Semi-automatic generation of learning domain modules for technology supported learning systems

In a time when Technology Supported Learning Systems are being widely used, there is a lack of tools that allows their development in an automatic or semi-automatic way. Technology Supported Learning Systems require an appropriate Domain Module, ie. the pedagogical representation of the domain to be mastered, in order to be effective. However, content authoring is a time and effort consuming task, therefore, efforts in automatising the Domain Module acquisition are necessary.Traditionally, textbooks have been used as the main mechanism to maintain and transmit the knowledge of a certain subject or domain. Textbooks have been authored by domain experts who have organised the contents in a means that facilitate understanding and learning, considering pedagogical issues.Given that textbooks are appropriate sources of information, they can be used to facilitate the development of the Domain Module allowing the identification of the topics to be mastered and the pedagogical relationships among them, as well as the extraction of Learning Objects, ie. meaningful fragments of the textbook with educational purpose.Consequently, in this work DOM-Sortze, a framework for the semi-automatic construction of Domain Modules from electronic textbooks, has been developed. DOM-Sortze uses NLP techniques, heuristic reasoning and ontologies to fulfill its work. DOM-Sortze has been designed and developed with the aim of automatising the development of the Domain Module, regardless of the subject, promoting the knowledge reuse and facilitating the collaboration of the users during the process.

Cooperative learning strategies to teach nutrition to geriatric nursing staff

[EN] The objective of this study was to test the hypothesis that cooperative learning strategies will help to increase nutrition knowledge of nurses and nursing assistants caring for the elderly in different institutional communities of the Basque Country, Spain. The target population was a sample of volunteers, 16 nurses and 28 nursing assistants. Training consisted of 12 nutrition education sessions using cooperative strategies conducted over a period of 3 consecutive weeks. The assessment instruments included two pretest and two posttest questionnaires with questions selected in multiplechoice format. The first questionnaire was about general knowledge of applied nutrition (0-88 point scale) and the second one on geriatric nutrition knowledge (0-18 point scale). Data were analyzed using SPSS vs. 11.0. The outcomes indicated a significant increase in general nutrition knowledge (difference between the pre- and posttest mean score: 14.5±10.1; P<0.001) and in geriatric nutrition knowledge for all participants (difference between the pre- and post-test mean score: 4.6±4.6; P<0.001). So the results indicated that cooperative learning strategies could improve the nutrition knowledge of nursing staff. Additionally, the results of this study provide direction to continuing nutrition education program planners regarding appropriate content and methodology for programs.

Structural capital, innovation capability, and company performance in technology-based colombian firms

[EN] In today s economy, innovation is considered to be one of the main driving forces behind business competitiveness, if not the most relevant one. Traditionally, the study of innovation has been addressed from different perspectives. Recently, literature on knowledge management and intellectual capital has provided new insights. Considering this, the aim of this paper is to analyze the impact of different organizational conditions i.e. structural capital on innovation capability and innovation performance, from an intellectual capital (IC) perspective. As regards innovation capability, two dimensions are considered: new idea generation and innovation project management. The population subject to study is made up of technology-based Colombian firms. In order to gather information about the relevant variables involved in the research, a questionnaire was designed and addressed to the CEOs of the companies making up the target population. The sample analyzed is made up of 69 companies and is large enough to carry out a statistical study based on structural equation modelling (partial least squares approach) using PLS-Graph software (Chin and Frye, 2003). The results obtained show that structural capital explains to a great extent both the effectiveness of the new idea generation process and of innovation project management. However, the influence of each specific organizational component making up structural capital (organizational design, organizational culture, hiring and professional development policies, innovation strategy, technological capital, and external structure) varies. Moreover, successful innovation project management is the only innovation capability dimension that exerts a significant impact on company performance.

Motores térmicos de vapor, gas y de combustión interna a través de PBL (Project Based Learning)

Nivel educativo: Grado. Duración (en horas): De 31 a 40 horas

Using the system dynamics paradigm in teaching and learning technological university subjets

2nd International Conference on Education and New Learning Technologies

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.