Most of the emphasis on mining online assessment logs has been to identify contentspecific errors. However, the pattern of general “consistency” is domain independent, strongly related to performance, and can itself be a target of educational data mining. We demonstrate that simple consistency indicators are related to student outcomes, and suggest how consistency might be used in an online ass...

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...

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning tasks such as recommending resources (e.g. papers, books,..) to the learners (students). In this work, we propose a novel approach which uses recommender system techniques for educational data mining, especially for predicting student performance. To validate this approach,...

Student modeling is broadly used in educational data mining and intelligent tutoring systems for making scientific discoveries and for guiding instruction. For both of these goals, having high model accuracy is important, and researchers have incorporated a variety of features into student models. However, since different techniques use various features, when evaluating those approaches, we cou...

Every data has a lot of hidden information. The processing method of data decides what type of information data produce. In India education sector has a lot of data that can produce valuable information. This information can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Information and communication...

Technology advances have made the ability to collect large amounts of data easier than ever before. These massive datasets provide both opportunities and challenges for many fields and education is no different. Understanding how to deal with extreme amounts of student data in the EDM field is a growing problem. The 2010 KDD Cup Competition, titled "Educational Data Mining Challenge", included ...

The Web-based Inquiry Science Environment (WISE) logs student and teacher interactions during classroom science inquiry instruction. Over the past 10 years we have used these logs for many purposes including: to analyze patterns of student interactions with dynamic, interactive scientific models and improve instruction; to determine when students revisit prior activities and assess whether the ...

Complex Problem Solving (CPS) is a prominent representative of transversal, domain-general skills, empirically connected to a broad range of outcomes and recently included in large-scale assessments such as PISA 2012. Advancements in the assessment of CPS are now calling for a) broader assessment vehicles allowing the whole breadth of the concept to unfold and b) additional efforts with regard ...

Rule evaluation measures play an important role in educational data mining. A lot of measures have been proposed in different fields that try to evaluate features of the rules obtained by different types of mining algorithms for association and classification tasks. This paper describes a framework for helping non-expert users such as instructors analyze rule evaluation measures and define new ...

Employing data mining algorithms on previous student’s records may give important results in defining new ways of learning and during the process of development of a new curriculum for educational institutions. Creating group profiles by analyzing certain attributes of the students helps in defining more specifically the needs of the students. Achieving this requires manipulation of data in a s...