This paper focuses on predicting drop-outs and school failures when student data has been enriched with data derived from students social behaviour. These data describe social dependencies gathered from e-mail and discussion board conversations, among other sources. We describe an extraction of new features from both student data and behaviour…

Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…

In this paper we examine the collaborative performance of undergraduate engineering students who used shared project documents (Wikis, Google documents) and a software version control system (SVN) to support project collaboration. We present an initial implementation of TeamAnalytics, an instructional tool that facilitates the analyses of the…

Concept maps, visual representations of knowledge, are used in an educational context as a way to represent students' knowledge, and identify mental models of students; however there is a limitation of using concept mapping due to its difficulty to evaluate the concept maps. A concept map has a complex structure which is composed of concepts and…

This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification.…

This paper proposes a classification via clustering approach to predict the final marks in a university course on the basis of forum data. The objective is twofold: to determine if student participation in the course forum can be a good predictor of the final marks for the course and to examine whether the proposed classification via clustering…

"OperationARIES!" is an Intelligent Tutoring System that teaches scientific inquiry skills in a game-like atmosphere. Students complete three different training modules, each with natural language conversations, in order to acquire deep-level knowledge of 21 core concepts of research methodology (e.g., correlation does not mean causation). The…