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Education: Difference between revisions
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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.▼
▲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 />
== Machine Learning Application Areas ==
* '''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|climate education:]]''' by improving existing existing climate education tools or by creating new ones using AI and ML.
==Background Readings==
=== Primers ===
*'''Advances In Intelligent Tutoring Systems (2010)'''
*'''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.]
*'''Not Just Hot Air: Putting Climate Change Education Into Practice (2015)'''<ref>{{Cite
==Online
*[https://www.edx.org/course/climate-change-education-0
==Conferences, Journals, and Professional Organizations==
===Major journals===
*'''[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.
===Major societies and organizations===
*[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)''']: an interdisciplinary community at the frontiers of the fields of computer science, education, and psychology.
== Libraries and
*[http://ctat.pact.cs.cmu.edu/ '''Cognitive Tutor Authoring Tools (CTAT)''']: software that enables the authoring of intelligent tutor behavior.
*[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.
==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.
*[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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