Providing automated and individually tailored assessment feedback: panel session

Samuel Cunningham-Nelson, M. Jean Mohammadi-Aragh, Andrea Goncher, Wageeh Boles

Research output: Book chapter/Published conference paperConference paperpeer-review

Abstract

Providing valuable and timely feedback for students is a difficult task. How can we use machine learning to aid this? Participants attending this panel session will learn about various implementations of machine learning algorithms in providing useful feedback for students. These applications will include determining conceptual understanding, tailoring a student's individual learning pathway and providing feedback on components of programming. The panel will then explore how this area might develop in the future.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
EditorsMark J.W. Lee, Sasha Nikolic, Gary K.W. Wong, Jun Shen, Montserrat Ros, Leon C. U. Lei, Neelakantam Venkatarayalu
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1233-1234
Number of pages2
ISBN (Electronic)9781538665220
DOIs
Publication statusPublished - 16 Jan 2019
Event2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Novotel Northbeach Wollongong, Wollongong, Australia
Duration: 04 Dec 201807 Dec 2018
https://www.tale2018.org/ (conference website)
https://1775b349-31b3-42ae-85cf-8678bf2f3e88.filesusr.com/ugd/fc5d2b_662d818506f94c73958122d27caf8104.pdf (program)

Publication series

NameProceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

Conference

Conference2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
Abbreviated titleEngineering next-generation learning
CountryAustralia
CityWollongong
Period04/12/1807/12/18
Internet address

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