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Learning ecosystems have seen as environments which are "consistent with (not antagonistic to) how learners learn".
Currently, the next challenge ahead is to design learning ecosystems that integrate smart learning to personalise and self-regulate learning.
Companies that create learning ecosystems, behavior-based focus and enterprise modeling that last during training and throughout the company are setting themselves up for success.
The Traphagen & Traill publication identifies STEM learning ecosystems as "intentionally support[ing] those youth historically underrepresented in STEM...to foster diverse and inter-connected STEM learning experiences". This year, twenty-seven communities have been selected by the STEM Funders Network in the inaugural STEM Learning Ecosystems Initiative.
The next section discusses the main pedagogical challenges when designing learning ecosystems that integrate smart learning.
"We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems," said Jim Zemlin, executive director of The Linux Foundation.
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As the machine learning ecosystem evolves, companies will find interesting ways to combine in-house industry experience with a range of off-the-shelf tools and open source algorithms to create highly-customised decision-support tools.
Fig. 3 The learning ecosystem to support active learning.
By exploring the notion of learning ecosystem, we shown that is applicable to describe and model the main actors, diverse resources and sociotechnical dynamics within learning.
Thus, the concept of learning ecosystem provides an ideal ground to orchestrate multiple tools and practices in the best possible way (Dillenbourg & Jermann, 2010).
Like Google, Facebook and others, Microsoft would of course love for the machine learning ecosystem to build more of its solutions around its toolkit.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com