Teachers' attitudes to and beliefs about web-based Collaborative Learning Environments in the context of an international implementation

"Fifty-six teachers, from four European countries, were interviewed to ascertain their attitudes to and beliefs about the Collaborative Learning Environments (CLEs) which were designed under the Innovative Technologies for Collaborative Learning Project. Their responses were analysed using categories based on a model from cultural-historical activity theory [Engestrom, Y. (1987). Learning by expanding.- An activity-theoretical approach to developmental research. Helsinki: Orienta-Konsultit; Engestrom, Y., Engestrom, R., & Suntio, A. (2002). Can a school community learn to master its own future? An activity-theoretical study of expansive learning among middle school teachers. In G. Wells & G. Claxton (Eds.), Learning for life in the 21st century. Oxford: Blackwell Publishers]. The teachers were positive about CLEs and their possible role in initiating pedagogical innovation and enhancing personal professional development. This positive perception held across cultures and national boundaries. Teachers were aware of the fact that demanding planning was needed for successful implementations of CLEs. However, the specific strategies through which the teachers can guide students' inquiries in CLEs and the assessment of new competencies that may characterize student performance in the CLEs were poorly represented in the teachers' reflections on CLEs. The attitudes and beliefs of the teachers from separate countries had many similarities, but there were also some clear differences, which are discussed in the article. (c) 2005 Elsevier Ltd. All rights reserved."

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.