Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness

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Title: Mining High Dimensional Educational (Student) Data to Extract Patterns for Interestingness

An Introduction to Data Mining
Overview
Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Educational Data Mining

Educational Data Mining (EDM) is the application of Data Mining (DM) techniques to educational data, and so, its objective is to analyze these type of data in order to resolve educational research issues. EDM is concerned with developing methods to explore the unique types of data in educational settings and, using these methods, to better understand students and the settings in which they learn. EDM has emerged as a research area in recent years for researchers all over the world from different and related research areas such as: - Offline education try to transmit knowledge and skills based on face-to-face contact and also study psychologically on how humans learn. Psychometrics and statistical techniques have been applied to data like student behavior/performance, curriculum, etc. that was gathered in classroom environments - E-learning and Learning Management System (LMS). Elearning provides online instruction and LMS also provides communication, collaboration, administration and reporting tools. Web Mining (WM) techniques have been applied to student data stored by these systems in log files and databases. - Intelligent Tutoring (ITS) and Adaptive Educational Hypermedia System (AEHS) are an alternative to the just-put-it-on-the-web approach by trying to adapt teaching to the needs of each particular student. Data Mining has been applied to data picked up by these systems, such as log files, user models, etc. The Scope of Educational Data Mining

EDM has both applied research objectives, such as improving the learning process and guiding students’ learning; as well as pure research objectives, such as achieving a deeper understanding of educational phenomena. These goals are sometimes difficult to quantify and require their own special set of measurement techniques. - Data. In educational environments there are many different types of data available for mining. These data are specific to the educational area and so have intrinsic semantic information, relationships with other data and multiple levels of meaningful hierarchy. Some examples are the domain model, used in ITS and AEHS, that represents the relationships among the concepts of a specific subject in a graph or hierarchy format (e.g. a course consists of several chapters that are organized in lessons and each lesson includes several concepts); and the q-matrix that shows relationships between items/questions of a test/quiz system and the concepts evaluated by the test. Furthermore, it is also necessary to take pedagogical aspects of the learner and the system into account. - Techniques. Educational data and problems have some special characteristics that require the issue of mining to be treated in a different way. Although most of the traditional DM techniques can be applied directly, others cannot and have to be adapted to the specific educational problem at hand. Furthermore, specific data mining techniques can be used for specific educational problems. EDM involves different groups of users or participants. Different groups look at educational information from different angles according to their own mission, vision and objectives for using data mining. For example, knowledge discovered by EDM algorithms can be used not only...
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