1,721,047 research outputs found
Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System
No full textA 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 VISTATM. 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
K-OpenAnswer: a simulation environment to analyze the dynamics of massive open online courses in smart cities
No full textThe smartness of a city is given by the technologies it put to use, and more than that, by the people empowered by such technologies; it is worth thinking about how people can be trained to be empowered by smart technologies, and how cities can become “educational.” So, while sustainability and technology solutions for smart cities are strategic challenges, one of these is surely distance education and training. In this field, the Web offers many opportunities, such as the e-learning platforms where students can learn, according to their own needs and pace. The massive open online courses (MOOCs) are particular distance learning platforms, generally offering, so far, free courses on a huge amount of topics, and characterized by a (potentially) very high number of enrollments. In a MOOC, a teacher, or tutor, has a hard life when trying to follow and manage with the learning processes of thousands of students. In particular, assessment can be managed almost exclusively by letting the student answer questions in closed answers tests. This strategy has some didactic limits, while a valid alternative is to use peer assessment (PA) over more articulated assessment activities (e.g., open-ended questions). PA makes students grade their peers’ answers, and provides learners with significant advantages, such as refining their knowledge of the subject matter, and developing their meta-cognitive skills. In this work, we present a software platform called K-OpenAnswer, which helps teachers to simulate the dynamic of a MOOC where PA is used. The system uses a machine learning technique, based on a modified version of the K-NN algorithm, and provides teachers with a statistical environment by which they can monitor the evolving dynamic of a simulated MOOC, according to the techniques we use to implement PA. An experimental evaluation is presented that highlights the advantages of using the system as a valid tool for the study of real MOOCs
Monitoring Massive Open Online Courses (MOOC) During the Covid-19 Pandemic
No full textThe last two years have been characterized by an exponential growth in the use of the Internet as a working and learning tool, due to the “Covid-19” pandemic. The increase in smart working and distance learning have been some of the most striking effects. Cities have become more eco-sustainable: less pollution and better life quality. In this work we present a brief review of some data science platforms, which are useful to monitor the learning processes that take place in Massive Open Online Courses. Through these tools, teachers could find out useful information about the learning processes, by extracting it from the (big) data produced by students’ activities and stored in log files. To show the usefulness of such tools, we propose a very simple case study, showing how to extract strategic information from the log database produced by a course delivered via Moodle platform. The results of this case study strengthen our hypothesis on the utility of a data science approach for monitoring learning processes, especially in MOOCs
A Hybrid Architecture for the Recognition of User Interests during Hypermedia Navigation
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TutorChat: a Chatbot for the Support to Dyslexic Learner’s activity through Generative AI
No full textWe present TutorChat, an intelligent chatbot conceived to be able support search and synthesis of information during a learning task accomplishment, in particular for dyslexic students. TutorChat is based on ChatGPT; it is able to support question/answer inter-activity of learners, and to generate concept maps on the topics at hand, with the possibility, beside analysis, to have such maps extended with additional sub-maps starting from a selected concept. We let TutorChat be used by a sample of dyslexic learners, coming from different educational levels. Then we collected encouraging sample’s feedback, through a questionnaire, about appreciation of the system’s services, and perception of the usefulness coming from its use
Personalized e-learning in Moodle: the Moodle_LS System
No full textLearning Management Systems are among the most popular e-learning tools.
Over the last few years, however, scientific research has made considerable progress in developing valuable resources currently unavailable in most Learning Management Systems, including solutions aimed at providing students with personalized support throughout the learning process, which is an essential requirement in continuing education. Observing and modelling the learner, and adapting their learning experience accordingly means opening up new technological and, above all, methodological perspectives in e-learning. The work described in this paper is part of the Open Learning project, in which business-based and university researchers aim to combine the most frequently used e-learning technologies, Learning Management Systems, with the benefits of customized systems so as to develop an innovative learning content delivery system based on the personalization of the learning experience. The proposed system integrates moodle with an engine, LS-Plan, which provides automated sequencing of the learning material based on the learner’s knowledge and learning styles. This paper describes the new system and presents the results of tests conducted in the domain of Italian Neorealist Cinema
A Web-based Training System for Business Letter Writing
No full textAs 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
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