Catálogo de publicaciones - libros

In mathematical learning, it is important to give learners a number of problems that have various features in both surface problem situations and deep mathematical solution structures. In this study, we implement a system that generates various word problems by using episodes, which are knowledge regarded as cases of problem generation. Our system interacts with a teacher as a user to acquire the common knowledge needed to generate word problems. We performed experimental evaluations to verify problem generation by our system, with the results indicating that our system can successfully expand the variety of problems from the initial ones stored in the system. We also found that our system needs interactions with a knowledgeable user because novice users cannot necessarily provide the system with effective knowledge.

Formal models can be found in different computer science domains – they have the advantage to be independent of application domains and of programming languages. ITSs development is usually not based on formal models. Based on automaton theory and on formal descriptions known from modelling and simulation, the formal tutoring process model (tpm) is a formal model for ITSs. The model exists as basic tpm and as adaptive tpm. The extension of the adaptive model is described in the paper. Extended with a temporal dimension, i.e. the ’counter’, the static tpm can be used to realize another way of adaptation: the training case can be changed at runtime based on the counter values. This value can count the learner’s steps in the training case, it can be interpreted as duration, or as validity of a state.

We describe a portable coaching environment used within a domain-independent inquiry-learning infrastructure. This coach reasons about a student’s knowledge and offers pertinent, domain-specific feedback. It promotes good inquiry behavior by critiquing the student’s hypotheses and supporting data and relationships among propositions. Four inquiry tutors in separate disciplines have been developed that use embedded expert knowledge bases and reusable domain-independent rules. We describe the functionality of the coach within an art history domain, discuss the implementation of the coach, and elaborate on the options given to domain authors for customization.

Striving in the real world is more and more what artificial agents are required to do, and it is not a simple task. Interacting with humans in general, and with students in specific, requires an awful lot of subtlety if one is to be perceived as a great tutor and a pleasant fellow. Similarly, the more various types of information an artificial agent senses, the more apt it may be. But then comes the need to process all this stuff, and that can overwhelm even the most powerful computer. «Consciousness» mechanisms can help and sustain an apt tutor, allowing it to consider various sources of information in diagnosing and guiding learners. We show in the present paper how they effectively support theses processes in the specific context of astronauts training on the manipulation of the Space Station Robotic Manipulation System, Canadarm2.

A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models.

COLLECT-UML is a constraint-based ITS that teaches object-oriented design using Unified Modelling Language (UML). UMLis easily the most popular object-oriented modelling technology in current practice. 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 show that students’ performance increased significantly. In this paper, we present our experiences in extending the system to provide support for collaboration. We present the architecture, interface and support for collaboration in the new, multi-user system. A full evaluation study has been planned, the goal of which is to evaluate the effect of using the system on students’ learning and collaboration.

The integration of practice and learning is a key to cultivation of organizational capability for creating or inheriting intellect. In this paper, firstly we address the critical research issues for collaborative space design to integrate practice and learning. Following the discussion, we have built an ontology which specifies the structure of the collaborative learning, described patterns of a collaborative space by reference to the learning theories and developed an intelligent function to support a collaborative space design on ontology-based KM environment, , as a foundation for supporting collaborative space design.

We have created a set of novel visualisations of group activity: they mirror activity of individuals and their interactions, based upon readily available authentic data. We evaluated these visualisations in the context of a semester long software development project course. We give a theoretical analysis of the design of our visualizations using the framework from the “Big 5” theory of team work as well as a qualitative study of the visualisations and the students’ reflective reports. We conclude that these visualisations provide a powerful and valuable mirroring role with potential, when well used, to help groups learn to improve their effectiveness.

Cognitive tutors have been shown to increase student learning in long-term classroom studies but would become even more effective if they provided collaborative support and metacognitive tutoring. Reconceptualizing an established tutoring system as a research platform to test different collaborative and metacognitive interventions would lead to gains in learning research. In this paper, we define a component-based architecture for such a platform, drawing from previous theoretical frameworks for tutoring systems. We then describe two practical implementation challenges not typically addressed by these frameworks. We detail our efforts to extend a cognitive tutor and evaluate our progress in terms of flexibility, control, and practicality.

Heterogeneity in learning groups is said to improve academic performance. But only few collaborative online systems consider the formation of heterogeneous groups. In this paper we propose a mathematical approach to form heterogeneous groups based on personality traits and the performance of students. We also present a tool that implements this mathematical approach, using an Ant Colony Optimization algorithm in order to maximize the heterogeneity of formed groups. Experiments show that the algorithm delivers stable solutions which are close to the optimum for different datasets of 100 students. An experiment with 512 students was also performed demonstrating the scalability of the algorithm.