Semiautomatic Grading of Short Texts for Open Answers in Higher Education

Abstract

Grading student activities in online courses is a time-expensive task, especially with a high number of students in the course. To avoid a bottleneck in the continuous evaluation process, quizzes with multiple choice questions are frequently used. However, a quiz fails on the provision of formative feedback to the student. This work presents PLeNTaS, a system for the automatic grading of short answers from open domains, that reduces the time required for the grading task and offers formative feedback to the students. It is based on the analysis of the text from the point of view of three different levels: orthography, syntax, and semantics. The validation of the system will consider the correlation of the assigned grade with the human grade, the utility of the automatically generated feedback and the pedagogical impact caused by the system usage in the course.