An intelligent semantic agent for supervising chat rooms in e-learning system

[[abstract]]This paper presents an English chat room system in which students can discuss course by interacting with teachers and students. First, the novel mechanism provides learning angel agent and semantic agent that acts as supervisors constantly online to handle queries. Next, the mechanism also provides a QA subsystem that acts as assistant. The learning angel can detect syntax errors written by the online users. The semantic agent can check the semantic of each sentence. Sometimes learners may make semantic level mistakes. This implies that they don't understand the course topic. The semantic agent can thus give some correction suggestions to users and analyze the data in the learner corpus. Moreover, when users query the system, the system attempts to find the answer from the knowledge ontology or learner corpus. Besides, if sufficient number of QA pairs has been accumulated, the FAQ can act as a powerful learning tool for the learners.[[conferencetype]]國際[[conferencedate]]20050606~20050610[[booktype]]紙本[[conferencelocation]]Columbus, OH, US

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

Dialogic possibilities of online supervision

When schools locked down owing to the spread of COVID-19, Danish upper secondary school students worked on the major written assignment that completes their studies. This assignment is interdisciplinary, and students receive up to twenty hours of supervision from two teachers. This year, supervision was reorganised into a virtual format. This article explores how and in what ways students benefited from this reorganisation. This article is based on a mixed-methods design that includes quantitative and qualitative data and investigates how various online supervision formats support dialogic interaction. This article focuses on the student’s experience of supervision. It finds that all the formats we investigated offer the opportunity for dialogue during supervision, but their potential varies significantly. Some formats seem to have great potential for supporting students’ academic development, whereas others support their psychosocial development. We conclude by addressing the importance of choosing the online format suited to a given purpose and recommend that supervisors be aware of the didactic purposes of the various formats

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

New platform for intelligent context-based distributed information fusion

Tesis por compendio de publicaciones[ES]Durante las últimas décadas, las redes de sensores se han vuelto cada vez más importantes y hoy en día están presentes en prácticamente todos los sectores de nuestra sociedad. Su gran capacidad para adquirir datos y actuar sobre el entorno, puede facilitar la construcción de sistemas sensibles al contexto, que permitan un análisis detallado y flexible de los procesos que ocurren y los servicios que se pueden proporcionar a los usuarios. Esta tesis doctoral se presenta en el formato de “Compendio de Artículos”, de tal forma que las principales características de la arquitectura multi-agente distribuida propuesta para facilitar la interconexión de redes de sensores se presentan en tres artículos bien diferenciados. Se ha planteado una arquitectura modular y ligera para dispositivos limitados computacionalmente, diseñando un mecanismo de comunicación flexible que permite la interacción entre diferentes agentes embebidos, desplegados en dispositivos de tamaño reducido. Se propone un nuevo modelo de agente embebido, como mecanismo de extensión para la plataforma PANGEA. Además, se diseña un nuevo modelo de organización virtual de agentes especializada en la fusión de información. De esta forma, los agentes inteligentes tienen en cuenta las características de las organizaciones existentes en el entorno a la hora de proporcionar servicios. El modelo de fusión de información presenta una arquitectura claramente diferenciada en 4 niveles, siendo capaz de obtener la información proporcionada por las redes de sensores (capas inferiores) para ser integrada con organizaciones virtuales de agentes (capas superiores). El filtrado de señales, minería de datos, sistemas de razonamiento basados en casos y otras técnicas de Inteligencia Artificial han sido aplicadas para la consecución exitosa de esta investigación. Una de las principales innovaciones que pretendo con mi estudio, es investigar acerca de nuevos mecanismos que permitan la adición dinámica de redes de sensores combinando diferentes tecnologías con el propósito final de exponer un conjunto de servicios de usuario de forma distribuida. En este sentido, se propondrá una arquitectura multiagente basada en organizaciones virtuales que gestione de forma autónoma la infraestructura subyacente constituida por el hardware y los diferentes sensores