By Vytautas Štuikys

This monograph provides the demanding situations, imaginative and prescient and context to layout shrewdpermanent studying items (SLOs) via computing device technology (CS) schooling modelling and have version changes. It provides the most recent examine at the meta-programming-based generative studying items (the latter with complicated gains are taken care of as SLOs) and using academic robots in instructing CS subject matters. The brought method comprises the general methods to enhance SLO and shrewdpermanent academic atmosphere (SEE) and integrates either into the genuine schooling atmosphere to supply instructing in CS utilizing constructivist and project-based techniques in addition to review of pedagogic outcomes.

Smart studying gadgets for shrewdpermanent schooling in desktop Sciencewill attract researchers in CS schooling fairly these attracted to utilizing robots in instructing, path designers and academic software program and instruments builders. With learn and workout questions on the finish of every bankruptcy scholars learning CS comparable classes will locate this paintings informative and priceless too.

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Additional info for Smart Learning Objects for Smart Education in Computer Science: Theory, Methodology and Robot-Based Implementation

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5. g. adaptation of software reuse-based models, such as GLO, whitebox LO, glass-box LO to e-learning domain and many more). 6. There is a long list of organizational activities that aim at enforcing and stimulating the engagement of young generation in computational sciences. Examples are organized worldwide Olympiads in programming, the International Contest on Informatics and Computer Literacy, local Olympiads and other forums within separate countries and ‘Hour of Code’ to name a few. The latter was provided by ACM (from December 9 to 15, 2013) as a part of the Computer Science Education Week that ‘aims to introduce more than 10 million students of all ages to the basics of coding and to underscore the critical role of computing in all careers’.

It enables to express the different types of learning variability (pedagogical variability, social variability, content variability, technology variability and interaction among the variability constituents) through parameters uniformly [SˇBD13]. • It ensures a flexible management of learning variability first through selecting the adequate parameter values and then through the generation process which is supported by the meta-language processor. This is possible due to the uniform representation of the different variability constituents.

For example, meta-programming – the powerful technology or methodology to support generative reuse we use in different domains [SˇD13] – does not allow achieving its promises at once. Rather, it demands a thorough domain analysis, investment into the creation of meta-programming tools such as meta-language processors, refactoring/transformation tools, development of generic component libraries and code generators, etc. The payoff will come only after some time, and designers will have to accept that.

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