Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

Divyansh Shankar Mishra,Sucheta V Kolekar,Abhinav Agarwal

doi:10.3991/ijet.v16i12.21789

Abstract

With the advent of the era of big data and Web 3.0 on the horizon, different types of online deliverable resources in the pedagogical field have also become raft. Massive Open Online Courses (MOOCs) are the most important of such learning resources that provide many courses at different levels for the learners on the go. The data generated by these MOOCs, however, is often unorganized and difficult to track or is not used to the extent that allows identification of learner types to facilitate better learning. The proposed approach in this paper aims to detect the learning style of a learner, interacting with the MOOC portal, dynamically and automatically through a novel, indigenous and in-built browser extension. This extension is used to capture the usage parameters of the learner and analyze learning behavior in real-time. The usage parameters are captured and stored as a learner ontology to ease sharing and operating across different platforms. The learning style so deduced is based on the Felder Silverman Learning Style Model (FSLSM), where learner’s behavior under multiple criteria, vis-`a-vis perception, input, understanding, and processing are measured. Based on the generated ontological semantics of learner’s behavior, multiple models can be made to facilitate precise and efficient learning. The result shows that this state-of-the-art approach identifies and detects the learning styles of the learners automatically and dynamically, i.e., changing over time

Highlights

Nowadays, Massive Open Online Courses (MOOCs) as a platform has become an indispensable part of any pedagogical system owing to their “massiveness” and “openness.” Such platforms have served to enrich existing courses and introduce new ones through open and continuous remote learning alongside flexible schedules and a modular structure
The proposed novel Browser Extension is useful to capture the usage data at the learner side and can be analyzed to identify the learning styles of the learners dynamically. This extension anonymously and securely tracks and maintains learner data generated on interaction with the platform for the construction of a semantic learner model or a learner ontology
The proposed approach is divided into three phases: the first phase focuses on how the data is collected at the learner side once the learner has interacted with course elements, second is concerned with how this data is semantically organized and the ontology is created, and the third phase is related to the identification of learning styles based on Felder-Silverman Learning Style Model (FSLSM) using an algorithmic approach

Summary

Introduction

MOOCs as a platform has become an indispensable part of any pedagogical system owing to their “massiveness” and “openness.” Such platforms have served to enrich existing courses and introduce new ones through open and continuous remote learning alongside flexible schedules and a modular structure. Most courses have a fixed sequence of interactable elements that needs to be adhered to for the completion of the course While this rigid structure might serve to teach the learner, the details of the topic, in the most organized manner, it limits exploration and remains the same regardless of the different learning requirements of a learner. Another limitation is the lack of personalization: learners are made to go through the course in one fixed sequence, but they hardly receive adequate personalized/customized recommendations even after interacting with the course to some degree. To engage learners better, MOOC platforms need to become more adaptive and for this to happen, accurate learner-type identification and classification by capturing important learner’s characteristics such as learning style becomes the most important step

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Conclusion