A machine learning approach to identify and track learning styles in MOOCs

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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“…Naïve Bayes [62] is an easy and fast algorithmic tool to predict classes of test data set. It also performs well in multi class predictions.…”

Section: A a Classification Using Naïve Bayes Classifiermentioning

confidence: 99%

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

2020

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“…Or, actuellement, il n'y a dans la très grande majorité des MOOC qu'un seul parcours proposé aux apprenants, ce parcours ne convenant pas forcément à tous (Qazdar et al, 2016). Afin de répondre à cette question, beaucoup voient la personnalisation de l'apprentissage (Assami et al, 2018 ;Bejaoui, 2017 ;El Mawas et al, 2018 ;Hajri, 2018), notamment en s'appuyant sur des profils d'apprenants (Clerc et al, 2015 ;Sunar et al, 2015) ou des styles d'apprentissages (Geigle & Zhai, 2017 ;Grünewald et al, 2013 ;Hmedna et al, 2016). D'autres travaux tels que Gutiérrez-Rojas et al (2014) présentent un algorithme de planification pour suggérer dynamiquement le temps efficace d'apprentissage des participants, compte tenu de leurs profils, leurs préférences et leurs priorités.…”

Section: Questions Liées à La Personnalisation Et à L'adaptationunclassified

“…Un autre volet que nous abordons dans ce contexte, qui n'est pas des moindres, concerne l'analyse des données massive engendrée par les MOOC. En effet, ce domaine connaît un essor important impliquant des méthodes de Big Data et Learning analytics, ayant diverses finalités (Demetriadis et al, 2018 ;Ezen-Can et al, 2015 ;Hmedna et al, 2016 ;Li & Zhou, 2018 ;Vieira et al, 2018 ;Wang et al, 2018 ;Witthaus et al, 2016 ;Wong et al, 2018) : adaptation de l'apprentissage, propositions de systèmes de recommandation, analyse des pratiques des apprenants, classifications des apprenants, voire même, le développement de nouvelles stratégies pour l'actualisation des MOOC. L'une des utilités les plus notables de l'analyse des traces massives est la mesure des taux d'abandon.…”

Section: Analyse Des Traces Massives D'apprentissageunclassified

A Model and Tool to Support Pedagogical Scenario Building for Connectivist MOOC

et al. 2020

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“…Previous studies have found four different dimensions of learning styles: (1) processing (active/reflective), (2) perception (sensory/intuitive), (3) input (visual/verbal) and (4) understand (sequential/global) [13], [19], [15]. Rohaniyah [20] stated the major differences in learning styles are the (1) the way a people perceive (sensation versus intuition), (2) the way they made a decision (logical thinking versus imaginative feelings) and (3) how active or reflective for interacting (extroversion versus introversion).…”

Section: A Learning Stylesmentioning

confidence: 99%

“…The previous researcher stated that the stronger the visual style of learning was, the more time the learners spent on pictorial content [24]. Most researchers mentioned that visual learners outperformed verbal learners such as focused on pictorial learning objects and learn best from what they see [15] and [24]. Visual learners almost prefer video, demonstrations, pictures and graphs [19].…”

Section: A Research Question 1: What Are the Learning Styles Of Learmentioning

confidence: 99%

The Designing of Adaptive Self-Assessment Activities in Second Language Learning using Massive Open Online Courses (MOOCs)

Hashim¹,

Salam²,

Nurul³

et al. 2018

ijacsa

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Massive Open Online Courses (MOOCs) provides an effective learning platform with various high-quality educational materials accessible to learners from all over the world. In this paper, the types of learner characteristics in MOOCs second language learning are discussed. However, there are still problems and challenges including assessment. A quantitative research method approach has been utilized in this study. Results of the study are then used for implementing suitable adaptive self-assessment activities in MOOCs learning. Findings of this study are two folds: (1) The dimension of learner characteristics (learning styles and cognitive style) for improving student performance in MOOCs learning and (2) suitable selfassessment activities that consider learners requirement or adaptive to learner characteristics for improving MOOCs learning performance. Based on the findings, the data indicate that visual, active, thinking and intuitive learner is the proposed dimension used in this study. In this study, our aim is to propose adaptive self-assessment activities for improving MOOCs learning in the second language course. In the future study, students will be investigated about their engagement using MOOC assessment in the second language.

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A machine learning approach to identify and track learning styles in MOOCs

Cited by 21 publications

References 7 publications

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

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

A Model and Tool to Support Pedagogical Scenario Building for Connectivist MOOC

The Designing of Adaptive Self-Assessment Activities in Second Language Learning using Massive Open Online Courses (MOOCs)

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