Personalized E-Learning System Based on User’s Performance and Knowledge: An Adaptive Technique

Personalized E-Learning System Based on User’s Performance and Knowledge: An Adaptive Technique

Table of Contents




Abstract

The Learning style of every individual is unique. We require some adaptive and personalization techniques to extract the required learning content from enormous content available. Here we build up an adaptive learning approach so that the personalization is conceivable. An adaptive learning system is developed by considering learner’s knowledge. We develop this by considering a specific domain. It takes the individual’s performance into consideration and modifies constantly by responding with student-specific learning content. We categorize learners based on the initial assessment conducted. Based on the results obtained and on his/her choice of the content displayed. Learner’s performance is observed and modeled, adaption is done accordingly through continuous appraisals conducted after each module and tweaked during interactions. The essential goal of this paper is to incorporate the revelation of ideal settings, where the students can improve their learning capacities.

Keywords

Personalization, Adaptive Learning, domain, assessment, interaction

INTRODUCTION

E-Learning is “Utilization of Internet Innovations and advancements to present a greater range of approaches that upgrade abilities and performance”. Over a decade there have been many developments in E-learning. Unlike, traditional learning which provides the same and limited learning content for any individual. Moreover, the learner can learn during his comfortable time and place.E-Learning provides plenty of resources. Learners can pick their learning content in the e-learning system. E-learning is a continuous process. Day by day the learning content and resource availability are becoming huge. It consumes plenty of time while searching for the required content.

There have to be effective strategies to provide necessary learning content. Not everyone has the same knowledge regarding a particular subject. Hence the learning material to be provided to the learner also to be different. Providing related content makes the system adaptive and personalized. While developing an E-learning system personalization is an important aspect to be taken into consideration. It is critical to provide a customized framework that can consequently adapt to learner’s learning styles and keenly prescribed content with personalization. Since the issue isn’t about making electronic learning resources but about finding how to provide the accessible data in customized way. This paper establishes personalized E-learning system which does student profiling first followed by a knowledge test that assesses the learner’s level of knowledge and displays the information accordingly and during the course of time learner progress is tracked and assessed by conducting an assessment after learning a chapter the system adapts accordingly.

CONCLUSION

In this paper, we have discussed the importance of personalization for enabling proficient e-Learning forms. It gives the learner the right content based on one’s performance so that it fits well. As the data is increasing day by day and searching requires a lot of time, the Initial knowledge test as well as student profiling enables the system to provide the right content from the very beginning. As the content in the learner’s level is viewed one after the other it enables the learner to learn in an organized way, with perfection and the following topics can be understood clearly. Even if learners in higher levels are given access to lower levels. We are not limiting the user to higher levels with a view that they may want to refer or learn previous topics in any circumstances. The tests conducted after each chapter not only allow the learner to know his abilities and can keep track of his performance and the areas yet to be improved but also the system to keep track of the user’s performance and provide the necessary suitable content. Moreover, learners can easily access the right content without searching for a longer time. The learner can view the complete progress along with the number of attempts and scores obtained for each attempt, final score, Percentage of tests completed.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.

Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.

The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.

FULL Paper PDF file:

Personalized E-Learning System Based on User’s Performance and Knowledge: An Adaptive Technique

Bibliography

author

Patchava. RamyaSree, Tammisetty. Bhuvaneswari, Vulchi. Vamsi Swapnika Reddy, Jonnalagadda. Surya Kiran

Year

2019

Title

Personalized E-Learning System Based on User’s Performance and Knowledge: An Adaptive Technique

Publish in

International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-4, November 2019

DOI

10.35940/ijrte.D8899.118419

PDF reference and original file: Click here

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Nasim Gazerani was born in 1983 in Arak. She holds a Master's degree in Software Engineering from UM University of Malaysia.

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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.

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Somayeh Nosrati was born in 1982 in Tehran. She holds a Master's degree in artificial intelligence from Khatam University of Tehran.

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