Educational data mining is an emerging trend, concerned with developing methods for exploring the huge data that come from the educational system. This data is used to derive the knowledge which is useful in decision making. EDM methods are useful to measure the performance of students, assessment of students and study students’ behavior etc. In recent years, Educational data mining has proven ...

Data mining is an important paradigm for educational assessment. The usual assumption is that mining is performed after educational activity with that activity having been designed without regard for the mining process. This paper discusses how the prospects for successful mining can be improved by imposing constraints or biases on the activities and instruments that generate the data. These bi...

The idea of classification is to place an object into one class or category, based on its other characteristics. In education, teachers and instructors are all the time classifying their students for their knowledge, motivation, and behaviour. Assessing exam answers is also a classification task, where a mark is determined according to certain evaluation criteria. Automatic classification is an...

Educational Data Mining is a growing trend in case of higher education. The quality of the Educational Institute may be enhanced through discovering hidden knowledge from the student databases/ data warehouses. Present paper is designed to carry out a comparative study with the TDC (Three Year Degree) Course students of different colleges affiliated to Dibrugarh University. The study is conduct...

Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings , and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and u...

A great deal of research in educational data mining is geared towards predicting student performance. Bayesian Knowledge Tracing, Performance Factors Analysis, and the different variations of these have been introduced and have had some success at predicting student knowledge. It is worth noting, however, that very little has been done to determine what a student’s first course of action will b...

Educational data mining (EDM) and Learning analytics are still rather young research areas. The goal of this panel is to share different views on what major challenges researches need to address in EDM, learning analytics and related research areas including but not limited to User modeling, AI in Education, and Learning Sciences. The representatives of the corresponding communities are invited...

Building on the promise shown in game-based learning research, this paper explores methods for Game-Based Learning Assessments (GBLA) using a variety of educational data mining techniques (EDM). GBLA research examines patterns of behaviors evident in game data logs for the measurement of implicit learning—the development of unarticulated knowledge that is not yet expressible on a test or formal...

Carnegie Learning, developers of the widely deployed Cognitive Tutor, has been working on several new adaptive learning products. In addition to demoing the Cognitive Tutor, an educationally effective intelligent tutoring system for mathematics that has been the subject of a great deal of educational and educational data mining research, we demo two iPad apps, an equation solving app that recog...