Open AccessTHE STATE OF THE ART IN PROVIDING AUTOMATED FEEDBACK TO OPEN-ENDED STUDENT WORK
Author(s) -
Paula Larrondo,
Brian Frank,
Julián M. Ortíz
Publication year - 2021
Publication title -
proceedings of the ... ceea conference
Language(s) - English
Resource type - Journals
ISSN - 2371-5243
DOI - 10.24908/pceea.vi0.14854
Subject(s) - computer science , context (archaeology) , work (physics) , cognition , state (computer science) , mathematics education , transfer of learning , human–computer interaction , data science , artificial intelligence , psychology , engineering , mechanical engineering , algorithm , neuroscience , biology , paleontology
This article provides a review of the state of the art of technologies in providing automated feedback toopen-ended student work on complex problems. It includes a description of the nature of complex problems and elements of effective feedback in the context of engineering education. Existing technologies based on traditional machine learning methods and deep learning methods are compared in light of the cognitive skills, transfer skills and student performance expected in a complex problemsolving setting. Areas of interest for future research are identified.