Intelligent mooc for the disaster resilience dprof programme

The CADRE Project offers Intelligent MOOC for the disaster resilience DPROF programme (MOOC-DPROF). MOOC-DPROF aims at unlimited participation and open access via the Virtual Environment for the Built Environment Research to reduce knowledge shortfalls across the EU. PhD students registered in MOOC-DPROF differ by their knowledge levels, preferences, interests, goals, cognitive styles and learning styles. The basis of MOOC-DPROF is individual learning. The design of MOOC-DPROF is for it to run within the Moodle platform. PhD students are offered personalised learning materials in the form of digital textbooks, videos, audios as well as calculators, software, computer learning systems, an intelligent testing system, affective intelligent tutoring system, etc. A personalised MOOC-DPROF adapts the studies to individual needs. Upon completing the analysis of globally developed resilience management MOOCs, it was noticed that there is still no MOOC developed by applying biometric and intelligent systems in an integrated manner, something that has already been implemented with the MOOC-DPROF. The subsystems and a Case Study are briefly analysed in this paper

Material formativo y validación experimental de un sistema móvil de respuesta inmediata para la enseñanza de las habilidades directivas y gestión de la calidad en enfermería.

[SPA] Este trabajo presenta los resultados de una experiencia piloto que propone una herramienta de respuesta inmediata en el proceso de aprendizaje de la materia Gestión de la Calidad en Enfermería de una titulación universitaria del Grado de Enfermería en la Universidad de Murcia. Con la finalidad de alcanzar este objetivo se ha preparado material digital para el aprendizaje de la materia de “Habilidades Directivas y Gestión de la Calidad en Enfermería”, utilizando dispositivos de respuesta automática, también conocidos como clickers. El nuevo material es más dinámico que las tradicionales transparencias en PowerPoint, a la vez que estimula la participación de los alumnos. El material se ha preparado empleando la herramienta SIDRA (SIstema De Respuesta inmediata de la Audiencia), implementada por miembros del Grupo de Investigación de Ingeniería del Software de la Universidad de Murcia. Como resultado de esta innovación docente, se ha publicado un artículo con la primera parte del proyecto, donde presenta una revisión bibliográfica de estudios empíricos que emplean dispositivos inalámbricos en educación universitaria de enfermería. Otro artículo científico presenta la experiencia de SIDRA en un curso universitario que enseña las habilidades directivas y gestión de la calidad en enfermería. [ENG] This paper presents an empirical study on the effectiveness of the use of an Audience Response System (ARS) called SIstema De Respuesta inmediata de la Audiencia (SIDRA) in a nursing administration and management course at the University of Murcia. With this aim in view, instructional material was prepared to teach the course “Habilidades Directivas y Gestión de la Calidad en Enfermería”, by using automatic response devices known as clickers. This instrument is more dynamic than the traditional slides in PowerPoint, thus promoting the students’ participation. The software tool SIDRA (SIstema De Respuesta inmediata de la Audiencia), designed and implemented by members of the Software Engineering Research Group at the University of Murcia, was used to carry out the proposal. As a result, a mapping study which explores the adoption of wireless devices in university nursing teaching and addresses their repercussion on future professionals was published in a research journal. Another research paper submitted to a research journal describes our experience on the use of SIDRA in a nursing administration and management course.Este trabajo forma parte del proyecto PEGASO-PANGEA (TIN2009-13718-C02-02) financiado por el Ministerio de Ciencia e Innovación, y del proyecto GEODAS-REQ (TIN2012-37493-C03-02) financiado por el Ministerio de Economía y Competitividad y con fondos europeos FEDE

A Rule-Based Expert System for Teachers’ Certification in the Use of Learning Management Systems

In recent years and accelerated by the arrival of the COVID-19 pandemic, Learning Management Systems (LMS) are increasingly used as a complement to university teaching. LMS provide an important number of resources and activities that teachers can freely select to complement their teaching, which means courses with different usage patterns difficult to characterize. This study proposes an expert system to automatically classify courses and certify teachers’ LMS competence from LMS logs. The proposed system uses clustering to stablish the classification scheme. From the output of this algorithm, it defines the rules used to classify courses. Data registered from a university virtual campus with 3,303 courses and two million interactive events have been used to obtain the classification scheme and rules. The system has been validated against a group of experts. Results show that it performs successfully. Therefore, it can be concluded that the system can automatically and satisfactorily evaluate and certify the teachers’ LMS competence evidenced in their courses

On Predicting Learning Styles in Conversational Intelligent Tutoring Systems using Fuzzy Decision Trees

Intelligent Tutoring Systems personalise learning for students with different backgrounds, abilities, behaviours and knowledge. One way to personalise learning is through consideration of individual differences in preferred learning style. OSCAR is the name of a Conversational Intelligent Tutoring System that models a person's learning style using natural language dialogue during tutoring in order to dynamically predict, and personalise, their tutoring session. Prediction of learning style is undertaken by capturing independent behaviour variables during the tutoring conversation with the highest value variable determining the student's learning style. A weakness of this approach is that it does not take into consideration the interactions between behaviour variables and, due to the uncertainty inherently present in modelling learning styles, small differences in behaviour can lead to incorrect predictions. Consequently, the learner is presented with tutoring material not suited to their learning style. This paper proposes a new method that uses fuzzy decision trees to build a series of fuzzy predictive models combining these variables for all dimensions of the Felder Silverman Learning Styles model. Results using live data show the fuzzy models have increased the predictive accuracy of OSCAR-CITS across four learning style dimensions and facilitated the discovery of some interesting relationships amongst behaviour variables