scholarly journals Adjusting Felder-Silverman learning styles model for application in adaptive e-learning

Psihologija ◽  
2012 ◽  
Vol 45 (1) ◽  
pp. 43-58 ◽  
Author(s):  
Djordje Mihailovic ◽  
Marijana Despotovic-Zrakic ◽  
Zorica Bogdanovic ◽  
Dusan Barac ◽  
Vladimir Vujin
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Learning Styles ◽  
Learning Systems ◽  
Questionnaire Data ◽  
Data Mining Techniques ◽  
Significant Difference ◽  
E Learning ◽  
Using Data ◽  
Experimental Group

This paper presents an approach for adjusting Felder-Silverman learning styles model for application in development of adaptive e-learning systems. Main goal of the paper is to improve the existing e-learning courses by developing a method for adaptation based on learning styles. The proposed method includes analysis of data related to students characteristics and applying the concept of personalization in creating e-learning courses. The research has been conducted at Faculty of organizational sciences, University of Belgrade, during winter semester of 2009/10, on sample of 318 students. The students from the experimental group were divided in three clusters, based on data about their styles identified using adjusted Felder-Silverman questionnaire. Data about learning styles collected during the research were used to determine typical groups of students and then to classify students into these groups. The classification was performed using data mining techniques. Adaptation of the e-learning courses was implemented according to results of data analysis. Evaluation showed that there was statistically significant difference in the results of students who attended the course adapted by using the described method, in comparison with results of students who attended course that was not adapted.

scholarly journals Application of intellectual data analysis methods for digital educational platform

2020 ◽  
pp. 1-5
Author(s):  
Kristina Zhatkina ◽  
Oksana Kreider
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Services ◽  
Data Mining Techniques ◽  
Analysis Methods ◽  
The Neural Network ◽  
Competency Based ◽  
Using Data ◽  
Data Analysis Methods

This article describes the possibility of using data mining techniques. In order to join new carpet participants, it is necessary to understand that the system of interaction with them is public educational services. To implement digital educational platforms, it is proposed to create an agent that collects information about sites, and also selects and tests the architecture of the neural network to build an individual trajectory that is trained using the competency-based model.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals STUDENTS' PERSPECTIVES IN STUDYING MATHEMATICS SUBJECT THROUGH E-LEARNING TOOLS AT FOUNDATION EDUCATION LEVEL

2021 ◽  
Vol 6 (23) ◽  
pp. 108-117
Author(s):  
Che Haziqah Che Hussin ◽  
Nurliyana Juhan ◽  
Suriana Lasaraiya ◽  
Ayu Afiqah Nasrullah
Keyword(s):  
Data Analysis ◽  
Learning Styles ◽  
Education Level ◽  
Female Students ◽  
Learning Activities ◽  
Asynchronous Learning ◽  
Learning Tools ◽  
Significant Difference ◽  
E Learning ◽  
The Impact

The aim of the study was to find out how students preferred using asynchronous and synchronous e-learning tools. Asynchronous learning occurs when there is no predetermined time for it to take place. Learners can learn whenever and wherever they want, and they can take their time to learn what they need to know. Synchronous e-learning is characterized by structured and time-bound activities delivered via web conferencing and chatting. At the Preparatory Centre for Science and Technology, Universiti Malaysia Sabah (PCST, UMS) lecturers could conduct synchronous or asynchronous due to MCO which was enforced on March 18, 2020. As a result, this study was done to examine the impact of several learning styles on foundation UMS students during the COVID-19 crisis, including synchronous and asynchronous. The quantitative data analysis of research will be presented in this study. Microsoft Excel was used for data analysis. The male and female students' opinions were compared using an independent sample t-test. Additionally, the responses of students to various aspects of e-learning were represented using descriptive statistics. The findings found a significant difference in students' perceptions of the efficacy of asynchronous e-learning activities. Female students’ responses show that they found asynchronous is more effective than male students at the foundation education level. Students were found to have a greater interest in asynchronous and blended learning activities.

scholarly journals Review on Data Analysis Using Data Mining Techniques for Optimized Proteins Localization

IJARCCE ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 56-58
Author(s):  
Vivek Rajput ◽  
Prof. Amit Shrivastav
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Mining Techniques ◽  
Using Data
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