Visualization of Students’ Solutions as a Sequential Network

It is known that timely and personalized feedback is vital to the learning process, and because of increasing enrollment, instructors can find it harder to provide that feedback. Learning analytics presents a solution to this problem. The growth in popularity of online education systems better enables learning analytics by providing additional educational data. This work focuses on the analysis of students’ incorrect short answers and their pathways to correct solutions. By considering student submissions as sequences, this work uses a dimension called “distance” which can be used to predict how far off a student’s incorrect answer is from a correct one. This distance metric can be used for recognizing students who may need help, understanding which concepts students struggle with, evaluating assessment questions, and improving multiple-choice answers.This paper discusses the methods, relevant learning scenarios, and applications of the learning analytics system. It features the results and analysis of a usability test conducted on 56 faculty members.