Using Hybrid Effectively in Christian Higher Education

Hybrid is just one of a number of terms used for the convergence of face-to-face and online learning, At the University of Central Florida (UCF) they are called mixed mode courses, In the corporate world the most common language used for hybrid is blended learning, Blended learning, says Bob Mosher, is about using multiple learning modalities, which include, but are not limited to, the Web.7 The blended learning term is also being used more frequently within academic circles,8 Because of the inconsistency in how blended learning is employed, though, and because our goal is not to describe learning in general but to focus on individual courses, this article will use the term hybrid and will apply it more narrowly to mean a course in which face-to-face and online learning are integrated in such a way that the seat time of the course is reduced

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control

Today's fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement

Digital, material and networked: some emerging themes for SET education

Boundaries between the digital and material worlds are becoming blurred as the internet increasingly connects us to things as well as people and information. This is increasingly relevant to education as initiatives which significantly combine digital and material elements in networks are becoming a reality for Science, Engineering and Technology (SET) learning. Our paper reports on the initial findings of a project to carry out a ‘state of the art’ review of literature to establish the key themes, opportunities and obstacles that are emerging from the development and use of these ‘hybrid’ systems in learning. We wanted to explore the extent to which this new domain of study is being reported in the literature and to identify work representative of this area. Our aim was to investigate the depth of research in this area by going beyond the technologically descriptive to focus on pedagogical and organisational issues raised in the literature. To identify the state of current research in the area we carried out a systematic search of databases of Science, Engineering and Technology education literature. We found 808 papers relating to the hybrid learning initiatives we are interested in, of which the majority, 81%, involved the Engineering and Technology disciplines while 6.8% related to Science. The vast majority of papers referred to remote laboratories and most of these were concerned with describing the technologies involved. In order to explore issues emerging from the research, we carried out an in-depth text review of a particular subset of the papers found that focussed on pedagogical issues. The three main themes that emerged were: the importance of real data and authenticity in learning; the importance of a sense of presence (e.g. telepresence, social presence and/or immersion) and the locus of control in, and responsiveness of, a hybrid system. We conclude that these new digital ‘hybrid’ pedagogies offer a lens with which to view both the more traditional material pedagogies, e.g. laboratory-based learning, and purely digital pedagogies, e.g. virtual labs. Finally, issues of authenticity, presence and control/responsiveness will be of increasing pedagogical importance to other ‘hybrid’ systems, such as those involving ubiquitous computing

Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications

This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [EHJ17] and on quadratic backward stochastic differential equations as in [CR16]. We also performed tests on low-dimension control problems such as an option hedging problem in finance, as well as energy storage problems arising in the valuation of gas storage and in microgrid management. Numerical results and comparisons to quantization-type algorithms Qknn, as an efficient algorithm to numerically solve low-dimensional control problems, are also provided; and some corresponding codes are available on https://github.com/comeh/.Comment: 39 pages, 14 figures. Methodology and Computing in Applied Probability, Springer Verlag, In pres