Education: Difference between revisions
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On the one hand, in addition to being universally beneficial, education can improve the resilience of communities to climate change, especially in developing countries. ML can help enable personalized and scalable tools for education. On the other, education can empower individuals to adopt more sustainable lifestyles. ML can help educate the public about climate change through conversational agents and adaptive learning techniques. |
On the one hand, in addition to being universally beneficial, education can improve the resilience of communities to climate change, especially in developing countries. ML can help enable personalized and scalable tools for education. On the other, education can empower individuals to adopt more sustainable lifestyles. ML can help educate the public about climate change through conversational agents and adaptive learning techniques. |
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=== Primers === |
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*'''Advances In Intelligent Tutoring Systems (2010)''' <ref>{{Cite book|title=Advances in Intelligent Tutoring Systems|url=http://link.springer.com/10.1007/978-3-642-14363-2|publisher=Springer Berlin Heidelberg|accessdate=2020-08-28}}</ref>''':''' the textbook on creating adaptable learning agents, with chapters dedicated to different approaches and theories. [https://www.springer.com/gp/book/9783642143625 Available here.] |
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*'''Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies Of The Future (2016)'''<ref>{{Cite journal|last=Pinkwart|first=Niels|date=2016-06|title=Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies of the Future|url=http://link.springer.com/10.1007/s40593-016-0099-7|journal=International Journal of Artificial Intelligence in Education|language=en|volume=26|issue=2|pages=771–783|doi=10.1007/s40593-016-0099-7|issn=1560-4292}}</ref>''':''' a thorough analysis of the promise of Artificial Intelligence in education and the challenges that it entails. [https://link.springer.com/content/pdf/10.1007%2Fs40593-016-0099-7.pdf Available here.] |
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*'''Not Just Hot Air: Putting Climate Change Education Into Practice (2015)'''<ref>{{Cite web|url=https://unesdoc.unesco.org/ark:/48223/pf0000233083|website=unesdoc.unesco.org}}</ref>''':''' a primer prepared by the UNESCO about teaching climate change education to different populations of students. |
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==Online courses and course materials== |
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== Recommended Readings == |
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* Nkambou, et al., [https://www.springer.com/gp/book/9783642143625 Advances In Intelligent Tutoring Systems] (2010) |
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* Pinkwart, N. [https://link.springer.com/content/pdf/10.1007%2Fs40593-016-0099-7.pdf Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies Of The Future] (2016) |
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* UNESCO. [https://unesdoc.unesco.org/ark:/48223/pf0000233083 Not Just Hot Air: Putting Climate Change Education Into Practice] (2015) |
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===Major journals=== |
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*[https://educationaldatamining.org/ International Educational Data Mining Society]: a long-standing society that aims to apply different data mining and analysis techniques on educational data. |
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=== Journals and conferences === |
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== Libraries and tools == |
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* [https://iaied.org/ International Educational Data Mining Society] |
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* [http://www.unesco.org/education/tlsf/ UNESCO Teaching and Learning for a Sustainable Future] |
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=== Past and upcoming events === |
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== Important considerations == |
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⚫ | *[https://archive.ics.uci.edu/ml/datasets/ser+Knowledge+Modeling+Data+%28Students%27+Knowledge+Levels+on+DC+Electrical+Machines%29 User Knowledge Modeling Data (Students’ Knowledge Levels on DC Electrical Machines) Data Set]: a dataset of user learning activities and knowledge levels in electrical engineering. |
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== Selected problems== |
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==References== |
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<references /> |