Abstract
Application execution requests in cloud architecture and fog paradigm are generally heterogeneous in terms of contexts at the device and application level. The scheduling of requests in these architectures is an optimization problem with multiple constraints. Despite numerous efforts, task scheduling in these architectures and paradigms still presents some enticing challenges that make us question how tasks are routed between different physical devices, fog, and cloud nodes. The fog is defined as an extension of the cloud, which provides processing, storage, and network services near the edge network, and due to the density and heterogeneity of devices, the scheduling is very complex, and, in the literature, we still find few studies. Trying to bring innovative contributions in these areas, in this paper, we propose a solution to the context-aware task-scheduling problem for fog paradigm. In our proposal, different context parameters are normalized through Min-Max normalization, requisition priorities are defined through the application of the Multiple Linear Regression (MLR) technique and scheduling is performed using Multi-Objective Non-Linear Programming Optimization (MONLIP) technique. The results obtained from simulations in the iFogSim toolkit, show that our proposal performs better compared to the non-context-aware proposals.

Abstract
Innovative educational technologies, the integration of Massive Open Online Courses methodology (MOOC), Challenge-Based Learning (CBL), and Virtual Assistant methodologies in Big Data course represent a dynamic evolution in pedagogical approaches. MOOCs offer scalable access to high-quality educational content, enabling learners to engage with Big Data concepts flexibly. CBL fosters critical thinking and problem-solving skills by immersing students in real-world scenarios relevant to Big Data analysis. Virtual Assistant methodologies leverage artificial intelligence to provide personalized learning experiences, enhancing student support and interactivity. This integrated approach not only cultivates a comprehensive understanding of Big Data but also prepares learners for the demands of a data-driven world. The authors are discussing the methodology and the effectiveness of the implemented course.