Abstract
The current special issue and its companion issue, which is to follow, present the “Best of ITS 2010.” The articles included in these two issues are extended versions of the eight papers ranked highest by the reviewers of the 10th International Conference on Intelligent Tutoring Systems (ITS 2010). We, as Programme co-Chairs for ITS 2010 and the Special Issue editors, are very pleased to say that all authors invited to submit to the special issue responded positively to our request. All of them submitted expanded versions of their conference papers, which then underwent careful peer review for the journal, resulting in further revisions and the articles that you now see. As the Best of ITS 2010, these articles represent an excellent snapshot of where the field of AI in Education (AIED) currently is, and where it is heading. All eight articles in this special issue fall under the broad AIED umbrella. Beyond that simple fact, they defy simple categorizations; they represent diverse technologies and a range of domains. At the same time, we do see important themes and trends, the most striking of which is that all articles involved extensive analysis of data about student student learning with advanced learning technologies. This fact may come as no surprise to those who have followed research in AIED in recent years, but it illustrates vividly that our field is about development of advanced learning technologies while also striving to be an empirical science about how technology can best support and enhance human learning. In line with this overarching theme, many articles in the special issue touch on educational data mining (EDM). In addition, the following themes are represented: student modeling, dialogue analysis and dialogue systems, educational games, hybrid teaching systems, authoring (and automation thereof, using data), and evaluation. We briefly review how the papers exemplify these themes. Educational data mining (EDM). This relatively new area is concerned with developing and applying methods to explore data from educational settings to better understand students and the settings in which they learn (paraphrased from http://www.educationaldatamining.org/). Under this definition, five out of the eight articles in this special issue (and arguably even all eight) touch on EDM. These articles reflect the emergence of increasing amounts of learning data that is ripe for exploitation and, in parallel, a growth of work in new techniques for exploiting that data. Two of these articles present EDM work to develop and refine techniques for student modeling,which has long been a cornerstone of AIED research, fundamental to the goal of personalisation. The article by Baker, Goldstein, and Heffernan frames and tackles an interesting and important problem: is it possible to look at an instructional event (such as a student’s solving a problem step in an intelligent tutoring system) and at the very moment that the event happens predict how much learning it produces? Baker et al. addressed this
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