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Direct linkStefan Ruseti; Mihai Dascalu; Amy M. Johnson; Renu Balyan; Kristopher J. Kopp; Danielle S. McNamara – Grantee Submission, 2018
This study assesses the extent to which machine learning techniques can be used to predict question quality. An algorithm based on textual complexity indices was previously developed to assess question quality to provide feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). In…
Descriptors: Questioning Techniques, Artificial Intelligence, Networks, Classification
Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G. – International Educational Data Mining Society, 2017
An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…
Descriptors: Data Analysis, Misconceptions, Student Attitudes, Feedback (Response)
Kommers, Piet, Ed.; Issa, Tomayess, Ed.; Issa, Theodora, Ed.; McKay, Elspeth, Ed.; Isias, Pedro, Ed. – International Association for Development of the Information Society, 2016
These proceedings contain the papers and posters of the International Conferences on Internet Technologies & Society (ITS 2016), Educational Technologies (ICEduTech 2016) and Sustainability, Technology and Education (STE 2016), which have been organised by the International Association for Development of the Information Society and…
Descriptors: Conferences (Gatherings), Foreign Countries, Internet, Educational Technology
