Abstract:In previous work [10], we reported on an experiment performed in the context of SQL-Tutor, in which we analysed students' self-assessment skills. This preliminary study revealed that more able students were better in assessing their knowledge. Here we report on a new study performed on the same system. This time, we analysed the effect of an open student model on students' learning and self-assessment skills. Although we have not seen any significant difference in the post-test scores of the control and the experimental group, the less able students from the experimental group have scored significantly higher than the less able students from the control group. The more able students who had access to their models abandoned significantly less problems the control group. These are encouraging results for a very simple open model used in the study, and we believe that a more elaborate model would be more effective.

We investigate the combination of Augmented Reality (AR) with Intelligent Tutoring Systems (ITS) to assist with training for manual assembly tasks. Our approach combines AR graphics with adaptive guidance from the ITS to provide a more effective learning experience. We have developed a modular software framework for intelligent AR training systems, and a prototype based on this framework that teaches novice users how to assemble a computer motherboard. An evaluation found that our intelligent AR system improved test scores by 25 % and that task performance was 30 % faster compared to the same AR training system without intelligent support. We conclude that using an intelligent AR tutor can significantly improve learning compared to more traditional AR training.

We present COLLECT-UML, a constraint-based intelligent tutoring system (ITS) that teaches object-oriented analysis and design using Unified Modelling Language (UML). UML is easily the most popular object-oriented modelling technology in current practice. While teaching how to design UML class diagrams, COLLECT-UML also provides feedback on collaboration. Being one of constraint-based tutors, COLLECT-UML represents the domain knowledge as a set of constraints. However, it is the first system to also represent a higher-level skill such as collaboration using the same formalism. We started by developing a single-user ITS that supported students in learning UML class diagrams. The system was evaluated in a real classroom, and the results showed that students' performance increased significantly. In this paper, we present our experiences in extending the system to provide support for collaboration as well as domain-level support. We describe the architecture, interface and support for collaboration in the new, multi-user system. The effectiveness of the system has been evaluated in two studies. In addition to improved problem-solving skills, the participants both acquired declarative knowledge about effective collaboration and did collaborate more effectively. The participants have enjoyed working with the system and found it a valuable asset to their learning.

Fifteen years ago, research started on SQL-Tutor, the first constraintbased tutor. The initial efforts were focused on evaluating Constraint-Based Modeling (CBM), its effectiveness and applicability to various instructional domains. Since then, we extended CBM in a number of ways, and developed many constraint-based tutors. Our tutors teach both well-and ill-defined domains and tasks, and deal with domain-and meta-level skills. We have supported mainly individual learning, but also the acquisition of collaborative skills. Authoring support for constraint-based tutors is now available, as well as mature, well-tested deployment environments. Our current research focuses on building affect-sensitive and motivational tutors. Over the period of fifteen years, CBM has progressed from a theoretical idea to a mature, reliable and effective methodology for developing effective tutors.

Abstract. Personalised environments such as adaptive educational systems can be evaluated and compared using performance curves. Such summative studies are useful for determining whether or not new modifications enhance or degrade performance. Performance curves also have the potential to be utilised in formative studies that can shape adaptive model design at a much finer level of granularity. We describe the use of learning curves for evaluating personalised educational systems and outline some of the potential pitfalls and how they may be overcome. We then describe three studies in which we demonstrate how learning curves can be used to drive changes in the user model. First, we show how using learning curves for subsets of the domain model can yield insight into the appropriateness of the model's structure. In the second study we use this method to experiment with model granularity. Finally, we use learning curves to analyse a large volume of user data to explore the feasibility of using them as a reliable method for finetuning a system's model. The results of these experiments demonstrate the successful use of performance curves in formative studies of adaptive educational systems.

Abstract. Student modeling (SM) is recognized as one of the central problems in the area of Intelligent Tutoring Systems. Numerous SM approaches have been proposed and used with more or less success. Constraint-based modeling is new approach, which has been used successfully in three tutors developed in our group. The approach is extremely efficient, and it overcomes many problems that other student modelling approaches suffer from. We present the advantages of CBM over other similar approaches, describe three constraint-based tutors and present our future research plans.

Intelligent Tutoring Systems (ITS) have revolutionized online education by providing individualized instruction tailored towards each student. Constraint-based tutors model instructional domains at an abstract level, a novel approach that simplifies the development of ITSs. We have developed many effective constraint-based tutors over the last decade in a number of instructional domains of various characteristics, some of which have been successfully commercialized. Constraint-based tutoring is now a mature and successful approach to providing adaptive learning environments. Our authoring tools aim to make this technology widely available to teachers and students everywhere.

Numerous approaches to student modeling have been proposed since the inception of the field more than three decades ago. hat the field is lacking completely is comparative analyses of different student modeling approaches. Such analyses are sorely needed, as they can identify the most promising approaches and provide guidelines for future research. In this paper we compare Cognitive Tutoring to Constraint-Based Modeling (CBM). We present our experiences in implementing a database design tutor using both methodologies and highlight their strengths and weaknesses. We compare their characteristics and argue the differences are often more apparent than real. For specific domains, one approach may be favoured over the other, making them viable complementary methods for supporting learning.