Bio-Inspiring Learning Style Chatbot Inventory Using Brain Computing Interface to Increase the Efficiency of E-Learning

Abstract

In recent times Electronic Learning (E-Learning) and Massive Open Online Courses (MOOC) are more popular among the current generation of learners. Coursera, Edx, Simplilearn, Byjus and many other E-Learning service providers are available to deliver various courses. A recent study, in online courses, it has been found by Massachusetts Institute of Technology (MIT) that an astronomical dropout rate of about 96 per cent was found for the last five years. Educational researchers are attempting to decrease the dropout rate of E-Learning courses using various methods. Human Computer Interface (HCI) researchers are attempting to use Brain Computer Interface (BCI) to increase the efficiency of the E-Learning. Beta waves (14-30 Hz) are generated when the learners are more alert. Neil Fleming's VARK (Visual, Auditory, Read and Write and Kinesthetic) questionnaires are used by many researchers to classify the learners. Carl Jung explored that Introverts and Extraverts are the personality traits among the humans. Soomin Kim's study shows that for gathering of quantitative data, Chatbot may be a promising method. The proposed research work in this paper is to find out a correlation between Introvert and Extravert personality types and their learning styles. Initially, modified VARK questionnaires are implemented as a Chatbot to classify individuals as Introverts or Extraverts. After the classifications by the Chatbot, two minutes of visual and auditory contents are given to Introverts and Extraverts and learners' Beta brain waves are recorded and a dataset is created at an interval of one second. The dataset is validated using Machine Learning (ML) algorithms, like Naïve Bayes, N48 and Canopy. The proposed method is found to improve the accuracy of classification of learners. Bio-Inspired learning style Brain Computing Interface (BIL-BCI) framework proposed in this paper is a recommendation system to increase the accuracy of the classification among the E-Learners. INDEX TERMS E-Learning, massive open online course, VARK learning style, learning styles inventory(LSI), chatbot, classification, machine learning, introverts, extraverts, brain computer interface. I. INTRODUCTION Internet is expanding to a larger extent to reach all over the world. The average global internet speed is getting faster. Educational technologies [1] have become more viable with the advent of Internet. The higher education process has been dramatically changed by the use of latest techniques and tools. Modern technologies are increasing to have easy access for learning. Human's learning online can be changed by the E-Learning and MOOCs process. A large number of learners The associate editor coordinating the review of this manuscript and approving it for publication was Mohammad Zia Ur Rahman. can learn online at their own pace by using MOOC. Open course materials and peer feedbacks are used by the learners to interact with the courses. Around the globe, many universities are offering learners a chance to learn their relevant courses through MOOCs. E-...

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