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. |
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TODO: add context regarding contribution to emissions, connection to ML, and selected readings |
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== Data == |
== Data == |
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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: |
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* [https://pslcdatashop.web.cmu.edu/index.jsp Datashop]: a large repository of learning interaction data hosted by Carnegie Mellon University. |
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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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* [https://github.com/bkoester/PLA 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). |
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== Methods and Software == |
== Methods and Software == |
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* [http://ctat.pact.cs.cmu.edu/ Cognitive Tutor Authoring Tools (CTAT)]: software that enables the authoring of intelligent tutor behavior. |
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* [https://www.gifttutoring.org/ Generalized Intelligent Framework for Tutoring (GIFT)]: a framework of tools, methods and standards to make it easier to author computer-based tutoring systems. |
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== Recommended Readings == |
== Recommended Readings == |
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=== 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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=== Online courses === |
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* [https://www.edx.org/course/climate-change-education-0 Banco Interamericano de Desarollo - Climate Change Education]: a course that presents the tools to teach climate change n a positive, engaging and participatory way. |
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== Community == |
== Community == |
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=== Journals and conferences === |
=== Journals and conferences === |
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* [https://www.springer.com/journal/40593 The International Journal of Artificial Intelligence in Education (IJAIED)] |
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=== Societies and organizations === |
=== Societies and organizations === |
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* [https://iaied.org/ International Educational Data Mining Society] |
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* [http://educationaldatamining.org/ International Artificial Intelligence in Education Society (IAIED)] |
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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 === |
=== Past and upcoming events === |
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Revision as of 15:50, 25 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.
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).
Methods and Software
- 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.
Recommended Readings
Readings
- Nkambou, et al., Advances In Intelligent Tutoring Systems (2010)
- Pinkwart, N. Another 25 Years of AIED? Challenges and Opportunities for Intelligent Educational Technologies Of The Future (2016)
- UNESCO. Not Just Hot Air: Putting Climate Change Education Into Practice (2015)
Online courses
- 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
Journals and conferences
Societies and organizations
- International Educational Data Mining Society
- International Artificial Intelligence in Education Society (IAIED)
- UNESCO Teaching and Learning for a Sustainable Future