prototype system that is developed to be able to help teachers in evaluating manners and emotions of students in online learning. Facial detection is a technology for detecting facial features such as the nose, eyes, and mouth. Development of facial detection that has un-dergone upgrades until now can differentiate if something is a facial feature or not and detect more than one face. This research aims to help teaching staff in evaluating learning participants’ affective behavior online by applying a Convolutional Neural Network method that has been trained to be able to predict emotional extents through facial expression.

     

     

  • References

    1. [1] Goldman A I and Sripada C S 2005 Simulationist models of face-based emotion recognition Cognition 94 193–213 https://doi.org/10.1016/j.cognition.2004.01.005.

      [2] Mano L Y et al. 2019 Using emotion recognition to assess simulation-based learning Nurse Educ. Pract. 36 13–19 https://doi.org/10.1016/j.nepr.2019.02.017.

      [3] Wang W, Xu K, Niu H and Miao X 2020 Emotion Recognition of Students Based on Facial Expressions in Online Education Based on the Perspective of Computer Simulation Complexity 2020 https://doi.org/10.1155/2020/4065207.

      [4] Engel D, Woolley A W, Jing L X, Chabris C F and Malone T W 2014 Reading the mind in the eyes or reading between the lines? Theory of mind predicts collective intelligence equally well online and face-to-face PLoS One 9 https://doi.org/10.1371/journal.pone.0115212.

      [5] Scherer K R 2005 What are emotions? and how can they be measured? Soc. Sci. Inf. 44 695–729 https://doi.org/10.1177/0539018405058216.

      [6] N. Mehendale, “Facial emotion recognition using convolutional neural networks (FERC),†SN Appl. Sci., vol. 2, no. 3, pp. 1–8, 2020, https://doi: 10.1007/s42452-020-2234-1.

      [7] D. Y. Liliana, “Emotion recognition from facial expression using deep convolutional neural network,†J. Phys. Conf. Ser., vol. 1193, no. 1, 2019, https://doi: 10.1088/1742-6596/1193/1/012004.

      [8] H. D. Hutahaean, S. Muhammad Aulia Rahman, and M. D. Mendoza, “Development of interactive learning media in computer network using augmented reality technology,†J. Phys. Conf. Ser., vol. 2193, no. 1, p. 012072, 2022, https://doi: 10.1088/1742-6596/2193/1/012072.

      [9] S. Razia, P. Swathi Prathyusha, N. Vamsi Krishna, and N. Sathya Sumana, “A Comparative study of machine learning algorithms on thyroid disease prediction,†Int. J. Eng. Technol., vol. 7, no. 2.8, pp. 315–319, 2018, https://doi: 10.14419/ijet.v7i2.8.10432.

      [10] S. Q. Ong, H. Ahmad, G. Nair, P. Isawasan, and A. H. A. Majid, “Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time,†Sci. Rep., vol. 11, no. 1, pp. 1–12, 2021, https://doi: 10.1038/s41598-021-89365-3.

      [11] H. Fibriasari, N. Harianja, D. Ampera, Z. Ramadhan, and Baharuddin, “E-Learning Learning Model to Improve the Quality of Student’s French Master,†Rev. Int. Geogr. Educ. Online, vol. 11, no. 5, pp. 3053–3063, 2021, https://doi: 10.48047/rigeo.11.05.197.

  • Downloads

  • How to Cite

    ., B., Gultom, S., Restuati, M., Mansyur, A., Fibriasari, H., I Pane, I., Pujia, S., S G Galamgam, V., P Hacla, R., A Rais, M., Effendi, R., & R Pane7, M. (2022). Implementation of a convolutional neural network method to predict university students’ emotional extent in online learning. International Journal of Engineering & Technology, 11(1), 59-62. https://doi.org/10.14419/ijet.v11i1.31981