Ergonomía. Fundamentos para el desarrollo de soluciones ergonómicas

En este texto se presentan algunos conceptos y marcos teóricos útiles para el análisis del trabajo en ergonomía. El objetivo es mostrar los conceptos de base para el estudio del trabajo en la tradición de la ergonomía de la actividad, y analizar de manera general algunos de los modelos empleados para el análisis de una actividad de trabajo. Inicialmente se abordan los principios teóricos de la ergonomía y los principios que provienen de la fisiología, la biomecánica, la psicología y la sociología; también se presentan los acercamientos metodológicos empleados en esta misma perspectiva para el análisis de actividades de trabajo. Se parte del principio de que un estudio ergonómico del trabajo se puede llevar a cabo desde una doble perspectiva: la perspectiva analítica y la perspectiva comprensiva.

Assessorament virtual didàctic. 'Asesoramiento virtual didáctico'.

En el final del documento se recogen los mensajes

El reto de un aprendizaje equilibrado en la enseñanza de ELE.

Se aboga por el uso de internet y del sistema administrativo 'Blackboard' para el acceso a textos adecuados para practicar la lectura en español como lengua extranjera (ELE). Se salvan así algunas carencias y dificultades del sistema educativo, complementándose con las nuevas tecnologías.

Aplicación de herramientas de comunicación de la plataforma WebCT en la tutorización de estudiantes universitarios dentro del espacio Europeo de Educación Superior.

Resumen basado en el de la publicación

El blog como herramienta didáctica en el Espacio Europeo de Educación Superior.

Resumen basado en el de la publicación

Las TIC como herramienta fundamental de la formación permanente en la Universidad de Sevilla.

Resumen basado en el de la publicación

Collaborative eLearning Assistant Network: caring agents are conscious agents

Would a research assistant - who can search for ideas related to those you are working on, network with others (but only share the things you have chosen to share), doesn’t need coffee and who might even, one day, appear to be conscious - help you get your work done? Would it help your students learn? There is a body of work showing that digital learning assistants can be a benefit to learners. It has been suggested that adaptive, caring, agents are more beneficial. Would a conscious agent be more caring, more adaptive, and better able to deal with changes in its learning partner’s life? Allow the system to try to dynamically model the user, so that it can make predictions about what is needed next, and how effective a particular intervention will be. Now, given that the system is essentially doing the same things as the user, why don’t we design the system so that it can try to model itself in the same way? This should mimic a primitive self-awareness. People develop their personalities, their identities, through interacting with others. It takes years for a human to develop a full sense of self. Nobody should expect a prototypical conscious computer system to be able to develop any faster than that. How can we provide a computer system with enough social contact to enable it to learn about itself and others? We can make it part of a network. Not just chatting with other computers about computer ‘stuff’, but involved in real human activity. Exposed to ‘raw meaning’ – the developing folksonomies coming out of the learning activities of humans, whether they are traditional students or lifelong learners (a term which should encompass everyone). Humans have complex psyches, comprised of multiple strands of identity which reflect as different roles in the communities of which they are part – so why not design our system the same way? With multiple internal modes of operation, each capable of being reflected onto the outside world in the form of roles – as a mentor, a research assistant, maybe even as a friend. But in order to be able to work with a human for long enough to be able to have a chance of developing the sort of rich behaviours we associate with people, the system needs to be able to function in a practical and helpful role. Unfortunately, it is unlikely to get a free ride from many people (other than its developer!) – so it needs to be able to perform a useful role, and do so securely, respecting the privacy of its partner. Can we create a system which learns to be more human whilst helping people learn?

Innovative implementation of iLearn e-Portfolio for Personal Development Planning

iLearn is a Web 2.0 tool developed in Blackboard to help students with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the students. The e-Portfolio tool benefits students as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Students find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables students to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the assignments and how students make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, students become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for students to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the students’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their Personal Tutorial meetings and provide their Personal Tutor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their student experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares for the workplace and life beyond University including continuing professional development. Students are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for students to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

Innovative use of e-portfolio for personal development

iLearn is a quasi-Web 2.0 tool developed in Blackboard to help users with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the users, who are training to be professionals in construction management and surveying, The e-Portfolio tool benefits users as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Users find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables learners to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the tasks and how they make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, learners become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for them to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the users’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their supervision meetings and provide their supervisor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their learning experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares them for the workplace including continuing professional development. Users are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for them to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

Parallel induction of modular classification rules

The Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm.

J-PMCRI: a methodology for inducing pre-pruned modular classification rules

Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets.