Education data mining what is it

Definition[ edit ] Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning mining large repositories of data generated by or related to people’s learning data in educational settings.

Quite often, this data is extensive, fine-grained, what precise. For example, several mining management systems LMSs track information such as when each student mining each learning objecthow many times they accessed it, and how many minutes the learning object was displayed on the user’s computer screen.

As another example, intelligent tutoring systems record data every time a learner submits a solution to a problem; they may collect the time of the submission, whether or not the solution matches the expected solution, the amount of time that has passed since what last submission, the order in which solution components were entered into the interface, etc. The precision of this data is such that even a fairly short session with a computer-based learning environment e.

In other cases, the data is less fine-grained. For example, a student’s university education may contain a temporally ordered list of courses taken by the student, the grade that the student earned in each courseand when the student selected or changed his or her academic major. EDM leverages both types of data to discover meaningful information about different types of learners and how they learn, the structure of domain knowledgeand the effect of instructional strategies embedded within various learning environments.

These analyses provide data information that would be difficult to discern by looking at the raw data. For example, analyzing data from an LMS may reveal a relationship between the learning objects that a student accessed during the course and their final course grade.

What Is Data Mining?

Similarly, analyzing student transcript data may reveal a relationship between a student’s education in a education course and their decision to change their academic major. Such information provides insight into the design of learning environments, which allows students, teachers, school administrators, and mining policy makers to make informed decisions about how to interact with, provide, and manage educational resources.

History[ edit ] While the analysis of educational data is not itself a new practice, recent advances in educational technology data, including the increase in computing power and the ability to log fine-grained data about students’ use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.

This interest translated into a series of EDM education held from to as part of several international research conferences. Predicting students’ future learning behavior — With the use of student modelingthis goal can be achieved by creating student models that incorporate the learner’s characteristics, including detailed information such as their knowledge, behaviours and motivation to learn.

The user experience of the learner and their overall satisfaction with learning are also measured. Discovering or improving domain models — Through the various methods and applications of EDM, discovery of new and improvements to existing models is possible.

Examples include illustrating the educational content to engage learners and determining optimal instructional sequences to support the student’s learning style. Studying the effects of educational support that can be achieved through learning systems.

Advancing scientific knowledge about learning and learners by building and incorporating student models, education field of EDM what and the technology and software used. Users and stakeholders[ edit ] There are four main users and stakeholders involved with education data mining.

Data Mining in Today’s World

Learners — Learners are interested in understanding student needs and methods to improve the learner’s experience and performance. The challenge is to learn these groups based on the complex data as well as develop actionable what to interpret these groups. Educators can determine indicators that show student satisfaction and engagement of course material, and also monitor learning progress.