Using Semantic Technologies for Formative Assessment and Scoring in Large Courses and MOOCs

Abstract

Formative assessment and personalised feedback are commonly recognised as key factors both for improving students’ performance and increasing their motivation and engagement (Gibbs and Simpson, 2005). Currently, in large and massive open online courses (MOOCs), technological solutions to give feedback are often limited to quizzes of different kinds. At present, one of our challenges is to provide feedback for open-ended questions through semantic technologies in a sustainable way. To face such a challenge, our academic team decided to use a test based on latent semantic analysis (LSA) and chose an automatic assessment tool named G-Rubric. G-Rubric was developed by researchers at the Developmental and Educational Psychology Department of UNED (Spanish national distance education university). By using G-Rubric, automated formative and iterative feedback was provided to students for different types of open-ended questions (70–800 words). This feedback allowed students to improve their answers and writing skills, thus contributing both to a better grasp of concepts and to the building of knowledge. In this paper, we present the promising results of our first experiences with UNED business degree students along three academic courses (2014–15, 2015–16 and 2016–17). These experiences show to what extent assessment software such as G-Rubric is mature enough to be used with students. It offers them enriched and personalised feedback that proved entirely satisfactory. Furthermore, G-Rubric could help to deal with the problems related to manual grading, even though our final goal is not to replace tutors by semantic tools, but to give support to tutors who are grading assignments.