Abstract
Educational data mining (EDM) creates high impact in the field of academic domain. EDM is concerned with developing new methods to discover knowledge from educational and academic database and can be used for decision making in educational and academic systems. EDM is useful in many different areas including identifying at risk students, identifying priority learning needs for different groups of students, increasing graduation rates, effectively assessing institutional performance, maximizing campus resources, and optimizing subject curriculum renewal. This chapter discusses educational data mining, its applications, and techniques that have to be adopted in order to successfully employ educational data mining and learning analytics for improving teaching and learning. The techniques and applications discussed in this chapter will provide a clear-cut idea to the educational data mining researchers to carry out their work in this field.
TopMain Focus Of The Article
The purpose of this chapter is to provide a brief description of what is educational data mining, their techniques and applications and present some tools of educational data mining.
Key Terms in this Chapter
Enrollment Management: This term is frequently used in higher education to describe well-planned strategies and tactics to shape the enrolment of an institution and meet established goals.
Educational Objectives: It also known as the academic objectives and are of great importance when it comes to conception and designing of educational content.
Commercial Objectives: Commercial objectives are particularly important in case of private education, such as the creation of a niche and capturing the market in terms of enrollments.
Educational Data Mining: It refers to techniques and tools designed for automatically extracting meaning from large repositories of data generated by peoples learning activities in educational settings.
Weka: Weka work bench consists of several tools, algorithms and graphics methods that lead to the analysis and predictions.
Management Objectives: It can be of great use when it comes to the maintenance of educational infrastructure, which is a chief administrative oriented objective and involves the direct participation of higher authorities and senior management.
Educational Data Mining Methods: It come from different literature sources including data mining, machine learning, psychometrics, and other areas of computational modelling, statistics, and information visualization.