Cognitive Study of Data Mining Techniques in Educational Data Mining for Higher Education

Intrusion detection systems play a crucial rule in this era where networks reached almost any sector. Unfortunately, intrusion detection systems are far from perfectness. Therefore, researchers never stopped digging deeper to improve them. In this context, data mining techniques have been highly exploited for intrusion detection. In this paper, we present a comparative study of data mining techniques for intrusion detection. Specifically, we study the overall performances of those methods as well as the impact of training data size on their results. We use ISCX2012 as a benchmark for our experimentation. A realistic dataset that represents at a certain level today’s network traffic. The study shows that relatively old methods outperform some of the techniques highly used actually by the community. Regarding the impact of training dataset size, the investigated methods react differently from each other when we add more data to the training dataset. In addition, the results highlight the importance of attack traffic in the training dataset. Moreover, they strongly suggest the use of Random Forest for intrusion detection due to its linear performance relation with the training dataset’s size.

The data mining experiments from this chapter are a component of a larger research which is to be used to make several correlations, analysis in order to integrate data mining process in the managerial system for optimal decision support. I offered an insight of how data mining processes are being applied in the large spectrum of education by presenting recent applications and studies published in the scientific literature, considered to be relevant to the development of this emerging science. I presented my work through a number of experiments conducted over questionnaires data and scholastic databases at Faculty of Economics and Business Administration Cluj-Napoca, using classification learning and data clustering methods. In the last part of the chapter I introduced the concept of Academic Intelligence Management, and illustrated the integration of data mining processes and their particular role in higher education management and decision support. The studies will continue with deeper mining of educational topics, such as performance in scientific research, correlations between the students' knowledge and the competences demanded on the labor market, academic failure, to perceive what and how much the students know, to realize learning gaps, and also improve teaching methods and educational management processes.

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.

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.

Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. Initially terms like educational data mining emerged as a branch of data mining borrowing techniques from its ancestor. The challenges brought about by this large and heterogeneous data are diverse and needs a greater serious technical treatment. New and emerging fields like learning analytics have been introduced to manage the complexities of this data deluge. Learning analytics deals with data in the context of learner and the learning environment to improve the overall learning experience. The ultimate aim of the field is to make use of the data about learners and their environments to gain insights into the learning process using some of the well-known techniques and algorithms from the fields of data mining and machine learning. The process involves collecting, analysis of data and reporting the results to understand and optimize the learning experience. The fields of data mining and academic analytics closely related to learning analytics. Systematic Literature Review (SLR) is a robust, organized and rigorous literature review and reporting process aimed at identifying, collecting and synthesizing the relevant literature on a research question according to specified criteria. The process is more unbiased and balanced by systematic sequence of steps. This paper presents a systematic literature review by first developing the systematic literature review protocol and then discussing the main findings of the literature review by especially focusing on the applications and uses of machine learning and data mining techniques in the domain of learning analytics.
 
 Index Terms—Systematic Literature Review (SLR), Learning Analytics (LA), Big Data, Educational Data Mining (EDM), Machine Learning (ML).

Every year students success rate was analysed by the Educational Institutions to develop their Academic standard. To identify the success rate many kinds of techniques are used such as statistics, physical examination and currently ongoing datamining techniques. Data mining Techniques was widely used in many fields, it is also used in the Educational environment known as Educational Data Mining (EDM). Educational data mining generate prototype in solving the research problems in students data and used to locate the unseen patterns in the students detailed dateset. This paper uses the EDM to characterize the distinct factors affecting the students performance by making predictions with efficient algorithms. Educational professionals have to identify the causes for the student failure in academic performance and the students not succeed in completing their education which becomes a social problem these days. The machine learning algorithms help the researchers for evaluation of student’s learning habits, their academic performance and added enhancement if required. This paper would discuss different kinds of algorithms to analyse the economic background of the students which mainly affects the students performance. The dataset was utilized from the UCI Repository of secondary school students performance and analysed using the Weka tool for the datamining process.

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.

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.

<p class="Abstract"><span lang="EN-GB">In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.<em></em></span></p>

Business process management (BPM) is an administration discipline to design, improve and manage processes, and consists of several phases. One of them is the modeling of the current process (as is), during which the process is mapped as it is. One tool that can be used in the process discovery and modeling phase is data mining in information systems. Data mining techniques allow a series of applications in organizations, either as a verification process or as a discovery process. The purpose of this article is to analyze the use of data mining in the modeling stage of complex and not well structured processes when applying BPM to improve processes at a Federal Institute of Higher Education in Brazil. Due to the fact that the nature of the products and services requested in the institute’s system is very varied, the processes are not standardized, and the use of the information system is not done in a disciplined way, data mining was not enough to identify the complete processes and the flow of activities in detail. Although it was not enough, data mining facilitated the definition of instance flows and made it possible to detect critical points in the process, such as points with a long duration of time. With data mining, the analysis time, meetings and modeling effort of the analysts and users were reduced.KeywordsBusiness process managementBPMData miningNon-standard processesHigher education

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

Educational Data Mining (EDM) is a current trending multidisciplinary research area that explores the data and develops the methods in educational environments. The challenges in the EDM includes predicting and improving the student's performance which helps both instructors and students, identifying the key factors that affect the performance of students, understanding the learning process of the students, providing the best suggestions to students for their future development. The accurate prediction of student's performance helps the instructors to take proactive measures, improve teaching methodologies and guide the students in a right way. This also helps the student's to be active and encourage them to put extra effort on their studies. In this work, to achieve the goals of EDM various statistical and data mining techniques are used. The raw datasets obtained through various resources are first explored and the goals are identified. Afterwards, the data mining techniques are used to identify the hidden relationships and extracting the knowledge from these datasets.

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.

Educational data mining: A survey from 1995 to 2005

The data mining technique is used to transform raw data into useful information, which is carried out by recognizing the hidden patterns in the data. It is used in various sectors and fields such as education, marketing, fraud detection, manufacturing and health care. Educational Data Mining (EDM) is the Use of these techniques of data mining for data extraction from educational environment. Data mining in educational sector has widespread applications. Educational sector produces huge amount of student data which if analysed can be used for various purposes such as predicting the needs of students and evaluating their progress, also helping the institutions. We intend to compare all the techniques and algorithms used in various applications, thus assessing their efficiency.