scholarly journals ПОШУК, ОТРИМАННЯ Й АНАЛІЗ ДАНИХ В ОСВІТІ: СУЧАСНИЙ СТАН І ПЕРСПЕКТИВИ РОЗВИТКУ

2016 ◽  
Vol 50 (6) ◽  
pp. 152
Author(s):  
Yurii O. Kovalchuk
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning Systems ◽  
Basic Principles ◽  
Data Mining Algorithms ◽  
Independent Direction ◽  
E Learning ◽  
The Subject ◽  
Classification And Regression ◽  
Mining Algorithms

The main tasks (classification and regression, association rules, clustering) and the basic principles of the Data Mining algorithms in the context of their use for a variety of research in the field of education which are the subject of a relatively new independent direction Educational Data Mining are considered. The findings about the most popular topics of research within this area as well as the perspectives of its development are presented. Presentation of the material is illustrated by simple examples. This article is intended for readers who are engaged in research in the field of education at various levels, especially those involved in the use of e-learning systems, but little familiar with this area of data analysis.

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 Data mining fool’s gold

2020 ◽  
Vol 35 (3) ◽  
pp. 182-194
Author(s):  
Gary Smith
Keyword(s):  
Data Mining ◽  
Scientific Method ◽  
Large Data ◽  
Learning Systems ◽  
Large Data Sets ◽  
Data Sets ◽  
Computer Algorithms ◽  
Data Mining Algorithms ◽  
Rigorous Testing ◽  
Mining Algorithms

The scientific method is based on the rigorous testing of falsifiable conjectures. Data mining, in contrast, puts data before theory by searching for statistical patterns without being constrained by prespecified hypotheses. Artificial intelligence and machine learning systems, for example, often rely on data-mining algorithms to construct models with little or no human guidance. However, a plethora of patterns are inevitable in large data sets, and computer algorithms have no effective way of assessing whether the patterns they unearth are truly useful or meaningless coincidences. While data mining sometimes discovers useful relationships, the data deluge has caused the number of possible patterns that can be discovered relative to the number that are genuinely useful to grow exponentially—which makes it increasingly likely that what data mining unearths is likely to be fool’s gold.

scholarly journals INTRODUCTION OF EDUCATIONAL DATA MINING BY USING A VARIETY OF TECHNIQUES IN ORDER TO ACHIEVE THE GOAL FROM THE MOODLE LMS

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ali Akhtar ◽  
Mohammad Serajuddin ◽  
Hasan Zafrul
Keyword(s):  
Data Mining ◽  
Learning Styles ◽  
Learning Style ◽  
Data Extraction ◽  
Verbal Learning ◽  
Educational Data Mining ◽  
Knowledge Presentation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms

Different works relating to this specialty have been done in recent years and several data extraction approaches have been used to solve numerous educational problems. This analysis compares the Felder-Silverman Learning Style Model component of student activity in Moddle class with three data mining algorithms for the identification of knowledge presentation dimension (visual/verbal) learning style. This study analyzes Moodle LMS student log data using data mining strategies to identify their learning styles that rely on one aspect of the learning style of Feld-Silverman: visual/verbal. The WEKA compares various classification algorithms as classified J48 Decision Tree, Naive Bayes and Portion. The selected classifiers were evaluated using a 10-fold cross validation. The tests revealed that at 71.18 percent the Naive Bays achieve the strongest score. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0782/a.php" alt="Hit counter" /></p>

scholarly journals Student Learning Prediction Using Machine Learning Techniques

2019 ◽  
Vol 8 (6) ◽  
pp. 4179-4183
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Student Performance ◽  
Machine Learning Techniques ◽  
Data Mining Algorithms ◽  
Learning Techniques ◽  
Final Exams ◽  
E Learning ◽  
Using Data ◽  
Mining Algorithms

Now a day’s e-learning is smartly growing technology. This technology is more helpful for students to communicate with their professors through chats or emails. ELearning also removes the obstacle of physical presence of an Elearner. The main aim of this paper is to predict student performance in their final exams using different machine learning techniques. Information like attendance, marks, assignments, class participation, seminar, CA, projects and semester are collected to predict student performance. This prediction helps the instructors to analyze their students based on their performance. For that we have used WEKA tool for the prediction of the student performance. WEKA (Waikato Environment for Knowledge Analysis) is one of the data mining too which is used for the classification and clustering using data mining algorithms. This prediction helps the students and the staffs to know how much effort their students need to be put in their final exams to get good marks.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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