Automatic evaluation of open-ended questions for online learning. A systematic mapping

Abstract

The assessment of students’ performances in Higher Education is one of the essential components of teaching activities. Open-ended tasks allow a more in-depth assessment of students’ learning levels, but their evaluation and grading are time-consuming and prone to subjective bias. Since the Covid-19 pandemic, most traditional Higher Education courses converted to online courses; automatic grading and feedback tools and methods (AGFTM) have become critical components of online learning systems, especially with regards to short answers and essays assessment. This work frames the recent advancement in AGFTM through a systematic mapping of the research field and a literature review. This analysis gives an overview of the trends, specific goals, methods, quality of proposals, challenges and limitations in this research area. The results indicate that it is a growing research area, with a large set of techniques involved, but still not mature, where practical implementations have yet to come.