Effective Learning Behavior of Students’ Internet Based on Data Mining

2021 ◽  
Author(s):  
Jixiang Yang
Keyword(s):  
Data Mining ◽  
Learning Behavior ◽  
Effective Learning

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

Data Mining to Identify Project Management Strategies in Learning Environments

2019 ◽  
pp. 532-545
Author(s):  
Ana González-Marcos ◽  
Joaquín Ordieres-Meré ◽  
Fernando Alba-Elías
Keyword(s):  
Data Mining ◽  
Project Management ◽  
Learning Environments ◽  
Team Building ◽  
Management Strategies ◽  
Educational Data Mining ◽  
Effective Learning ◽  
Virtual Teamwork ◽  
Teachers And Students ◽  
Management Policies

Projects have become a key strategic working form. It is agreed that project performance must achieve its objective and be aligned with criteria that the project stakeholders establish. The usual metrics that are considered are cost, schedule, and quality. Configuration for the management of projects is a matter of decision that influences the project's evolution. There also are factors like virtual teamwork and team building processes that are relevant to that evolution. Effectiveness in managing projects depends on these factors and is investigated in this work by means of educational data mining as they can help to build more effective learning and operating procedures. The conclusions from this study can help higher education course designers as well as teachers and students by making apparent the influence of smarter strategies in the learning process. In fact, the same benefits will help practitioners too, as they can improve their continuous learning procedures and adjust their project management policies and strategies.

Mining Students' Learning Behavior in Moodle System

2014 ◽  
Vol 7 (4) ◽  
pp. 12-26 ◽  
Author(s):  
K. Touya ◽  
Mohamed Fakir
Keyword(s):  
Data Mining ◽  
Web Application ◽  
Educational Data Mining ◽  
Educational Environment ◽  
Learning Behavior ◽  
System Database ◽  
Hidden Knowledge ◽  
E Learning ◽  
Mining Algorithms ◽  
Interesting Area

In the last few years, Educational Data Mining has become an interesting area exploited to discover and extract hidden knowledge of students from educational environment data. During the establishment of this work an attempt was made to manage the extracted information using mining techniques. These methods took place in order to get groups of students with similar characteristics. The application of classification, clustering and association rules mining algorithms on the data stored on the e-learning (Moodle system) database allowed to extract knowledges that help to understand students' behaviors and patterns. Additionally, the development of a Web application for the educators is a tool to monitor their students learning behavior by monitoring the number of assignments taken, the number of quizzes taken, the number of forum post and read by students, etc. The knowledge obtained can help the instructors to make decision about their students' interacting with the courses activities in Moodle system, and to create an efficient educational environment. In this research, a Data Mining tool called RapidMiner was used for mining the data from the Moodle system database, and a web application written in PHP was established to aid teachers with statistics.

scholarly journals Educational Data Mining for Student Learning Pattern Analysis using Clustering Algorithms

2020 ◽  
Vol 9 (6) ◽  
pp. 481-488
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Data Mining Algorithm ◽  
Learning Behavior ◽  
Study Program ◽  
Data Mining Algorithms ◽  
Students First ◽  
And Performance

The exponential increase in universities’ electronic data creates the need to derive some useful information from these massive amounts of data. The progression in the data mining field causes it conceivable to educational data to improve the nature of educational processes. This study, thus, uses data mining methods to study the learning behavior and performance of university students. It focused on two aspects of the performance of the students. First, predicting students' learning behavior at the end of a complete year of the study program. Second, predict student performance with the help of the data model proposed by this study. Finally, provide course material recommendations using the data mining algorithm. Three data mining algorithms were considered which are K-Means, FCM, and KFCM., and maximum accuracy of 90.22% was achieved by KFCM. The study indicates that in terms of time and memory usages K-means algorithm give better results. This creates an opportunity for identifying students that may graduate with poor results or may not graduate at all, so early intercession might be possible.

scholarly journals Perilaku Belajar Efektif Terhadap Kemampuan Kognitif Psikomotorik Siswa Dalam Pembelajaran Matematika

2018 ◽  
Vol 1 (2) ◽  
pp. 77
Author(s):  
Sriyana Sriyana ◽  
Widodo Winarso
Keyword(s):  
Random Sampling ◽  
Quantitative Research ◽  
Mathematics Learning ◽  
Psychomotor Skills ◽  
Learning Behavior ◽  
Effective Learning ◽  
Analysis Technique ◽  
Simple Regression Analysis ◽  
Cluster Random Sampling ◽  
Simple Regression

The research was conducted to learn about the effect of effective learning behavior on students' cognitive-psychomotor skills in class IX mathematics learning at MTs Ash-Shiddiqiyyah Sumber Cirebon. This method of research is quantitative research. The population of this research is composed of all students of class IX MTs Ash-Shiddiqiyyah, The sample of this research is class IX B with a total of 24 students taken from Cluster Random Sampling. The data collection techniques used are 2, ie questionnaires for effective behavioral data and test data for the cognitive-psychomotor skills of students. The technique of data analysis for hypothesis testing is a simple regression analysis technique. The result of this research shows that the result of t-count = 7.522 is greater than t-table = 0.404 so it means that there is an influence between an effective learning behavior towards the ability cognitive-psychomotor of the student in mathematics

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