THE STATE OF THE ART IN PROVIDING AUTOMATED FEEDBACK TO OPEN-ENDED STUDENT WORK

Abstract

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.