Fernando A. Mikic Fonte

An intelligent tutoring module controlled by BDI agents for an e-learning platform

INES (INtelligent Educational System) is an operative prototype of an e-learning platform. This platform includes several tools and technologies, such as: (i) semantic management of users and contents; (ii) conversational agents to communicate with students in natural language; (iii) BDI-based (Believes, Desires, Intentions) agents, which shape the tutoring module of the system; (iv) an inference engine; and (v) ontologies, to semantically model the users, their activities, and the learning contents. The main contribution of this paper is the intelligent tutoring module of the system. Briefly, the tasks of this module are to recognize each student (checking his/her system credentials) and to obtain information about his/her learning progress. So, it can be able to suggest to each student specific tasks to achieve his/her particular learning objectives, based on several parameters related to the existing learning paths and the students profile.

Using semantics in INES, an Intelligent Educational System

INES (INtelligent Educational System) is a functional prototype of an online learning platform, which combines three essential capabilities related to e-learning activities. These capabilities are those related to Learning Management Systems, Learning Content Management Systems, and Intelligent Tutoring Systems. To carry out all these functionalities, our system, as a whole, comprises a set of different tools and technologies, as follows: an intelligent chatterbot which is able to communicate with students in natural language, an intelligent agent based on BDI (Believes, Desires, Intentions) technology which acts as the brain of the system, an inference engine based on JESS (a rule engine for the Java platform), and an ontology. At the present paper we will focus on this ontology, which is used in the platform to provide semantic support for system users (administrators, teachers, and students), their activities, and the learning contents. We will specifically address the architecture and performance of the ontology, and their contribution to INES.

NLAST: A natural language assistant for students

This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.

Improving the communication with the students within the assessment stage using BeA

ICTs (Information and Communication Technologies) have been used for supporting the learning process (for both distance and in-person learning) over the last years. A key part of the in-person learning process is the assessment of the students through written exams. Several years ago, BeA (Blended e-Assessment) system has been used for addressing this kind of assessment process. BeA supports the whole process of assessing through traditional written exams using ICTs. This paper introduces the facilities available in BeA to allow student-professor communication. These facilities allow the students to know at every time the state of their exams, to access the contents of those exams and the professors reviews, and to communicate directly with the professor to solve any issue.

A Tagging Recommender Service for Mobile Terminals

This paper introduces more Tourism, a hybrid recommendation platform that provides information about tourist resources depending on the user profile, location, schedule and the amount of time for visiting interest points isolated or combined in a route. This platform is enriched with several services, such as: mashups, socialization, and adaptive interfaces in order to enrich the users experience when visiting touristic attractions. The system is able to find touristic resources taking into account users’ likes, through the use of hybrid Content-based Filtering + Collaborative Filtering techniques combined with tagging and folksonomies. To our knowledge, this is the first recommendation service oriented to mobile terminals that use extensively the advantages of Web 2.0 for social collaborative tagging.

Hacia la creación de una clase invertida de Arquitectura de Ordenadores

Los materiales audiovisuales se estan usando cada vez mas en la docencia. Estos materiales a su vez son la base que permite invertir la clase: explicar en videos en casa lo que antes se explicaba en clase, y hacer en clase lo que antes el estudiante hacia en casa, que basicamente eran ejercicios y problemas. El presente articulo muestra la propuesta de una clase inversa de Arquitectura de Ordenadores de un grado de Ingenieria de Tecnologias de Telecomunicacion en la Universidad de Vigo (Espana), partiendo de experiencias previas de grabacion de clases e inversion parcial de alguna de ellas. Se describen los pasos seguidos y las caracteristicas particulares de esta propuesta. La experiencia en la realizacion de esta propuesta puede ser aplicada a otras asignaturas. Keywords—Flipped learning; recorded lectures; flipped classroom.