Computer programming is a challenge for students and a major reason why people avoid Computer Science courses. Investigating alternative teaching methods is essential to encourage students to learn and understand the concepts of programming. The use of games in learning and training is advocated and supported by many researchers due to its motivational and attractive features. This study focuses on an approach that supports the use of learning methodologies based on constructionist activities. Therefore, a pedagogical framework is proposed to guide lecturers who teach programming on how to integrate games-based learning to present coding concepts in the context of familiar realworld applications like computer games development. The framework is supported by motivational and attractive game features in conjunction with the authentic and meaningful aspects of constructionist activities for Games-Based Construction Learning (GBCL). This paper summarises and presents a framework model based on a literature review and a panel of experts, with a view to performing a two-stage process to validate this framework. The paper discusses the design and validation of the framework and proposes actions regarding its implementation.
Computer science education, Games-based construction learning, Pedagogy.
Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.
Forest economics, Time series, Prediction.