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The main objective of SE@VBL workshops was to develop a critical discussion about the next generation of environments and analytics employed in video learning tools, the form of these environments and analytics, and the way they can be utilized in order to help us to better understand and improve the value and "smartness" of video-based learning.
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These results suggest that the predicted negative consequences of external rewards may be addressed in this new generation of learning environments.
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The increasing amount of data being stored and the variety of algorithms proposed to meet processing demands of the data scientists have led to a new generation of computational environments and paradigms.
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Previous generations of game environments were either too obviously fake or too limited in terms of where they let players go and what they let them do.
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CEO of Professional Science Editing for Scientists @ prosciediting.com