scholarly journals Integration of Data Mining Clustering Approach with the Personalized E-Learning System

2018 ◽  
Author(s):  
Samina Kausar ◽  
Huahu Xu ◽  
Iftikhar Hussain ◽  
Wenhau Zhu ◽  
Misha Zahid
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Educational Data Mining ◽  
Learning System ◽  
Heat Diffusion ◽  
Data Mining Techniques ◽  
Learning Capabilities ◽  
E Learning ◽  
Clustering Approach ◽  
Hidden Patterns

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

scholarly journals Personalized E-Learning System Architecture Using Data Mining Approach

2018 ◽  
Author(s):  
Samina Kausar ◽  
Huahu Xu ◽  
Iftikhar Hussain ◽  
Wenhau Zhu ◽  
Misha Zahid
Keyword(s):  
Data Mining ◽  
System Architecture ◽  
Educational Data Mining ◽  
Learning System ◽  
Data Mining Techniques ◽  
Learning Capabilities ◽  
Learning System Architecture ◽  
E Learning ◽  
Hidden Patterns ◽  
Using Data

Educational data mining is an emerging discipline that focuses on development of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the field of education, the heterogeneous data is involved and continuously growing in the paradigm of big data. To extract meaningful knowledge adaptively from big educational data, some specific data mining techniques are needed. This paper presents a personalized e-learning system architecture which detects and responds teaching contents according to the students’ learning capabilities. Furthermore, the clustering approach is also presented to partition the students into different groups based on their learning behavior. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities to boost up their outcomes. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The various clustering methods K-means, Clustering by Fast Search and Finding of Density Peaks (CFSFDP), and CFSFDP via Heat Diffusion (CFSFDP-HD) are also analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of K-means with CFSFDP and CFSFDP-HD. The proposed e-learning system using data mining techniques is vigorous compared to typical e-learning systems. The data mining techniques are equally effective to analyze the big data to make education systems robust.

scholarly journals Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis

2018 ◽  
Vol 3 (11) ◽  
pp. 25-31 ◽  
Author(s):  
Minimol Anil Job
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Large Datasets ◽  
Business Value ◽  
Hidden Information ◽  
Data Mining Techniques ◽  
Learner Performance ◽  
Digital Era ◽  
Hidden Patterns

Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.

scholarly journals Educational data mining using cluster analysis and decision tree technique: A case study

2020 ◽  
Vol 12 ◽  
pp. 184797902090867
Author(s):  
Snježana Križanić
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Decision Tree ◽  
Student Behavior ◽  
Educational Data Mining ◽  
Education Institution ◽  
Data Mining Techniques ◽  
Event Logs ◽  
Hidden Knowledge ◽  
E Learning

Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs downloaded from an e-learning environment of a real e-course. Data mining techniques applied for the research are cluster analysis and decision tree. The cluster analysis was performed by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.

scholarly journals Integration of Data Mining Clustering Approach in the Personalized E-Learning System

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 72724-72734 ◽  
Author(s):  
Samina Kausar ◽  
Xu Huahu ◽  
Iftikhar Hussain ◽  
Zhu Wenhao ◽  
Misha Zahid
Keyword(s):  
Data Mining ◽  
Learning System ◽  
E Learning ◽  
Clustering Approach

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>

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