The 5th International Conference on Educational Data Mining (EDM 2012) is held in picturesque Chania on the beautiful Crete island in Greece, under the auspices of the International Educational Data Mining Society (IEDMS). The EDM 2012 conference is a leading international forum for high quality research that mines large data sets of educational…

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…

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual's parameters. We examine if this variation is important in terms of instructional…

In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…

In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in…

Identifying the skills that determine the success or failure to exercises and question items is a difficult task. Multiple skills may be involved at various degree of importance, and skills may overlap and correlate. In an effort towards the goal of finding the skills behind a set of items, we investigate two techniques to determine the number of…

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…

The interaction behaviours of successful, high-achieving learners when using a Learning Management System (LMS) are different than the behaviours of learners who are having more difficulty mastering the course material. This paper explores the idea that conventional Learning Management Systems can exploit data mining techniques to predict the…

In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve student learning. However, there remain challenges to overcome for these methods to become available to…

Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students' success in computer-based learning environments. Consequently, understanding students' self-regulated learning behavior has been the subject of increasing interest. Unfortunately, monitoring these behaviors in real-time has proven…

In this paper we combine a logistic regression student model with an exercise selection procedure. As opposed to the body of prior work on strategies for selecting practice opportunities, we are working on an assumption of a finite amount of opportunities to teach the student. Our goal is to prescribe activities that would maximize the amount…

In recent years, the educational data mining and user modeling communities have been aggressively introducing models for predicting student performance on external measures such as standardized tests as well as within-tutor performance. While these models have brought statistically reliable improvement to performance prediction, the real world…