Computing of Learner’s Personality Traits Based on Digital Annotations

Abstract

Researchers in education are interested in modeling of learner’s profile and adapt their learning experiences accordingly. When learners read and interact with their reading materials, they do unconscious practices like annotations which may be, a key feature of their personalities. Annotation activity requires readers to be active, to think critically and to analyze what has been drawn up, and to make explicit annotations in the margins of the text. Readers make annotation traces through underlining, highlighting, scribbling comments, summarizing, asking questions, expressing confusion or ambiguity, and evaluating the reading content. In this paper, we present a semi-automatic approach to building learners’ personality profiles based on their annotation traces yielded during an active reading session. The experimental results show the system’s efficiency to measure, with reasonable accuracy, the scores of a learner’s conscientiousness and neurotics traits.