Stefano A. Cerri

SERVICES, SEMANTICS, AND STANDARDS: ELEMENTS OF A LEARNING GRID INFRASTRUCTURE

There has been considerable political pressure and much hope invested in the use of communication and information technologies to provide wider access to education, while improving quality and reducing costs. Unfortunately, many of the responses to the challenge of these aspirations have consisted of simple Web technology-driven products, which have failed to progress effective learning. In this paper we outline the characteristics and pedagogical goals of a learning paradigm that is used to drive the technical requirements, rather than being constrained by what is easily achieved in XHTML. We identify and explain the key roles played by services, semantics, and standards in meeting pedagogical goals of novel learning situations, and illustrate with some scenarios that build bridges between traditional learning contexts and future possibilities. Crucially, we explain why we have adopted Grid technologies in the European Learning Grid Infrastructure (ELeGI) research program.

The STROBE Model: Dynamic Service Generation on the Grid

This article presents the STROBE model: both an agent representation and an agent communication, model based on a social approach, which means interaction centered. This model represents how agents may realize the interactive, dynamic generation of services on the Grid. Dynamically generated services embody a new concept of service implying a collaborative creation of knowledge, i.e., learning; services are constructed interactively between agents depending on a conversation. The approach consists of integrating selected features from multi-agent systems and agent communication, language interpretation in applicative/functional programming and e-learning/human-learning into a unique, original, and simple view that privileges interactions, including control. The main characteristic of STROBE agents is that they develop a language (environment + interpreter) for each of their interlocutors. The model is inscribed within a global approach, defending a shift from the classical algorithmic (control based) view to problem solving in computing to an interaction-based view of social informatics, where artificial as well as human agents operate by communicating as well as by computing. The paper shows how the model may not only account for the classical communicating agent approaches, but also represent a fundamental advance in modeling societies of agents in particular in dynamic service generation scenarios such as those necessary today on the Web and proposed tomorrow for the Grid. Preliminary concrete experimentations illustrate the potential of the model; they are significant examples for a very wide class of computational and learning situations.

Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes

In this paper, we propose a game adaptation technique that seeks to improve the training outcomes of stroke patients during a therapeutic session. This technique involves the generation of customized game levels, which difficulty is dynamically adjusted to the patients’ abilities and performance. Our goal was to evaluate the effect of this adaptation strategy on the training outcomes of post-stroke patients during a therapeutic session. We hypothesized that a dynamic difficulty adaptation strategy would have a more positive effect on the training outcomes of patients than two control strategies, incremental difficulty adaptation and random difficulty adaptation. To test these strategies, we developed three versions of PRehab, a serious game for upper-limb rehabilitation. Seven stroke patients and three therapists participated in the experiment, and played all three versions of the game on a graphics tablet. The results of the experiment show that our dynamic adaptation technique increases movement amplitude during a therapeutic session. This finding may serve as a basis to improve patient recovery.

On the Social Rational Mirror: Learning E-commerce in a Web-Served Learning Environment

Recent work in our teams focused on a methodology, called Phi - calculus, the aim of which is to study the process of interaction between a human - or a group - and a machine when the former is interested on using certain capabilities of the latter in order to improve the task of explicitating knowledge in a coherent and unambiguous fashion. Our more recent work consisted on examining how Phi-calculus might be instantiated in the context of Human Education. The result is a Web-served Learning Environment, called PhiInEd, to assist both the planning and the execution phases of a course. PhiInEd has been used by a class of D.E.A. on Business Contracts.

Agent-Grid integration ontology

The integration of GRID and MAS (Multi-Agents Systems) is an active research topic We have recently proposed the Agent-Grid Integration Language, to describe a service-based integration of GRID and MAS models However, the complexity of the mutual integration aspects leads us to define a rigorous way to formalize the key concepts, their relations and the integration rules by means of an ontology With this ontology, we can describe the elements and their composition that occur in various service exchange scenarios with agent on the Grid The ontology could be used both to model the behaviour of GRID-MAS integrated systems and to check the consistency of these systems and their instances A concrete scenario is illustrated.

Ontology-Centered Personalized Presentation of Knowledge Extracted from the Web

The paper presents an approach for the dynamic generation of a complex structure of personalized web pages for learning purposes, reflecting the ontology of the considered domain. The need of assuring a holistic character for the body of knowledge induced in the learners mind is emphasized. This is very important in the learning processes, especially nowadays, in the context of the huge amount of information available on the web and of its permanent evolution. The approach permits the adaptation of the content of the generated web pages to the incoming information from the web. New information is extracted, annotated and coherently integrated in the body of knowledge in order to keep the holistic character of the body of knowledge. Personalization is achieved by filtering the semantic network according to the learner model, which keeps the list of concepts known or unknown by the learner. The approach was used in an EU INCO Copernicus project for computer aided language learning.

Steps towards C+C: A Language for Interactions

We present in this paper our reflections about the requirements of new architectures and languages for the Web, confronted with the ones emerging from qualified scientists such as Mc Carthy [1] and Wegner [2]. The contribution highlights if and how these reflections may be concretely realized by means of extensions of non standard models and tools that we have already experimented and that appeared in previous papers (the STROBE model and Phi Calculus). We conclude with the preliminary specifications of a new language for modeling and programming Interactions, called C+C, that represents constructively our approach, privileging the communicational aspects among Autonomous Agents, with respect to the more traditional algorithmic ones.

DIAL: serendipitous DIAlectic Learning

Killer applications, such as Excel and others, may be retrospectively considered as potential excellent learning environments in different specific domains and also in meta-cognitive skills,even if there is no learning intention either in the designer or the user. We briefly present a patented methodology for knowledge acquisition and construction, which has been widely and successfully used for five years, and interpreted as a learning environment for users engaged in domain-dependent interactions.

Concurrent, asynchronous search for the availability of knowledge

We describe how a common conceptual model of the application domain can be used to support a cooperative and concurrent search among loosely coupled knowledge sources distributed over a network with unknown topology. The introduction of a conceptual level, shared among all the knowledge sources in the net, allows us to obtain independence from the logical data organization of each node, not only from the physical, as is the case in distributed database management systems (DDBMS). The navigation process is based on the conceptual model and has been designed and developed by means of a set of cooperative agents, with specific knowledge and abilities, that reason about the local data and exchange information to ensure both the communication through the net and the information processing at each net node. In order to ensure the comprehension of the architecture, we include a worked-out example in the domain of pharmacology, where such a model and its associated domain-specific language have been realized.

A contradiction-driven approach to theory formation: conceptual issues, pragmatics in human learning, potentialities

In Educational literature, Discovery Learning appears as an approach in which the learner builds up his/her own knowledge by performing experiments within a domain and inferring/increasing rules as a result. Such a constructivist approach has been largely exploited in the design of computational artefacts with learning purposes, the so-called Discovery Learning Environments (DLEs). One known feature of such environments is the autonomy degree required for students to succeed while handling a domain. Additionally, DLEs designers are often challenged to get students actually engaged. Such questions are on the basis of our concerns with the design and usage of particular DLEs, within which learning events occur as a consequence of contradiction detection and overcoming, during human/machine cooperative work. In this paper, we present an artificial agent capable of handling such a contradiction-driven approach of learning, by highlighting the exchanges that the agent should promote with a human learner. The conceptual model supporting the agents design relies on the scientific rationale, particularly the empirical approach guided by the theory-experiment confrontation. We shall reinforce the interest of the model for the design of DLEs by presenting its exploitation in a real learning situation in Law. Also, we suggest potential instantiations of the model elsewhere than in Human Learning.