MOOC and OER: identity management

Open educational resources (OER) and massive open online courses (MOOC) are new and emerging issues in the international higher education context. Under the exponential growth of the supply of courses and related publications, the purpose of this chapter is to foster scientific discussion on the socio-cultural and economic impacts, as well as its technological and pedagogical implications. Supported by the methodological typology of bibliographical studies, systematized interpretative-critical analysis based on review of the concepts, and principles guiding OER and MOOC, the authors' reflections show that the enlargement terminologies without epistemological delimitation have provoked theoretical and practical mistakes. In the final considerations, the authors systematize broader problematizations around the open educational practices in universities aimed to five dimensions: spatio-time-content, theoretical models, principles of pedagogical innovation, economic aspects, and fundamentals of collaborative culture.info:eu-repo/semantics/publishedVersio

Genetic Algorithm Modeling with GPU Parallel Computing Technology

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012; Smart Innovation, Systems and Technologies, Vol. 19, Springe

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure