Adaptive course generation through learning styles representation

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student’s knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student’s preferences and learning performances. Learning material is chosen by the system matching the student’s learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed.     

Enhancing cultural recommendations through social and linked open data

In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user’s current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

     

Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System

A case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTA^TM. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web.     

A web-based training system for business letter writing

As with the growing degree of office automation and diffuse use of electronic media, such as e-mails, written business communication is becoming a key element to promote synergies, relationships and disseminating information about products and services. Task recognition and the definition of strategies and suitable vocabularies are some of the activities that office workers deal with each time a communicative intent has to be effectively transferred and understood by a given addressee. This paper introduces a web-based intelligent training system based on the constructivism theory and self-directed learning paradigms for assisting company workers in the drafting business letters-writing task. A case-based engine suggests ad hoc rhetorical letters that users have the chance to adapt to their particular contexts and save them into user-defined case libraries.     

Conversation rebuilding: From the foreign language classroom to implementation in an intelligent tutoring system

This paper shows how an innovative “communicative” technique in teaching foreign languages—Conversation Rebuilding (CR)—readily lends itself to implementation in an Intelligent Tutoring System (ITS). Classroom language teachers using CR get students to formulate acceptable utterances in a foreign idiom by starting from rough approximations (using words the students know) and gradually zeroing in on the utterance which a native speaker of that idiom might produce in a similar setting. The ITS presented here helps students do the “zeroing in” optimally. It lets them express themselves temporarily in an “interlingua” (i.e., in their own kind of French or English or whatever they are studying), as long as they make something of their communicative intent clear, that is, as long as the System can find a semantic starting point on which to build. The ITS then prods the students to express themselves more intelligibly, starting from the “key” elements (determined by a heuristic based on how expert classroom teachers proceed) and taking into consideration the students' past successful or unsuccessful attempts at communication. To simplify system design and programming, however, conversations are “constrained”: students playact characters in set dialogs and aim at coming up with what the characters actually say (not what they could possibly say). While most Intelligent Computer Assisted Language Learning (ICALL) focuses the attention of students on norms to acquire, the ICALL implementation of CR presented in this paper focuses the attention of students on saying something—indeed, almost anything—to keep the conversation going and get some kind of meaning across to the other party. It sees successful language acquisition primarily as the association of forms with intent, not simply as the conditioning of appropriate reflexes or the elaboration/recall of conceptualized rules (which are the by-products of successful communication). Thus, in espousing this hard-line communicative approach, the present paper makes a first, non-trivial point: ICALL researchers might usefully begin by investigating what the more able teachers are doing in the classroom, rather than by building elaborate computer simulations of out-dated practices, as happens all too often. The paper then goes on to describe the architecture of a prototype ITS based on CR—one that the authors have actually implemented and tested—for the acquisition of English as a foreign language. A sample learning session is transcribed to illustrate the man-machine interaction. Concluding remarks show how the present-day limits of ICALL (and Artificial Intelligence in general) can be partially circumvented by the strategy implemented in the program, i.e. by making the students feel they are creatively piloting an interaction rather than being tested by an unimaginative machine.