Assessing Procedural Knowledge in Free-Text Answers through a Hybrid Semantic Web Approach

Abstract

Several techniques have been proposed to automatically grade students' free-text answers in e-learning systems. However, these techniques provide no or limited support for the evaluation of acquired procedural knowledge. To address this issue, we propose a new approach, named ProcMark, specifically designed to assess answers containing procedural knowledge. It requires a teacher to provide the ideal answer as a semantic network (SN) that is used to automatically score learners' answers in plain text. The novelty of our approach resides mainly in three areas: a) the variable granularity levels possible in the SN and the parameterizing of ontology concepts, thus allowing the students free expression of their ideas, b) the new similarity measures of the grading system that give refined numerical scores, c) the language-independence of the grading system as all linguistic information is given as data files or dictionaries and is distinct of the semantic knowledge of the SN. Experimental results in a Computer Algorithms course show that the approach gives marks that are very close to those of human graders, with a very strong (0.70, 0.79, and 0.79) positive correlation.