E-LEARNING PLATFORM ACCESS AND USAGE STATISTICS THROUGH DATA MINING: AN EXPERIMENTAL STUDY IN MOODLE

2016 ◽  
Author(s):  
Angelos Charitopoulos ◽  
Maria Rangoussi ◽  
Dimitrios Koulouriotis
Keyword(s):  
Data Mining ◽  
Experimental Study ◽  
Learning Platform ◽  
E Learning

scholarly journals Educational data mining in moodle data

2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Sushil Shrestha ◽  
Manish Pokharel
Keyword(s):  
Data Mining ◽  
Learning Process ◽  
Educational Data Mining ◽  
Feature Selection Method ◽  
Learning System ◽  
Significant Feature ◽  
Support Vector ◽  
Learning Platform ◽  
E Learning ◽  
Teaching Learning

<p>The main purpose of this research paper is to analyze the moodle data and identify the most influencing features to develop the predictive model. The research applies a wrapper-based feature selection method called Boruta for the selection of best predicting features. Data were collected from eighty-one students who were enrolled in the course called Human Computer Interaction (COMP341), offered by the Department of Computer Science and Engineering at Kathmandu University, Nepal. Kathmandu University uses Moodle as an e-learning platform. The dataset contained eight features where Assignment.Click, Chat.Click, File.Click, Forum.Click, System.Click, Url.Click, and Wiki.Click was used as the independent features and Grade as the dependent feature. Five classification algorithms such as K Nearest Neighbour, Naïve Bayes, and Support Vector Machine (SVM), Random Forest, and CART decision tree were applied in the moodle data. The finding shows that SVM has the highest accuracy in comparison to other algorithms. It suggested that File.Click and System.Click was the most significant feature. This type of research helps in the early identification of students’ performance. The growing popularity of the teaching-learning process through an online learning system has attracted researchers to work in the field of Educational Data Mining (EDM). Varieties of data are generated through several online activities that can be analyzed to understand the student’s performance which helps in the overall teaching-learning process. Academicians especially course instructors who use e-learning platforms for the delivery of the course contents and the learners who use these platforms are highly benefited from this research.</p>

Mining Student Participatory Behavior in Virtual Learning Communities

2012 ◽  
pp. 267-314
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Ion Gh. Rosca ◽  
Radu Mogos ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Data Mining ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Log Files ◽  
Learning Platform ◽  
Participatory Behavior ◽  
Clustering And Classification ◽  
E Learning ◽  
Exploratory Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing participatory behavior of the students enrolled in an online two-year Master degree programme in Project Management. The main data sources were the operational database with the students’ records and the log files and statistics provided by the e-learning platform. 129 enrolled students and more than 195 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining is decided, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to describe the participatory behavior of the students, as well as to identify the factors explaining the students’ behavior, and the relationship between academic performance and behavior in the virtual learning environment. The results are very encouraging and suggest several future developments.

Student Performance in E-Learning Environments

2011 ◽  
pp. 149-194
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Ion Gh. Rosca ◽  
Radu Mogos ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Learning Environments ◽  
Business Administration ◽  
Data Sources ◽  
Future Developments ◽  
Learning Platform ◽  
E Learning ◽  
The Relationship ◽  
Economic Studies

The aim of this chapter is to explore the application of data mining for analyzing performance and satisfaction of the students enrolled in an online two-year master degree programme in project management. This programme is delivered by the Academy of Economic Studies, the biggest Romanian university in economics and business administration in parallel, as an online programme and as a traditional one. The main data sources for the mining process are the survey made for gathering students’ opinions, the operational database with the students’ records and data regarding students activities recorded by the e-learning platform are. More than 180 students have responded, and more than 150 distinct characteristics/ variable per student were identified. Due the large number of variables data mining is a recommended approach to analysis this data. Clustering, classification, and association rules were employed in order to identify the factor explaining students’ performance and satisfaction, and the relationship between them. The results are very encouraging and suggest several future developments.

scholarly journals Towards an Intelligent Hybrid Recommendation System for E-Learning Platforms Using Data Mining

2017 ◽  
Vol 12 (06) ◽  
pp. 52 ◽  
Author(s):  
Marouane El Mabrouk ◽  
Salma Gaou ◽  
Mohamed Kamal Rtili
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Data Collection ◽  
Recommendation System ◽  
Digital Data ◽  
Automatic Data ◽  
Maximum Information ◽  
Learning Platform ◽  
E Learning ◽  
Hybrid Recommendation

Nowadays, Information and communications technology (ICT) becomes a very important thing in human life in different fields. They are used in many fields as information systems (software, middleware) using various telecommunication media to give users the ability to manipulate digital data. In addition, with new technology development, a new concept appeared in the late 90s and early millennium, which is distance learning through e-Learning platform. Recommendation systems become increasingly used in information systems and especially in e-learning platform. These systems are used to propose and recommend content of these platforms to users according to needs of the latter in order to allow them to have the maximum information for learning. In this paper, we present an intelligent hybrid recommendation system based on data mining. This system has four parts, the first for data collection and for center of interest construction by two modes: explicit data collection, which based on users and what they filled in their profiles, and implicit and automatic data collection by proposing a survey to users in order to gather information about their interest. A second part for processing information already collected in the previous part and for creating the learning model, classifying users who posted the content and classifying content also in order to send the results to the recommendation module. The third part is for making the similarity between learners and content and doing the recommendation for learners and the final part is for creating a log file of recommendation by learner, which will be used in the upcoming recommendation. According to results already done, we noticed that our proposition is satisfactory and the system is well optimized in terms of accuracy, response and processing time compared to the standard recommendation.

Cloud-Based E-Learning Platform

2016 ◽  
Vol 15 (5) ◽  
pp. 109-130 ◽  
Author(s):  
Mohsen El-Shawarby
Keyword(s):  
Learning Platform ◽  
E Learning

scholarly journals Validation of a TAM Extension in Agriculture: Exploring the Determinants of Acceptance of an e-Learning Platform

2021 ◽  
Vol 11 (10) ◽  
pp. 4672
Author(s):  
Ivonne Angelica Castiblanco Jimenez ◽  
Laura Cristina Cepeda García ◽  
Federica Marcolin ◽  
Maria Grazia Violante ◽  
Enrico Vezzetti
Keyword(s):  
Technology Acceptance ◽  
Time Management ◽  
Agricultural Sector ◽  
Geographical Location ◽  
Ease Of Use ◽  
Content Quality ◽  
Learning Platform ◽  
Primary Determinant ◽  
E Learning ◽  
Acceptance Model

Supporting education and training initiatives has been identified as an effective way to address Sustainable Development Challenges. In this sense, e-learning stands out as one of the most viable alternatives considering its advantages in terms of resources, time management, and geographical location. Understanding the reasons that move users to adopt these technologies is critical for achieving the desired social objectives. The Technology Acceptance Model (TAM) provides valuable guidelines to identify the variables shaping users’ acceptance of innovations. The present study aims to validate a TAM extension designed for FARMER 4.0, an e-learning application in the agricultural sector. Findings suggest that content quality (CQ) is the primary determinant of farmers’ and agricultural entrepreneurs’ perception of the tool’s usefulness (PU). Furthermore, experience (EXP) and self-efficacy (SE) shape potential users’ perceptions about ease of use (PEOU). This study offers helpful insight into the design and development of e-learning applications in the farming sector and provides empirical evidence of TAM’s validity to assess technology acceptance.

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