Application of educational data mining to create intelligent multi-agent personalised learning system

2017 ◽  
Author(s):  
Irina Krikun
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning System ◽  
Personalised Learning ◽  
Multi Agent

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>

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&rsquo; 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.

Adaptive Tutoring System With Application of Intelligent Agents

2018 ◽  
Vol 9 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Jaroslav Meleško ◽  
Eugenijus Kurilovas
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Learning Styles ◽  
Intelligent Agents ◽  
Web Of Science ◽  
Learning System ◽  
Tutoring System ◽  
Personalised Learning ◽  
Intelligent Software ◽  
Intelligent Software Agents

In this article, the authors suggest a methodology to adapt learning units to the needs and talents of individual students using an intelligent learning system. Learning personalisation is done based on several factors. Felder and Silverman Learning Styles model is used to create student's profile with conjunction of data mining technologies and previously recorded behaviour of the student. Firstly, the authors perform systematic review of application of intelligent software agents in teaching throughout Clarivate Analytics Web of Science database. Secondly, they present methodologies to personalise learning by means of intelligent technologies. They analyse preferences of students according to Soloman-Felder Learning Styles questionnaire. The resulting model of a student is used in the creation of a personalised learning unit. The model of an adaptive intelligent teaching system based on application of aforementioned technologies is presented in more detail.

scholarly journals An Educational Data Mining Model for Supervision of Network Learning Process

2018 ◽  
Vol 13 (11) ◽  
pp. 67
Author(s):  
Jianhui Chen ◽  
Jing Zhao
Keyword(s):  
Data Mining ◽  
Online Learning ◽  
Learning Process ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Learning System ◽  
Teaching Management ◽  
Management Platform ◽  
E Learning ◽  
Mining Model

To improve the school's teaching plan, optimize the online learning system, and help students achieve better learning outcomes, an educative data mining model for the supervision of the e-learning process was established. Statistical analysis and visualization in data mining techniques, association rule algorithms, and clustering algorithms were applied. The teaching data of a college English teaching management platform was systematically analyzed. A related conclusion was drawn on the relationship between students' English learning effects and online learning habits. The results showed that this method could effectively help teachers judge students' online learning results, understand their online learning status, and improve their online learning process. Therefore, the model can improve the effectiveness of students' online learning.

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&rsquo; 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 Survey Paper on Information based Learning System using Educational Data Mining

2021 ◽  
Vol 9 (VI) ◽  
pp. 1868-1873
Author(s):  
Dhanendra Kumar
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Data Warehouse ◽  
Educational Data Mining ◽  
Learning System ◽  
Essential Information ◽  
Educational Method ◽  
Data Mining Techniques ◽  
Survey Paper ◽  
Level Of Understanding

Educational Data Mining (EDM) is a platform for learning and exploring from data to get essential information and generate the unique pattern which will help study, analyse and skill student performance in academic. Various data mining methods can be apply to filter the data from data warehouse to implement data mining techniques which helps student for taking decisions for better outcome. The model which can be use in Educational data mining must be a constructive and descriptive model applied on data warehouse and must gather very accurate data for enhance the performance of study. Regression analysis can also be used to develop a model to use as study tool; it can be used dependent or independent variables. If the model is enough perfect for using as study tool then every cluster of data must be use that model to fetch the resultant data. Sometimes educational data mining is considered as overall performance of students, but each student has its own level of understanding the contents so that method must also be enough flexible for every one ; for fulfilling this requirement educational method can be complex, but once it is constructed then it will helpful for every one. This paper is describing various data mining techniques and their proper uses.

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