Using data mining techniques to automatically construct concept maps for adaptive learning systems

2010 ◽  
Vol 37 (6) ◽  
pp. 4496-4503 ◽  
Author(s):  
Shyi-Ming Chen ◽  
Shih-Ming Bai
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Concept Maps ◽  
Learning Systems ◽  
Data Mining Techniques ◽  
Using Data ◽  
Adaptive Learning Systems

Evaluation of Clustering Methods for Adaptive Learning Systems

2015 ◽  
pp. 237-260 ◽  
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

Evaluation of Clustering Methods for Adaptive Learning Systems

2016 ◽  
pp. 519-542
Author(s):  
Wilhelmiina Hämäläinen ◽  
Ville Kumpulainen ◽  
Maxim Mozgovoy
Keyword(s):  
Data Mining ◽  
Adaptive Learning ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Optimal Choice ◽  
Learning Systems ◽  
Learning Tools ◽  
Clustering Methods ◽  
Central Task ◽  
Adaptive Learning Systems

Clustering student data is a central task in the educational data mining and design of intelligent learning tools. The problem is that there are thousands of clustering algorithms but no general guidelines about which method to choose. The optimal choice is of course problem- and data-dependent and can seldom be found without trying several methods. Still, the purposes of clustering students and the typical features of educational data make certain clustering methods more suitable or attractive. In this chapter, the authors evaluate the main clustering methods from this perspective. Based on the analysis, the authors suggest the most promising clustering methods for different situations.

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.

Data Mining Applications in Computer-Supported Collaborative Learning

2015 ◽  
pp. 226-236
Author(s):  
Rosanna Costaguta
Keyword(s):  
Data Mining ◽  
Collaborative Learning ◽  
Learning Environments ◽  
Explicit Knowledge ◽  
Collaborative Work ◽  
Learning Systems ◽  
Computer Supported Collaborative Learning ◽  
Data Mining Techniques ◽  
Manual Processing ◽  
Using Data

Our own data mining techniques allow us to discover non-explicit knowledge from a large amount of data. Currently, Computer-Supported Collaborative Learning systems generate a wealth of data, derived from the stored interactions and product of collaborative work of students and teachers. Manual processing of these interactions is both costly and tedious, and practically impossible to do in real time. Because of this, there are now trends of research that attempt to achieve automatic processing using data-mining techniques. This chapter describes the phases and tasks involved in the entire process of knowledge discovery and also presents some research applying data mining to process the contributions of students and teachers in collaborative-learning environments.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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