Using Instructive Data Mining Methods to Revise the Impact of Virtual Classroom in E-Learning (original) (raw)
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Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning
2010
In the past few years, Iranian universities have embarked to use e-learning tools and technologies to extend and improve their educational services. After a few years of conducting e-learning programs a debate took place within the executives and managers of the e-learning institutes concerning which activities are of the most influence on the learning progress of online students. This research is aimed to investigate the impact of a number of e-learning activities on the students' learning development. The results show that participation in virtual classroom sessions has the most substantial impact on the students' final grades. This paper presents the process of applying data mining methods to the web usage records of students' activities in a virtual learning environment. The main idea is to rank the learning activities based on their importance in order to improve students' performance by focusing on the most important ones.
International Association for Development of the Information Society, 2018
In the paper, we present the results of a case study conducted at Faculty of Administration, University of Ljubljana among 1st year undergraduate students. We investigated the correlations between students' activities in the e-classroom and grades at the final exam. The sample included 92 participants who took part at the final exam in the course Basic Statistics. In the e-classroom, students learn new content for individual self-study is prepared and their knowledge is checked with quizzes. In the empirical study, we used data mining software Orange for two tasks of predictive modelling: The research question was: based on the student's performance on quizzes is it possible to predict if (1) a student will pass an exam, and (2) a student's grade at the exam will be good. The empirical results indicate very strong connection between student's performance on quizzes and their grade at final exam in the course. Moreover, the results pointed out which quizzes, in other words topics, are most important for passing an exam or obtaining better grade.
Students behavioural analysis in an online learning environment using data mining
7th International Conference on Information and Automation for Sustainability, 2014
The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
Interactive Technology and Smart Education
Purpose This paper aims to study the relationship between students’ activities in the e-classroom and grades for the final exam. The study was conducted at the Faculty of Administration, University of Ljubljana among first-year undergraduate students. In the e-classroom, students learn new content for individual self-study, and their knowledge is checked with quizzes. Design/methodology/approach In the empirical study, the relationship between performance in quizzes and at the final exam was studied from two perspectives. First, successful and unsuccessful students (in terms of quizzes) were compared. Second, the Orange data mining software was used for two predictive modelling tasks. The research question was based on a student’s quiz performances, is it possible to predict whether the student will pass an exam and will the student’s grade for the exam be good. Findings The empirical results indicate a very strong connection between a student’s performance in quizzes and their scor...
Studying Academic Indicators within Virtual Learning Environment Using Educational Data Mining
International Journal of Data Mining & Knowledge Management Process
The rapid developments in information and communication technologies taking place recent years, make it easy for people to pursue their education distantly. The development of new technologies helped to open spatial and temporal boundaries of learning toward virtual learning, and helped to transform education process from its classical form of courses within classrooms to a new virtual form within virtual environments; Consequently, lessons and lectures are delivered using information and communication technologies tools, and student's attendance is virtually performed via Internet. Moreover, the education process in its new form becomes a supervised process, rather than a fully controlled process since virtual learning changed the education process pattern represented by the triangle (student, teacher and content) by increasing the importance of both "student" and "content" factors and transforming the main task of the "teacher" from "Teaching" to "Tutoring". Consequently, many questions are raised concerning students' performance and concerning the adequacy of virtual learning process. These questions are related to the need of accreditation for virtual learning and virtual universities. Our work aims to use Educational Data Mining (EDM) in order to study academic indicators concerning a representative sample of students in a virtual learning environment within Syrian Virtual University-SVU (The students who are following Bachelor of Information Technology Diploma-BIT). Our main goal is to discover the main factors influencing students' academic trajectory and students' academic evolution within such environment. Our results indicate strong correlation-in this virtual learning environment-between student average and some factors like: student's English level (despite the fact that Arabic language is the teaching language), student's age, student's gender, student's over-stay and student's place of residence (inside /outside Syria). Our results indicate also a need to modify the academic trajectory of students by changing the prerequisites of few courses delivered as a part of BIT diploma like Advanced DBA II, Data Security. In this research, the results also highlight the effect of the Syrian Crisis on students. Finally, we've suggested some future recommendations based on our observations and results to develop the current information system in SVU in order to help us to deduce some indicators more easily.
Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)
The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
Educational Data Mining: A review of evaluation process in the e-learning
Telematics and Informatics, 2018
Due to the growing interest in e-learning as an important process of teaching and learning, new mechanisms to evaluate its pedagogical effectiveness are necessaries. This review describes the scenario of 20 years corresponding to data mining research where the context is the elearning and the main subject is the evaluation aspect, which is considered a latent problem within this environment. Our goal is to provide an unexplored review of EDM research of the teaching and learning process considering the educational perspective. In order to obtain a more wide and complete review, the search of the bibliographic material was realized with the terms "data mining" and "education" which resulted in 525 articles. As exclusion criterion, articles that did not emphasize the improvement of the teaching and learning process were discarded, resulting in 72 articles. As result of our review, the analyzed papers show that the researches in EDM expanded into several areas and themes, for example, oriented studies on interactions between the educational actors, monitoring, and evaluation of teaching-learning process, administrators' evaluation about the adopted pedagogical actions, learning risks, and recommendation and recovery of educational media. The review allowed to present perspectives, identify trends and observe potential research directions, such as behavioral research, collaboration, interaction and performance in the development of teaching-learning activities.
E-Learning: A Milestone in the Research of Data Mining
2010
Mining and Data Warehousing are two most important techniques for pattern discovery and centralized data management in today's technology. E-Learning is one of the most significant applications of data mining. The main objective is to provide a proposal for a functional model and service architecture. The standards and system architecture are analyzed here. This paper gives importance to the integration of Web Services on the e-Learning application domain, because Web Service is the most advanced choice for distance education now. The process of e-Learning can be possible more effectively with the help of Web usage mining. More advanced tools are developed for online customer's behaviour to increase sales, and profit, but no such tools are developed to understand learner's behaviour in e-Learning. In this paper, some data mining techniques are discussed that could be used to enhance web-based learning environments.
Student Behavior Patterns in a Virtual Learning Environment
This work focuses on the identification of student behavior patterns obtained from their interactions on a virtual learning Environment (VLE). Clustering techniques were used to classify certain indicators and to obtain groups of students with similar characteristics. The activities performed are directly related to four Computer Science degree courses in the Distance Education modality. Generally, our results show that students interacted more with online forum, followed by the quiz, tasks, instant messaging, resources, and twitter. The knowledge acquired via the data mining techniques helped to discover certain characteristics of their online interaction, which should be taken into account when enhancing the teaching-learning process.
Data mining technology for the evaluation of web-based teaching and learning systems
2002
Instructional design for Web-based teaching and learning environments causes problems for two reasons. Firstly, virtual forms of teaching and learning result in little or no direct contact between instructor and learner, making the evaluation of course effectiveness difficult. Secondly, the Web as a relatively new teaching and learning medium still requires more research into learning processes with this technology. We propose data miningtechniques to discover and extract knowledge from a database -as a tool to support the analysis of student learning processes and the evaluation of the effectiveness and usability of Web-based courses. We present and illustrate different data mining techniques for the evaluation of Web-based teaching and learning systems.