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 === |
| + | *'''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== |
| + | <references /> |
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Revision as of 17:18, 28 August 2020
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.
Background Readings
Primers
- Advances In Intelligent Tutoring Systems (2010) [1]: the textbook on creating adaptable learning agents, with chapters dedicated to different approaches and theories. Available here.
- Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies Of The Future (2016)[2]: a thorough analysis of the promise of Artificial Intelligence in education and the challenges that it entails. Available here.
- Not Just Hot Air: Putting Climate Change Education Into Practice (2015)[3]: a primer prepared by the UNESCO about teaching climate change education to different populations of students.
Online courses and course materials
- Banco Interamericano de Desarollo - Climate Change Education: a course that presents the tools to teach climate change n a positive, engaging and participatory way.
Community
Major journals
- The International Journal of Artificial Intelligence in Education (IJAIED): the main reference for research in applying AI to the education sciences.
Major societies and organizations
- International Educational Data Mining Society: a long-standing society that aims to apply different data mining and analysis techniques on educational data.
- International Artificial Intelligence in Education Society (IAIED): an interdisciplinary community at the frontiers of the fields of computer science, education, and psychology.
Libraries and tools
- Cognitive Tutor Authoring Tools (CTAT): software that enables the authoring of intelligent tutor behavior.
- Generalized Intelligent Framework for Tutoring (GIFT): a framework of tools, methods and standards to make it easier to author computer-based tutoring systems.
Data
There are many hurdles in accessing data generated from educational settings, given the privacy issues that arise and the digital divide that exists in many countries,where learning is offline. There are nonetheless a few data sources that can be of interest:
- Datashop: a large repository of learning interaction data hosted by Carnegie Mellon University.
- 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.
- University of Michigan source code and data associated with Practical Learning Analytics course: code and resources for the Pracitcal Learning Analytics online course offered at Michigan (code in R).
Selected problems
References
- ↑ Advances in Intelligent Tutoring Systems. Springer Berlin Heidelberg. Retrieved 2020-08-28.
- ↑ Pinkwart, Niels (2016-06). "Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies of the Future". International Journal of Artificial Intelligence in Education. 26 (2): 771–783. doi:10.1007/s40593-016-0099-7. ISSN 1560-4292. Check date values in:
|date=(help) - ↑ unesdoc.unesco.org https://unesdoc.unesco.org/ark:/48223/pf0000233083. Missing or empty
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