Difference between revisions of "Education"

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''This page is about the intersection of education and machine learning in the context of climate change. For an overview of climate change education, please see the [https://en.wikipedia.org/wiki/Climate_change_education Wikipedia page] on this topic.''
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:
 
   
 
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.<br />
* [https://pslcdatashop.web.cmu.edu/index.jsp Datashop]: a large repository of learning interaction data hosted by Carnegie Mellon University.
 
* [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.
 
* [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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== Machine Learning Application Areas ==
== Methods and Software ==
 
   
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* '''Creating [[adaptable educational tools]]:''' by using AI and ML techniques to adapt to learner behavior, enabling more powerful tools with less attrition rates.
* [http://ctat.pact.cs.cmu.edu/ Cognitive Tutor Authoring Tools (CTAT)]: software that enables the authoring of intelligent tutor behavior.
 
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*'''Improving [[Climate Education|climate education:]]''' by improving existing existing climate education tools or by creating new ones using AI and ML.
* [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.
 
   
== Recommended Readings ==
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==Background Readings==
   
=== Readings ===
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=== Primers ===
   
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*'''Advances In Intelligent Tutoring Systems (2010)'''<ref>{{Cite journal|date=2010|editor-last=Nkambou|editor-first=Roger|editor2-last=Bourdeau|editor2-first=Jacqueline|editor3-last=Mizoguchi|editor3-first=Riichiro|title=Advances in Intelligent Tutoring Systems|url=https://link.springer.com/book/10.1007/978-3-642-14363-2|journal=Studies in Computational Intelligence|language=en-gb|doi=10.1007/978-3-642-14363-2|issn=1860-949X}}</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.]
* Nkambou, et al., [https://www.springer.com/gp/book/9783642143625 Advances In Intelligent Tutoring Systems] (2010)
 
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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.]
* 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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*'''Not Just Hot Air: Putting Climate Change Education Into Practice (2015)'''<ref>{{Cite journal|last=UNESCO|first=|date=2015|title=Not Just Hot Air: Putting Climate Change Education Into Practice|url=https://www.ctc-n.org/resources/not-just-hot-air-putting-climate-change-education-practice|journal=UNESCO Report|volume=|pages=|via=}}</ref>''':''' a primer prepared by the UNESCO about teaching climate change education to different populations of students.
* UNESCO. [https://unesdoc.unesco.org/ark:/48223/pf0000233083 Not Just Hot Air: Putting Climate Change Education Into Practice] (2015)
 
   
=== Online courses ===
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==Online Courses and Course Materials==
   
* [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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*[https://www.edx.org/course/climate-change-education-0 '''Climate Change Education''']: a course that presents the tools to teach climate change in a "positive, engaging and participatory way", curated by the Banco Interamericano de Desarollo.
   
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==Conferences, Journals, and Professional Organizations==
== Community ==
 
   
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===Major journals===
=== Journals and conferences ===
 
   
* [https://www.springer.com/journal/40593 The International Journal of Artificial Intelligence in Education (IJAIED)]
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*'''[https://www.springer.com/journal/40593 The International Journal of Artificial Intelligence in Education (IJAIED)]:''' the main reference for research in applying AI to the education sciences.
   
=== Societies and organizations ===
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===Major societies and organizations===
   
* [https://iaied.org/ International Educational Data Mining Society]
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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.
* [http://educationaldatamining.org/ International Artificial Intelligence in Education Society (IAIED)]
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*[http://educationaldatamining.org/ '''International Artificial Intelligence in Education Society (IAIED)''']: an interdisciplinary community at the frontiers of the fields of computer science, education, and psychology.
* [http://www.unesco.org/education/tlsf/ UNESCO Teaching and Learning for a Sustainable Future]
 
   
=== Past and upcoming events ===
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== Libraries and Tools ==
   
 
*[http://ctat.pact.cs.cmu.edu/ '''Cognitive Tutor Authoring Tools (CTAT)''']: software that enables the authoring of intelligent tutor behavior.
== Important considerations ==
 
 
*[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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==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:
   
 
*[https://pslcdatashop.web.cmu.edu/index.jsp '''Datashop''']: a large repository of learning interaction data hosted by Carnegie Mellon University.
== Next steps ==
 
 
*[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.
 
*[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).
   
== References ==
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==References==
  +
<references />

Latest revision as of 19:45, 6 December 2020

This page is about the intersection of education and machine learning in the context of climate change. For an overview of climate change education, please see the Wikipedia page on this topic.

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.

Machine Learning Application Areas[edit | edit source]

  • Creating adaptable educational tools: by using AI and ML techniques to adapt to learner behavior, enabling more powerful tools with less attrition rates.
  • Improving climate education: by improving existing existing climate education tools or by creating new ones using AI and ML.

Background Readings[edit | edit source]

Primers[edit | edit source]

  • 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[edit | edit source]

  • Climate Change Education: a course that presents the tools to teach climate change in a "positive, engaging and participatory way", curated by the Banco Interamericano de Desarollo.

Conferences, Journals, and Professional Organizations[edit | edit source]

Major journals[edit | edit source]

Major societies and organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

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:

References[edit | edit source]

  1. Nkambou, Roger; Bourdeau, Jacqueline; Mizoguchi, Riichiro, eds. (2010). "Advances in Intelligent Tutoring Systems". Studies in Computational Intelligence. doi:10.1007/978-3-642-14363-2. ISSN 1860-949X.
  2. 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)
  3. UNESCO (2015). "Not Just Hot Air: Putting Climate Change Education Into Practice". UNESCO Report.