E-learning systems become more popular than it has ever been. However the popularity of e-learning systems, still suffer from some problems related to the completion rate of online courses and the learners’ failures. Nowadays, a lot of educational institutions are concentrating on how to solve those problems in order to improve the quality of the learning process. This paper presents an ontological model based on machine learning techniques to predict learners coming performance using data produced by learners through their interaction with the Learning Management System and Facebook groups. It also presents two different approaches to evaluate the ontology model in terms of completeness and correctness.
- data mining ,
- social networks
Many fields have been affected by Web technologies including the field of education. As they have to change the way of learning from how it was previously done . Today e-learning is gaining popularity with learners around the world because of this technology evolution. E-Learning systems become the usual learning environment as they enable the interaction among learners and provide access to a board range of learning resources with the usage of computer technology . They have been changed and evolved over the last years by utilizing internet and web technologies. Current researches are examining the learning process in different e-learning systems in order to find different variables that can affect the success of learners in e-learning systems.Mainly, e-learning systems based on a learning management system (LMS) which is special-purpose software. Today many universities start using LMS in their learning process. Itis used to manage logs of registered learners, course syllabus, and track learners’ activities as well as as learners’ results . Therefore, LMS is considered an important part of the e-learning solution provided by many educational centers. In addition, there is an increase use of social networks in the learning process. Learners usually use it for communicating with each other as well as for discovering, sharing, and exchanging knowledge. Facebook, Twitter, LinkedIn, Instagram, MySpace, and Google+ are the most common social networks used around the world. There are over than 1.65 billion active Facebook users . Because of Facebook’s popularity between learners and tutors, it has been chosen as source of learners’ social datain this research. This paper presents an ontological model based on data mining techniques to predict learners coming performance based on data produced by learners through their interaction with LMS (Learning Management System) and social network(course Facebook group). Two different approaches were used in order to evaluate the ontology model in terms of completeness and correctness using the Recall-Precision matrix.
The study proposed a model that can be used to predict learners’ future performance and identify learners with high risk to drop out a course or fail in the final exam. The prediction of learners’ future performance is an important task asithelpslearners to be aware of their progress as well as tutors to improve their teaching procedures in order to engage underachieving learners in an appropriate learning process. The educational data produced through learner engagement and interaction with both e-learning systems and social networks was analyzed through different classification techniques. Decision tree techniques give the best result among other data mining techniques. Random Forest, J48, and simple cart decision tree techniques give the best accuracy (95.83%). The initial findings of the study support the literature in that learners’ engagement with the e-learning system is outcomes. The results of this study show that some variables have a direct impact on learners’ performance such as the average number of comments, midterm grade, learning activities, session grades, study time, age, and gender. In the future, the study can be improved by examining more variables such as the relationship between technology acceptance, learning styles, and learners’ success. Moreover, we could consider the type of errors made by each learner in different tests in order to enrich our analysis and identify learner’s weak points.
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:978-1-7281-4213-5/20/$31.00 ©2020 IEEEAn Ontological Model to Predict Dropout Students Using Machine Learning Techniques
Ontological Model to Predict Dropout Students Using Machine Learning Techniques
2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia, 2020, pp. 1-5,
PDF reference and original file: Click here
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.