While adaptive learning is emerging as a promising technology to promote access and quality at a large scale in higher education (Becker et al., 2018), the implementation of adaptive learning in teaching and learning is still sporadic, and it is unclear how to best design and teach an adaptive learning course in a higher education context. As early adopters, a team of instructors, instructional designers, and administrators at the University of Central Florida (UCF) identified five key design features as an adaptive learning design framework to guide the unique course design process. These five features involve deliberate design and development efforts that could bring significant benefits to student learning. The purpose of this field note is to present a design framework and best practices for teaching from both a systems and a pedagogical approach in the context of implementation at UCF. We also share the rationale and classification framework UCF has adopted to ensure the term “adaptive learning” is universally understood across campus. This paper offers insights into the design, delivery, and implications of utilizing adaptive learning systems in higher education courses at a public research university and attempts to capture the intimacy of lessons learned and best practices gathered since the project’s inception in 2014.

Stories of cyber attacks are becoming a routine in which cyber attackers show new levels of intention by sophisticated attacks on networks. Unfortunately, cybercriminals have figured out profitable business models and they take advantage of the online anonymity. A serious situation that needs to improve for networks' defenders. Therefore, a paradigm shift is essential to the effectiveness of current techniques and practices. Since the majority of cyber incidents are human enabled, this shift requires expanding research to underexplored areas such as behavioral aspects of cybersecurity. It is more vital to focus on social and behavioral issues to improve the current situation. This paper is an effort to provide a review of relevant theories and principles, and gives insights including an interdisciplinary framework that combines behavioral cybersecurity, human factors, and modeling and simulation.

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SUMMARYA technique of explicit calculation of sensitivity coe cients based on the approximation of the retrieved function by a linear combination of trial functions of compact support is presented. The method is applicable to steady state and transient linear inverse problems where unknown distributions of boundary uxes, temperatures, initial conditions or source terms are retrieved. The sensitivity coe cients are obtained by solving a sequence of boundary value problems with boundary conditions and source term being homogeneous except for one term. This inhomogeneous term is taken as subsequent trial functions. Depending on the type of the retrieved function, it may appear on boundary conditions (Dirichlet or Neumann), initial conditions or the source term. Commercial software and analytic techniques can be used to solve this sequence of boundary value problems producing the required sensitivity coe cients. The choice of the approximating functions guarantees a ÿltration of the high frequency errors. Several numerical examples are included where the sensitivity coe cients are used to retrieve the unknown values of boundary uxes in transient state and volumetric sources. Analytic, boundary-element and ÿnite-element techniques are employed in the study.

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