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Cognition Prediction Model for MOOCs Learners Based on ANN

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Intelligent Human Computer Interaction (IHCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13184))

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Abstract

Massive Open Online Courses (MOOCs) are becoming in- increasingly popular in recent years. In a virtual world, examining the cognition processes of the students is a real hassle. The primary goal of this study is to examine the influence of MOOCs on learning. This study presents a Cognitive Model based on brain signals for predicting the most effective MOOCs video lecture. In this work, students’ brain signals collected using an Electroencephalogram (EEG) device while watching MOOCs videos are used to classify their level of confusion using a publicly available dataset. The video that causes the least amount of confusion in the majority of students has been chosen as the best. This paper proposes and analyses the Cognitive Model for MOOCs Learning. A Deep Learning-based Artificial Neural Network Model has been created to predict student confusion levels. The methodology has been built using 10K fold cross-validation and shown to be 97% accurate in predicting students’ misunderstandings while watching MOOCs videos. The proposed Cognitive Model will aid in the evaluation of MOOCs course performance.

Varsha T. Lokare—Presently working as an Assistant Professor, Rajarambapu Institute of Technology, Sakharale, Affiliated to Shivaji University, Kolhapur.

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Authors and Affiliations

  1. Department of Computer Engineering, Dr Babasaheb Ambedkar Technological University, Lonere-Raigad, 402103, India

    Varsha T. Lokare, Laxman D. Netak & N. S. Jadhav

  2. Rajarambapu Institute of Technology, Sakharale, India

    Varsha T. Lokare

Authors
  1. Varsha T. Lokare
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  2. Laxman D. Netak
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  3. N. S. Jadhav
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Corresponding author

Correspondence to Varsha T. Lokare .

Editor information

Editors and Affiliations

  1. Kent State University, Kent, OH, USA

    Jong-Hoon Kim

  2. University of Tartu, Tartu, Estonia

    Madhusudan Singh

  3. Kent State University, Kent, OH, USA

    Javed Khan

  4. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  5. Massachusetts Institute of Technology, Cambridge, MA, USA

    Marigankar Sur

  6. Hankuk University of Foreign Studies, Seoul, Korea (Republic of)

    Dhananjay Singh

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Lokare, V.T., Netak, L.D., Jadhav, N.S. (2022). Cognition Prediction Model for MOOCs Learners Based on ANN. In: Kim, JH., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2021. Lecture Notes in Computer Science, vol 13184. Springer, Cham. https://doi.org/10.1007/978-3-030-98404-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-98404-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98403-8

  • Online ISBN: 978-3-030-98404-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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