Abstract. In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing interaction patterns who also show similar learning improvements with an ELE. We then discuss how a k-means on-line classifier, trained with the learner groups detected off-line, can be used for adaptive support in ELEs. We aim to show the value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs. 1

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Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the expert-elicited knowledge used in the user model is application and domain specific, the entire model development process must be repeated for each new application. In this thesis, we outline a data-based user modeling framework that uses both unsupervised and supervised machine learning in order to reduce the development costs of building user models, and facilitate transferability. We apply the framework to build user models of student interaction with two different learning environments (the CIspace Constraint Satisfaction Problem Applet for demonstrating an Artificial Intelligence algorithm, and the Adaptive Coach for Exploration for mathematical functions), and using two different data sources (logged interface and eye-tracking data). Although these two experiments are limited by the fact that we do not have large data sets, our results provide initial evidence that (i) the framework can automatically identify meaningful student interaction behaviors, and (ii) the user models built via the framework can recognize new student behaviors online. In addition, the similar results obtained from both of our experiments show framework transferability across applications and data types. iii

AIspace is a project that has been providing interactive tools for teaching and learning basic concepts in Artificial Intelligence for several years. In this paper we give an overview of the history and current state of the project. We introduce new functionality which has been added to the tools and allows them to be customized for various applications such as presentations, online tutorials, or assignments. We also share our experience on how the AIspace tools have been successfully integrated into undergraduate AI courses at the University of British Columbia. Finally, we provide preliminary results of fielded evaluations of AIspace usage in these courses.

There are inherent challenges in teaching and learning Artificial Intelligence (AI) due to the complex dynamics of the many fundamental AI concepts and algorithms. Interactive visualization tools have the potential to overcome these challenges. However, there are reservations towards adopting interactive visualizations due to mixed results on their pedagogical effectiveness. Previous work has also often failed to directly assess student preferences and motivation. CIspace is a set of nine interactive visualization tools demonstrating fundamental principles in AI. The CIspace tools are currently in use in undergraduate and graduate classrooms at the University of British Columbia and around the world. In this paper, we present two experiments aimed at assessing the effectiveness of one the tools in terms of knowledge gain and user preference. Our results provide evidence that the tool is as effective as a traditionally accepted form of learning in terms of knowledge gain, and that students significantly prefer to use the tools over traditional forms of study. These results strengthen the case for the incorporation of CIspace, and other interactive visualizations, into courses.

In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data). Despite limitations due to the size of our datasets, we provide initial evidence that the framework can automatically identify meaningful student interaction behaviors and can be used to build user models for the online classification of new student behaviors online. We also show framework transferability across applications and data types.

Abstract—Formative usability evaluation aims at finding usability problems during the development process. The earlier these problems are identified, the less expensive to fix they are. This paper presents some preliminary results from a formative usability testing of the 1st prototype developed for the ARiSE (Augmented Reality in School Environments) project. Keywords—AR-based educational systems, formative evaluation, usability evaluation, user testing. I.