Performance Analysis of E-Learning System Using Data Mining Techniques

Educational data mining (EDM) is an up-coming interdisciplinary research field, in which data mining (DM) techniques applying in educational data. Its objective is to better understand how students gain knowledge and recognize the settings in which they learn to improve educational outcomes. Educational systems can store a huge amount of data that coming from multiple sources in different formats. Each particular educational problem requires different types of the mining techniques because traditional DM techniques cannot be applied directly to these types of data and problems. There are many general data mining tools are available but these are not designed for deal with educational data and an educator cannot use these tools without knowledge of data mining concepts. To overcome from this problem many authors provided educational data mining tools with different data mining techniques for different purposes. This paper surveys the EDM Tools from 2001 to 2016 and presents the consolidated list of tools in one place to help the EDM researchers. Then categorizes the tools based on data mining methods and explains the usage of EDM Tools.

In recent years, research on Educational Data Mining (EDM) has developed rapidly. However, most researches focus on data source issues, and ignore the importance of data preprocessing and data mining algorithms. This paper has studied EDM, with a special focus on educational big data mining algorithms. Firstly, it analyzed the relevant elements of EDM and introduces big data technology based on the requirements of educational data application. Then it introduced the common educational big data mining algorithms and their applications, and finally discussed the development trend of educational big data mining algorithms.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Due to the growing interest in data mining and the educational system, educational data mining is the emerging topic for research community. The various techniques of data mining like classification and clustering can be applied to bring out hidden knowledge from the educational data. Web video mining is retrieving the content using data mining techniques from World Wide Web. There are two approaches for web video mining using traditional image processing (signal processing) and metadata based approach. In this paper, we focus on the education data mining and precisely MOOCs which constitute a new modality of e-learning and clustering techniques. We present a methodology that can be used for mining Moocs videos using metadata as leading contribution for knowledge discovery.

The possibilities of implementing data mining in educational analytics are investigated, the main directions of intellectual analysis of educational data in the framework of interaction of participants in the educational process are highlighted. The use of e-learning systems in the educational process leads to the accumulation of large volumes of educational data and digital footprints of students. The use of Educational Data Mining methods for analyzing this information, forecasting and visualizing it in the form of interactive reports allows to reveal hidden knowledge and patterns that significantly improve the training of future professionals. The purpose of the work is to investigate the development of intellectual analysis of educational data, the main tasks and methods of intellectual analysis to identify promising areas of its application in information systems and e-learning technologies of higher education institutions. Methodology. Based on the analysis of literature sources, the main tasks are reviewed and the stages of intellectual analysis of educational data are identified in order to improve the efficiency of the learning process in higher professional education. By means of system analysis, a scheme of the process of working with big data generated by e-learning systems is proposed. The relevance of using Data Mining methods in higher education is reviewed and substantiated. The scientific novelty of the study is to substantiate the scheme of information technology using data mining methods obtained from LMS to optimize educational processes and predict student trajectories. Conclusions. It is proved that the development of information technology based on the use of data mining methods in the implementation of e-learning systems contributes to solving problems related to understanding student behavior, improving the quality of e-courses, improving teaching methods, reducing the cost of organizing the learning process and determining further directions of educational analytics in accordance with global trends.

In today's conditions, customer loyalty has gained importance with the increase in the competitive environment between companies, the development of marketing strategies and the improvement of companies. Therefore, it is essential to acquire customers for a company to survive. Retaining an existing customer in the telecommunication sector is less costly than gaining a new customer. Customer churn analysis is the process of predicting customers with high abandonment requests by examining the offers and utilizable behaviors. Customer churn analysis provides services to develop various campaigns aiming to increase the company’s loyalty by predicting the customers who are planning to move to another company. In this way, it gives the company a competitive advantage. This study aims to make predictions by developing models for customer churns through data mining and machine learning methods in the telecommunication sector. In addition, we believe that the application in this article will contribute to data analysts and academicians who will want to analyze customer churn with different data sets in telecommunication and other sectors in the future. The analysis in this study is carried out on a data set obtained from an open-access database, including 20 transaction records for the customer from 7043 customers and whether the customer left the company. Among the data mining methods, Random Forest (RF), Support Vector Machines (SVM) and Multilayer Artificial Neural Networks (ANN) are modeled in open-source Phyton environment. The results have shown that ANN has fared better at classifying customers than other machine learning methods.

Predictive analytics has emerged as a powerful tool in the field of financial engineering, enabling organizations to forecast trends, optimize strategies, and mitigate risks. Data mining techniques play a crucial role in extracting meaningful patterns from large volumes of financial datasets, contributing to the development of predictive modeling techniques. The purpose of this paper is to explore the application of various data mining techniques in predictive analytics within the financial sector, with a focus on their effectiveness in risk management, fraud detection, and portfolio optimization. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are increasingly being employed to model complex financial systems and enhance predictive accuracy. Additionally, techniques like clustering, association rule mining, and time series analysis are used to uncover hidden relationships and temporal patterns in financial datasets. Despite the significant advancements in these techniques, challenges remain in terms of data quality, computational complexity, and interpretability of results. This paper addresses these challenges and provides a comprehensive review of the most widely used data mining methods in financial engineering. Furthermore, it discusses the potential for future advancements in data mining algorithms and their integration with other emerging technologies like artificial intelligence and big data analytics. The findings indicate that while data mining offers significant potential in financial analytics, careful consideration must be given to the selection of appropriate techniques and the proper handling of data to ensure reliable predictions. Ultimately, this paper aims to contribute to the ongoing development of robust predictive modeling techniques in financial engineering by emphasizing the importance of data mining as an indispensable tool.

Education can be utilized as a tool to face many problems, overcome many hurdles in life. The knowledge obtained from education helps to enhance opportunities in one's employment development. To extract useful information from the knowledge obtained, Educational Data Mining is widely used. Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution (school) and can be used to discover a unique pattern of students' academic performance and behavior. The present work intends to enhance students' academic performance in secondary school using data mining techniques. Real data was collected using school reports and questionnaire method by the Portugal school which has been used in this paper. Naive Bayesian algorithm can be easily implemented to predict the students' academic performance and behavior. Classification of students into two classes, pass and fail, involves training phase and testing phase. In training phase, Naive Bayes classifier is built and in the testing phase, Naive Bayes classifier is used to make the prediction. The accuracy of the classifier is calculated using WEKA tool in which confusion matrix is generated. The accuracy of the classifier obtained is 87% which can be further improved by the selection of appropriate attributes. Developing the classification algorithms in this way helps to obtain a more efficient student performance predictor tool using other data mining algorithms and it also helps to improve the quality of education in an educational institution.

The research analyzes the potential of AI and the prospects of using data mining in the educational process of universities. The authors show that students can apply tools and methods of educational data mining to solve tasks across various disciplines. This positively impacts the development of knowledge and skills in advanced methods of experimental data processing. The research identifies potential challenges in training and retraining educational personnel to study and apply educational data mining. These challenges include a lack of knowledge in mathematical disciplines and information technologies, the need to improve training programs necessary for mastering educational data mining, a shortage of instructors ready to support this discipline, and an insufficiently developed innovative thinking style among students. The authors developed and tested digital educational content and assignments for students on using AI and big data analysis in education. The research outlines the prospects for further study in teaching students to apply data mining.

PurposeIn accounting and finance, researchers have used many ways to detect manager’s fraud risk. Until now, many researchers have used some data mining methods in these two fields to detect this risk. The purpose of this paper is to compare the precision of two data mining methods in detecting such a risk.Design/methodology/approachFor this purpose, this paper analyzed the texts of board’s reports and used two methods including the convex optimization (CVX) method and least absolute shrinkage and selection operator (LASSO) regression method. In this way, the words of these reports, which have the greatest power in explaining the manager’s high fraud risk index, were identified. Using these words, this paper presented a model that could detect manager’s high fraud risk index in companies.FindingsThe results indicated that both methods can detect the manager’s high fraud risk index with a precision between 82.55 and 91.25 percent. The LASSO method was significantly more precise than the CVX method.Research limitations/implicationsThe lack of access to an official and reliable list of firms suspected to fraud and the lack of access to the Microsoft Word (MS Word) file of board’s reports were two of the most important limitations of this study.Practical implicationsRegulatory bodies and independent auditors can consider the proposed methods in this study for assessing the fraud risk for a firm or other legal parties.Originality/valueThis paper avoided using merely financial statements data to detect the manager’s fraud risk index and focused on texts of board’s reports for the detection process. The capabilities of data mining and text mining methods for detecting the manager’s fraud risk index using board’s reports were tested in this paper. By comparing CVX and LASSO results, this paper indicated that methods with a binary-dependent variable have more power and are more precise than methods with continuous-dependent variables for detecting fraud.

The aim of this research is to explore and analyze the benefits of risk classification methods by using data mining techniques on premium ratemaking in nonlife insurance. We rely on generalized linear models (GLMs) framework for nonlife premiums and examine the impact of specific data mining techniques on classifications of risk in motor hull insurance in Bosnia and Herzegovina. We study this relationship in an integrated framework considering a standard risk model based on the application of Poisson GLM for claims frequency estimate. Although GLM is a widely used method to determine insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The application of the data mining method in this paper aims to improve the results in the process of nonlife insurance premium ratemaking. The improvement is reflected in the choice of predictors or risk factors that have an impact on insurance premium rates. The following data mining methods for the selection of prediction variables were investigated: forward stepwise and neural networks. We provide strong and robust evidence that the use of data mining techniques influences premium ratemaking in nonlife insurance.KeywordsRatemakingForward stepwiseNeural networksNonlife insuranceGLM

Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

Oil well productivity classification and abundance prediction are important for estimating economic benefit of a well. However, it is difficult to predict because well logs are complex and the amount of data collected today has far exceeded our ability to refine and analyze without the use of automated analysis techniques. In response to the problem above mentioned, data mining technology in recent years has shown the ability for discovering information and effectively extracts information from massive observational data sets that can be used to decisions. Especially, classification and prediction methods, are receiving increasing attention from researchers and practitioners in the domain of petroleum exploration and production (E&P) in China. Therefore, data mining is regarded as one of the ten key techniques for challenging problem of oil exploration and development. In this paper, four distinct kinds of classification and prediction methods in data mining, including decision tree (DT), artificial neural network (ANN), support vector machine (SVM) and Bayesian network are used to two real-world case studies. One is hydrocarbon reservoir productivity classification with 21 samples from 16 wells logging data in Karamay Oilfield 8 th district reservoir. The results show that SVM and Bayesian are superior in the classification accuracy (95.2%) to DT, ANN and SVM, and can be considered as a prominent classification model. Another is reservoir abundance prediction with 17 mature accumulation systems samples in JiYang depression basin. The results show that SVM is superior in the prediction accuracy (91.92%) to DT, ANN and Bayesian, and can be taken as an excellent prediction model.

The aim of this study is to highlight the importance of the unsupervised learning in Data mining and CRM fields. Data mining commonly known by its acronym KDD: knowledge discovery in data base, it refers to all methods and algorithms used for data exploration or prediction in large data bases volumes, Data mining is very important in various fields such as science, business and other areas deal with a large data set. CRM: Customer Relationship Management is an integrated information system that is used to plan, schedule and control the pre-sales and post-sales activities in an organization, both CRM and data mining techniques helps organizations maximize the value of every customer interaction and drive superior corporate performance. Clustering is one of the favoured used methods in data mining: The objective of this study is to implement the clustering algorithm K-Medoids via a shell script applied on a set of Analytical CRM data stored in Teradata environment.

The advancement of medical technology has increased the amount of medical data. Data mining has become an essential tool for hospital management and medical research because it enables people to manage accumulated data and discern possible meaningful information from databases. Further medical research can thus use the important data. The current study compared 3 data mining methods, and attempted to identify the determining factors of breast cancer. The data were collected from a hospital in Taiwan and had been gathered between 2002 and 2010. Our study examined 1357 cases and 7 variables. The dataset was divided into 10 categories of training sets and testing sets. Three popular data mining algorithms (decision tree C5.0, SVM, and logistic regression) were used to predict the patients’ survival and death rates. The results showed that decision tree C5.0 outperformed both SVM and logistic regression. For training sets, the decision tree C5.0 achieved a classification accuracy of 95.8% with a sensitivity of 97.7% and a specificity of 94.7%. For testing sets, the decision tree C5.0 achieved a classification accuracy of 94.9% with a sensitivity of 95.7% and a specificity of 94.3%. The results suggested that decision tree C5.0 is the best model for prognosis in clinical practice. Our findings may provide a reference for doctors to identify new cases of breast cancer.