Fernando A. Mikic

CHARLIE: An AIML-based chatterbot which works as an interface among INES and humans

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 concerning to a LMS (Learning Management System), a LCMS (Learning Content Management System), and an ITS (Intelligent Tutoring System). To carry out all this functionalities, our system, as a whole, comprises a set different tools and technologies, as follows: semantic managing users (administrators, teachers, students…) and contents tools, an intelligent chatterbot able to communicate with students in natural language, an intelligent agent based on BDI (Believes, Desires, Intentions) technology that acts as the brain of the system, an inference engine based on JESS (a rule engine for the Java platform) and ontologies (to modelate the user, his/her activities, and the learning contents) that contribute with the semantics of the system, etc. At the present paper we will focus on the chatterbot, CHARLIE (CHAtteR Learning Interface Entity), developed and used in the platform, which is an AIML-based (Artificial Intelligence Markup Language) bot. We will specifically address its performance and its contribution to INES.

Towards a Standard for Mobile E-Learning

The Learning Technology (LT) standardization process is a hot topic today within the e-learning scientific community. This paper introduces the main LT specifications and analyzes them as far as their usability for mobile learning is concerned. Future directions and particular needs for M-learning are also discussed.

Accessibility and Mobile Learning Standardization

Every Web user should have access to the information and experiences available online. Accessibility means that people with disabilities can navigate and interact with the Web. We address accessibility for mobile learning and its implications as far as learning technology (LT) standards is concerned. We consider accessibility not only as the needs and preferences of those users with a disability, but also as the needs and preferences of all users, regardless of the situation or circumstances. This paper reviews the main LT specifications and standards in relation with their suitability for mobile learning. In addition, a new contribution to this LT standardization process is proposed: the inclusion of a device profile in some of these specifications.

T-Bot and Q-Bot: A couple of AIML-based bots for tutoring courses and evaluating students

Intelligent tutoring systems are computer programs that aim at providing personalized instruction to students. In recent years, conversational robots, usually known as chatterbots, become very popular in the Internet, and ALICE (artificial linguistic internet computer entity) is probably the most popular one. ALICE brain is written in AIML (artificial intelligence markup language), an open XML language. We consider the combination of both approaches, i.e, the use of AIML-based bots for tutoring purposes in open e-learning platforms like Claroline or Moodle. With that aim in mind, we have developed two different bots for helping the students during the learning process and for supporting the teaching activities of the professor. One of them is a tutor bot (T-Bot), and is able to analyse the requests made by the learners in written natural language and to provide adequate and domain specific answers orienting the student to the right course contents. The other one is an evaluation bot (Q-Bot), and is oriented to track and supervise the student progress by means of personalized questionnaires. Both bots have been already developed and integrated as user-friendly modules in Claroline and Moodle.

Developing virtual teaching assistants for open e-learning platforms

Integration of Artificial Intelligence (AI) techniques within Learning Management Systems (LMS) represent a little explored field of research. We use this paper to show how these techniques can help students as well as tutors across the learning process within an open source e-learning platform. Especially, we present T-BOT and Q-BOT, a couple of chatter bots capable of tutoring and evaluating students using open platforms as Moodle or Claroline. These bots are developed in Program E, a PHP-base interpreter, and can interoperate with the students through natural language thanks to an AIML brain.

Device Profile (DP): A Way to Achieve Accessibility and Device Independence for Mobile Learning

We address accessibility and device independence for mobile learning and its implications as far as learning technology (LT) standards are concerned. This paper reviews the IMS learner information specifications in relation with their suitability for mobile learning and, in addition, it presents a new contribution to this LT standardization process through the inclusion of the concept of a device profile in some of these specifications.

Applying computational science techniques to support adaptive learning

Adaptive Learning Systems offer customized learning experiences according to the actual student needs and capabilities. Effective student modelling, adequate representation of the knowledge domain and proper characterization of learning tools are key issues to provide high quality Adaptive Learning Systems. Most current systems are based on Artificial Intelligence techniques (e.g. fuzzy logic, neural networks, Bayesian networks, etc.) trying to reproduce human teaching behaviours by using a computational representation of expertise. This paper offers a survey on Adaptive Learning showing how Computational Science techniques are applied to instructional systems and identifying forthcoming trends for the future.

USING TAGS IN AN AIML-BASED CHATTERBOT TO IMPROVE ITS KNOWLEDGE

Nowadays, it is common to find on the Internet different conversational robots which interact with users simulating a natural language conversation. Among them, we can emphasize the chatterbots based on AIML language. In this paper we present an AIML based chatterbot that shows as its main contribution the use of tags and folksonomies. Thanks to its use, we can generate a context for each conversation, being able to maintain a state for each user in the system, and improving the adaptation capabilities of the bot.

Dynamic Personalisation of Media Content

Dynamic personalization of media content is the latest challenge for media content producers and distributors. The idea is to adapt in near real time the content of a video stream to the viewers profile. This concept encompasses any type of context-awareness customisation, expressed preferences and viewer profiling. To achieve this goal we propose a multi tier framework composed of a content production tier, a content distribution tier and a content consumption tier, representing producers, distributors and viewers, plus an artefact brokerage tier, implemented as an agent-based e brokerage platform, to support the dynamic selection of the content to be inserted in the video stream of each viewer.

A case-based peer-to-peer framework for managing student models in Intelligent Tutoring Systems

Intelligent tutoring systems (ITSs) aim at providing personalized and adaptive tutoring to students by the incorporation of a student modeling component. In the near future, a very interesting scenario will appear when multiple tutoring systems exchange information in order to learn from its own experiences and improve their student modeling components. In order to get closer to such scenario, in this paper we present a case-based peer-to-peer multi-agent system for collaborative management of student models in ITSs. The goal of the system is twofold: first, to initialize the student model when a new student logs on the tutor system and second, to update the student model depending on the studentpsilas interaction with the system and exchanging this information with its peers. The quality of the system is evaluated in terms of its ability for searching similar cases (accuracy) tested under three different strategies. Our results show that increasing the system complexity (number of nodes and/or number of students) and using a committee strategy, the performance of the global system is improved by reducing network traffic, and preserving the quality of the solutions for the new students (cases).