Data Mining - Concepts and Techniques.

Recently, “Big Data” has attracted not only researchers’ but also manufacturers’ attention along with the development of information technology. In this paper, the concept, characteristics, and applications of “Big Data” are briefly introduced first. Then, the various data involved in the three main phases of product lifecycle management (PLM) (i.e., beginning of life, middle of life, and end of life) are concluded and analyzed. But what is the relationship between these PLM data and the term “Big Data”? Whether the “Big Data” concept and techniques can be employed in manufacturing to enhance the intelligence and efficiency of design, production, and service process, and what are the potential applications? Therefore, in order to answer these questions, the existing applications of “Big Data” in PLM are summarized, and the potential applications of “Big Data” techniques in PLM are investigated and pointed out.

Big Data in product lifecycle management

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

Educational data mining and learning analytics: An updated survey

Educational data mining and learning analytics for 21st century higher education: A review and synthesis

Educational data mining concerns with developing methods for discovering knowledge from data that come from educational domain. In this paper we used educational data mining to improve graduate students’ performance, and overcome the problem of low grades of graduate students. In our case study we try to extract useful knowledge from graduate students data collected from the college of Science and Technology – Khanyounis. The data include fifteen years period [1993-2007]. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we present the extracted knowledge and describe its importance in educational domain.

Mining educational data to improve students' performance: a case study

The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students' performance and so on. The knowledge is hidden among the educational data set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is used to evaluate student's performance and as there are many approaches that are used for data classification, the decision tree method is used here. By this task we extract knowledge that describes students' performance in end semester examination. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling. Keywords-Educational Data Mining (EDM); Classification; Knowledge Discovery in Database (KDD); ID3 Algorithm.

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Brijesh Kumar Baradwaj

Papers

Mining Educational Data to Analyze Students" Performance

Mining Educational Data to Analyze Students' Performance