In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. We present a novel application of the q-matrix method, an educational data mining technique, to the problem of analyzing formal proofs. Our results indicate that automated analysis of formal proof data can provide an intelligent tutoring system with us...

Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used to compute latencies of actions. On the other han...

A key issue in using machine learning to classify tutorial dialogues is how to represent time-varying data. Standard classifiers take as input a feature vector and output its predicted label. It is possible to formulate tutorial dialogue classification problems in this way. However, a feature vector representation requires mapping a dialogue onto a fixed number of features, and does not innatel...

One of the main objectives of Indian educational system is evaluating or enhancing the educational organization. Data Mining (DM) is the process of searching the concealed information from a large quantity of data set. It analyzes the data from different source and it converts into meaningful information. There are a lot of advantages of data mining technique in education sector. Utilization of...

While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attentio...

Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, th...

The landscape of online learning has evolved in a synchronous fashion with the development of the every-growing repertoire of technologies, especially with the recent addition of Massive Online Open Courses (MOOCs). Since MOOC platforms allow thousands of students to participate at the same time, MOOC participants can have fairly varied motivation. Meanwhile, a low course completion rate has be...

The field of educational data mining has been paying attention to Knowledge Tracing (KT) for a long time. Corbett and Anderson assumed the amount of learning that students do does not depend on whether students get items right or wrong. Ohlsson and others argued that the student should learn more from a previous incorrect performance. We decided to investigate a Bayes Network similar to KT but ...

Educational Data Mining researchers use various prediction metrics for model selection. Often the improvements one model makes over another, while statistically reliable, seem small. The field has been lacking a metric that informs us on how much practical impact a model improvement may have on student learning efficiency and outcomes. We propose a metric that indicates how much wasted practice...

For the improvement and evaluation of learning management systems as well as concepts and tools for personal history, the availability of suitable data has become increasingly important. In this paper we discuss considerations for creating such a data set and report on our experiences while recruiting contributions for the Web History Repository project (WHR). We focus on measures and technique...