Education: Difference between revisions

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 />

==Background Readings==

 

*'''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.

 

 

*[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.

*[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://ctat.pact.cs.cmu.edu/ '''Cognitive Tutor Authoring Tools (CTAT)''']: software that enables the authoring of intelligent tutor behavior.

*[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==