Performance evaluation of students using multimodal learning systems

Abstract

Multimodal learning, as an effective method for helping students to understand complex concepts, has attracted much research interest recently. Using more than one media in the learning process typically makes the study material easier to grasp. In the current study, students annotate linguistic and visual elements in multimodal texts by using geometric shapes and assigning attributes. However, how to effectively evaluate student performance is a challenge. This work proposes to make use of a vector space model to process student-generated multimodal data, with a view to evaluating student performance based on the annotation data. The vector model consists of fuzzy membership functions to model the performance in the various annotation criteria. These vectors are then used as the input to a multi-criteria ranking framework to rank the students.