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

2nd International Conference on Education and New Learning Technologies

Entrepreneurship in the University of the Basque Country (UPV/EHU)

Currently entrepreneurship is a basic pillar in the socioeconomic area of any country since it contributes to attain the government objectives of job creation and boost of dynamism and competitiveness of the business network. These aspects are even more crucial nowadays due to the global economic crisis. This paper attempts to provide some new insights about the scientific field entrepreneurship in the Basque Country analyzing some aspects related to the entrepreneurial activity, the profile of the entrepreneur and the characteristics of the entrepreneurial businesses. It also aims to provide empirical evidence that proves the good features of the spin-offs and TBF groups as job creators, technology transferors to the society and important actors in the growth and development of the economies for both the business network and its surrounding society. To this aim, a descriptive study has been performed of the firms belonging to the Zitek Program of Bizkaia Campus of the University of the Basque Country (UPV/EHU).

Analysis, optimization and development of an answer scoring system

The main contribution of this work is to analyze and describe the state of the art performance as regards answer scoring systems from the SemEval- 2013 task, as well as to continue with the development of an answer scoring system (EHU-ALM) developed in the University of the Basque Country. On the overall this master thesis focuses on finding any possible configuration that lets improve the results in the SemEval dataset by using attribute engineering techniques in order to find optimal feature subsets, along with trying different hierarchical configurations in order to analyze its performance against the traditional one versus all approach. Altogether, throughout the work we propose two alternative strategies: on the one hand, to improve the EHU-ALM system without changing the architecture, and, on the other hand, to improve the system adapting it to an hierarchical con- figuration. To build such new models we describe and use distinct attribute engineering, data preprocessing, and machine learning techniques.