Exploiting Learner Models Using Data Mining for E-Learning: A Rule Based Approach

2013 ◽  
pp. 77-105 ◽  
Author(s):  
Marianne Holzhüter ◽  
Dirk Frosch-Wilke ◽  
Ulrike Klein
Keyword(s):  
Data Mining ◽  
Rule Based ◽  
E Learning ◽  
Using Data ◽  
Rule Based Approach

Institutional Research Using Data Mining

2011 ◽  
pp. 66-102 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Vasile Bodea ◽  
Radu Mogos
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Virtual Learning Environment ◽  
Student Activities ◽  
Participatory Behavior ◽  
Student Profiles ◽  
Clustering And Classification ◽  
E Learning ◽  
Using Data ◽  
And Behavior

The aim of this chapter is to explore the application of data mining for analyzing academic performance in connection with the participatory behavior of the students enrolled in an online two-year Master degree program 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. One hundred eighty-one enrolled students, and more than 150 distinct characteristics/ variables per student were used. Due to the large number of variables, an exploratory data analysis through data mining was chosen, and a model-based discovery approach was designed and executed in Weka environment. The association rules, clustering, and classification were applied in order to identify the factors explaining the students’ performance and the relationship between academic performance and behavior in the virtual learning environment. Data mining has revealed interesting patterns in data. These patterns indicate that academic performance is related to the intensity of the student activities in virtual environment. If the student understands how to work and she/he is motivated to communicate with others, then he might have a good academic performance. Based on clustering analysis, different student profiles were discovered, explaining the academic performance. The results are very encouraging and suggest several future developments.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

Rules Extraction using Data Mining in Historical Data

2016 ◽  
pp. 263-279
Author(s):  
Manish Kumar ◽  
Shashank Srivastava
Keyword(s):  
Data Mining ◽  
Weather Forecasting ◽  
Satellite Communications ◽  
Building Blocks ◽  
Predictive Analysis ◽  
Rule Based ◽  
Using Data ◽  
Rules Extraction ◽  
The Impact ◽  
Expected Outcomes

Rules are the smallest building blocks of data mining that produce the evidence for expected outcomes. Many organizations like weather forecasting, production and sales, satellite communications, banks, etc. have adopted this mode of technological understanding not for the enhanced productivity but to attain stability by analyzing past records and preparing a rule-based strategy for the future. Rules may be extracted in different ways depending on the requirements and the dataset from that has to be extracted. This chapter covers various methodologies for extracting such rules. It presents the impact of rule extraction for the predictive analysis in decision making.

scholarly journals Review on Secure E-Learning using Data Mining Techniques and Concepts

2015 ◽  
Vol V4 (07) ◽  
Author(s):  
Miss. Priyanka R. Pradhan ◽  
Mr. R. B. Kulkarni ◽  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
E Learning ◽  
Using Data

scholarly journals Data Mining Untuk Mengetahui Tingkat Loyalitas Konsumen Terhadap Merek Kendaraan Bermotor dan Pola Kecelakaan Lalulintas di DIY

2011 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Agus Sasmito Ariwibowo ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Brand Loyalty ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Police Department ◽  
Mining Method ◽  
Data Traffic ◽  
Rule Based ◽  
Accident Data ◽  
Using Data

Abstract— The data of vehicle sales and traffic accident can be processed into information that is important for vehicle dealers and the Police Department. Those important information researched are the level of consumer loyalty to the vehicle brands and to predict the vehicle’s brands that will be purchased by a consumer. The study also tries to analyze the traffic accident data to find out is there any link between the occurrence of an accident to a certain brand of vehicle.                This research implementing data mining method called ‘rule based classification’ to establish the sales of vehicles rules by which can be used to classify consumer into group level of brand loyalty and also estimate the brand of the next vehicle’s brand that will be purchased by the consumer. This research will process the data traffic accident by using data mining techniques called Apriori Method. Apriori Method is used to identify a pattern of accidents based on brand, type of vehicles, and the vehicle’s color. The results are used to estimate whether there is any correlation between the occurrences of a traffic accident to a particular brand.                The result can help companies or vehicle dealers to obtain information about the level of the consumer’s brand loyalty to the dealer’s brand and to predict the brand that the consumer would be buy for the next vehicle. The result can also help the Police Department to find out whether there is any correlation between the occurrence of traffic accidents to the brand, type and the color of vehicle. Keywords— rule based classification, apriori, brand loyalty, traffic accident.

Personalization of e-Learning Using Data Mining

2010 ◽  
Vol 17 (4) ◽  
pp. 585-594 ◽  
Author(s):  
Beulah Christalin La Christudas ◽  
Sujni Paul ◽  
Kirubakaran Ezra ◽  
Saravanan Venkatraman
Keyword(s):  
Data Mining ◽  
E Learning ◽  
Using Data
Sign in / Sign up

Export Citation Format

Share Document