Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)
Auteurs : I. P. Ratnapala, R. G. Ragel, S. Deegalla
Résumé scientifique
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.
Les articles autour de lui, par date et par proximité
Plus haut, c’est plus proche de cet article ; à gauche, c’est plus ancien. Les traits relient les articles proches entre eux : les groupes de traits sont des sous-thèmes. Sélectionnez un point pour voir pourquoi il est là. Retour au résumé de cet article
Les articles autour de celui-ci
Classés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.
Les articles les plus proches
-
Identifying Online Profiles of Distance Learning Students Using Data Mining TechniquesOsama Islam, M. Siddiqui, N. R. Aljohani · 2019 · ICDTE · cité 5 fois · Pas encore résumé
Résumé scientifique
Educational data has grown over the years with the increased use of technology within educational environments. This has led to a huge amount of data being stored in various data sources representing the student, his/her activities, and other aspects relevant to the learning process. To meet this analytical need, Educational Data Mining (EDM) has emerged to assist educational institutions in identifying key benefits such as students at risk, the level of student engagement or predicting student performance. The aim of this research was to explore the various aspects of student interaction data using data mining techniques to identify relevant patterns of behaviors and possible key attributes…
-
Data mining of students’ behavior in E-learning systemO. Gushchina, A. V. Ochepovsky · 2020 · Journal of Physics, Conference Series · cité 9 fois · Pas encore résumé
Résumé scientifique
The article deals with educational data mining techniques aimed at increasing effectiveness of E-learning process as well as the idea of adaptive feedback, individual assessment and more personalized attention to student’s profile due to dynamic monitoring and tracking of students’ behavior in the E-learning system. The following techniques are identified: cluster analysis to determine the most popular time threshold for the task per session; analysis and visualization of data to highlight the main options that contribute to the effective completion of courses, and the most popular educational resources; V-fold cross-checking with the use of statistical processing aimed at students by their …
-
Assessing Online Learners’ Access Patterns and Performance Using Data Mining TechniquesM. C. Almodiel · 2021 · International Journal in Information Technology in Governance, Education and Business · cité 1 fois · Pas encore résumé
Résumé scientifique
Abstract: In an online learning environment where students do not have direct face-to-face interactions with instructors, observing their learning behaviors is quite challenging. As we move from traditional to virtual classrooms, it is important to look into the learning methods and approaches that can be used to have a deeper understanding of student participation in an online learning environment, especially in the Philippine setting where this type of research is relatively very scarce or not investigated at all. This study showed the advances in employing the combination of cluster analysis and data mining in assessing the students' online access patterns and performance. Using the data …
-
Online Learners' Behaviors Detection Using Exploratory Data Analysis and Machine Learning ApproachTuti Purwoningsih, H. Santoso, Z. A. Hasibuan · 2019 · International Conference on Intelligent Computing · cité 18 fois · Pas encore résumé
Résumé scientifique
In e-learning, detecting student behavior in interacting with the Learning Management system (LMS) is needed to create personalized learning. Analysis of the learning context is carried out to obtain patterns of student behavior in the teaching and learning process. This paper aims to detect student behavior in e-learning as a supporter of the analysis of the learning context. Student behavior patterns were obtained from LMS data and social demographic profile from the Student Information System (SIS). In processing the data set in this research uses the Exploratory Data Analysis and Machine Learning approach. This research was conducted on students participating in the e-learning course in …
-
Extraction of LMS Student Engagement and Behavioral Patterns in Online Education Using Decision Tree and K-Means AlgorithmMa.Corazon Fernando Raguro, Ace Carpio Lagman, Lalaine P. Abad et al. · 2022 · Asia Pacific Information Technology Conference · cité 9 fois · Pas encore résumé
Résumé scientifique
The Learning Management System is an innovative tool to facilitate online learning using technology. It monitors students’ learning progress and actions. As most academic institutions are already shifted from the traditional learning to online and blended learning approaches, analysis of students’ learning behaviors is empirical to design necessary and suited academic intervention programs. With this, the researchers aimed to identify significant attributes affecting student academic performance in an online education environment. The knowledge discovery in databases (KDD) was used to provide step by step process in extracting and evaluating the predictive and cluster models which aim to cla…
-
Do individual characteristics affect online learning behaviors? An analysis of learners sequential patternsAyça Çebi, R. Araújo, Peter Brusilovsky · 2022 · Journal of Research on Technology in Education · cité 13 fois · Pas encore résumé
Résumé scientifique
Abstract Online learning systems allow learners to freely access learning contents and record their interactions throughout their engagement with the content. By using data mining techniques on the student log data of those systems, it is possible to examine learning behavior and reveal navigation patterns through learning contents. This study was aimed at investigating how learners’ characteristics affect their online learning behavior by using sequential pattern mining and clustering techniques. Participants were 74 undergraduate students from a public university, whose interactions with four different kinds of learning contents in an online learning system were collected. Results showed s…
-
Use of educational data mining to identify distance learning students' profiles and patterns of participationThaiza Ramos, A. Gomes, Márcia Lucena et al. · 2017 · Iberian Conference on Information Systems and Technologies · Pas encore résumé
-
Clustering students’ online behavior and relation with academic performanceAlejandro Mozo Quesada, F. Valverde, S. Rojas · 2022 · 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET) · cité 2 fois · Pas encore résumé
Résumé scientifique
In recent years, online courses have become very popular due to the restrictions on the movement of people resulting from the COVID pandemics. The expansion of online learning platforms has renewed interest in developing techniques for delving into student behavior and improving their learning experience. This paper proposes a novel analysis of data obtained from Moodle’s Activity Log using Learning Analytics techniques and different clustering algorithms. The main goal is to understand the students’ task-related behavior, to generate a comprehensible model, and to comprehend how it is related to their academic performance. The students were grouped using a single feature calculated for each…
Les travaux antérieurs sur lesquels il s’appuie
-
Data mining in course management systems: Moodle case study and tutorialC. Romero, Sebastián Ventura, E. García · 2008 · Comput. Educ. · cité 1 189 fois · Pas encore résumé
Résumé scientifique
Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. This work is a survey of the specific application of data mining in learning management systems and a case study tutorial with the Moodle system. Our objective is to introduce it both theoretically and practically to all users interested in this new research area, and in particular to online instructors and e-learning administrators. We describe the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and ass…
-
Analyzing E-Learning Systems Using Educational Data Mining TechniquesAnduela Lile · 2011 · Mediterranean journal of social sciences · cité 24 fois · Pas encore résumé
Résumé scientifique
Recently, Educational Data Mining has become an emerging research field used to extract knowledge and discover patterns from E-learning systems. The educational system in Albania is currently facing a number of issues such as identifying students' needs, personalization of training and predicting the quality of student interactions. Educational Data Mining provides a set of techniques, which can help the educational system to overcome these issues. The objective of this research is to introduce Educational Data Mining, by describing a step-by-step process using a variety of techniques such as Attribute Weighting (Weighting by Information Gain, Relief, Hi-Squared, Uncertainty), Clustering (K-…
-
Web Usage Mining for a Better Web-Based Learning EnvironmentOsmar R Zaiane · 2001 · cité 308 fois · Pas encore résumé
-
Measuring self-regulation in online and blended learning environmentsLucy Barnard, William Y. Lan, Yen M. To et al. · 2009 · Internet and Higher Education · cité 902 fois · Pas encore résumé
-
Learning effectiveness in a Web-based virtual learning environment: a learner control perspectiveShih-Wei Chou, Chien-Hung Liu · 2005 · Journal of Computer Assisted Learning · cité 415 fois · Pas encore résumé
Les articles qui le citent
-
Identification of Influencing Factors for Enhancing Online Learning Usage Model: Evidence from an Indian UniversityS. Ahuja, Puninder Kaur, S. Panda · 2019 · International Journal of Education and Management Engineering · cité 15 fois · Pas encore résumé
Résumé scientifique
With advent of technology online education has become the core of educational settings worldwide. This paper aims to identify the factors that contribute in enhancing the online learning usage model in context of India, an emerging leader in Educational settings across the globe. In this study, data mining techniques were applied on the data collected from the log files of online courses. The initial investigations supported the use of custom build framework for teaching online courses. Data Structure course was taught using online platform and the data was collected using the log files. The data collected was further analysed using data mining techniques using Rapid miner tool. Although the…
-
Educational data mining and learning analytics for 21st century higher education: A review and synthesisH. Aldowah, H. Al-Samarraie, Wan Mohamad Fauzy · 2019 · Telematics and informatics · cité 338 fois · Pas encore résumé
Résumé scientifique
Abstract The potential influence of data mining analytics on the students’ learning processes and outcomes has been realized in higher education. Hence, a comprehensive review of educational data mining (EDM) and learning analytics (LA) in higher education was conducted. This review covered the most relevant studies related to four main dimensions: computer-supported learning analytics (CSLA), computer-supported predictive analytics (CSPA), computer-supported behavioral analytics (CSBA), and computer-supported visualization analytics (CSVA) from 2000 till 2017. The relevant EDM and LA techniques were identified and compared across these dimensions. Based on the results of 402 studies, it was…
-
Sequential, typological, and academic dynamics of self-regulated learners: Learning analytics of an undergraduate chemistry online courseKen-Zen Chen, Shao-Qing Li · 2021 · Computers and Education: Artificial Intelligence · cité 30 fois · Pas encore résumé
Résumé scientifique
Abstract This research explored the typologies of self-regulated learners in an asynchronous online Chemistry I (OCI) course of an undergraduate active learning program. Student readiness and capability regarding self-regulated learning (SRL), the activity logs in the learning management system, and academic achievement were collected to investigate behavioral sequences of different typologies of learning self-regulation. Seventeen students who took OCI in the 2019 school year were recruited for the study. Through learning analytics for the 3965 behavioral codes, the study generated three key results. First, hierarchical cluster analysis classified two typologies of self-regulated online lea…
-
Unsupervised learning for understanding student achievement in a distance learning settingShuang-Yan Liu, M. d'Aquin · 2017 · IEEE Global Engineering Education Conference · cité 24 fois · Pas encore résumé
Résumé scientifique
Many factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role in obtaining successful learning outcomes, as well as external factors such as regions where they come from and the learning environment that they can access. Identifying the relationships between student characteristics and distance learning outcomes is a central issue in learning analytics. This paper presents a study that applies unsupervised learning for identifying how demographic characteristics of students and their engagement in online learning activities can affect t…
-
A Comparative Study of the Various Clustering Algorithms in E-Learning Systems Using Weka ToolsJihen Khalfallah, J. Ben Hadj Slama · 2018 · 2018 JCCO Joint International Conference on ICT in Education and Training, International Conference on Computing in Arab · cité 7 fois · Pas encore résumé
Résumé scientifique
Clustering also called cluster analysis is the task of data objects grouping where group objects are similar. Simultaneously, they should be different from other groups objects. Such groups are termed as Clusters. We are study in this paper several clustering algorithms applied in E-learning systems. Our objective lies in performing a deep analysis of clustering data mining techniques both theoretically and experimentally and to do a comparative study in order to distinguish which technique is more suitable to identify e-learners profile and also to evaluate better student performance in engineering education. A good clustering method is able to yield high quality clusters. The algorithms un…
Aller plus loin
Comment ces articles sont-ils trouvés ?
Nous comparons le titre et le résumé de cet article avec ceux de tous les articles résumés sur ce site, et avec les articles que Semantic Scholar recommande, ceux qu’il cite et ceux qui le citent (pour un article très cité, ses citations les plus récentes et, depuis OpenAlex, les plus citées). Parmi les travaux antérieurs et les suites, les articles qui s’utilisent en profondeur, une méthode qu’il utilise et les suites très citées remontent un peu. Les niveaux (très proche, proche, apparenté) viennent de la similarité des textes, pas d’un jugement de qualité ; * signale un article sans résumé, comparé par son titre. La phrase sous un article est écrite par une IA à partir des deux résumés : vérifiez-la dans les articles. Un article jugé hors sujet par trois lecteurs quitte la liste.
Mis à jour le 5 novembre 2026.