ANNOTATION-BASED LEARNER’S PERSONALITY MODELING IN DISTANCE LEARNING CONTEXT

Abstract

Researchers in distance education are interested in observing and modelling of learner’s personality profile, and adapting their learning experiences accordingly. When learners read and interact with their reading materials, they do unselfconscious activities like annotation which may be key feature of their personalities. Annotation activity requires the reader to be active, to think critically and to analyze what has been written, and to make specific annotations in the margins of the text. These traces is reflected through underlining, highlighting, scribbling comments, summarizing, asking questions, expressing confusion or ambiguity, and evaluating the content of reading. In this paper, we present a semi-automatic approach to build learners’ personality profiles based on their annotation traces yielded during active reading sessions. The experimental results show the system’s efficiency to measure, with reasonable accuracy, the scores of learner’s personality traits.