Educational Story-Based Game for Capturing the Learner's Personality

Abstract

In recent years with the help of digital games there is an increasing interest in creating Serious Games for learning through play. With the help of machine learning algorithms, an educational serious game can be used, not only to assist the learner in his/her studies, but also to extract insights about the learner's personality. In game-based learning we take into account that the student behaves differently according to his/her individual characteristics while learning by playing. The most used method to model the learner’s personality is the self-report using questionnaires. The drawback of this approach is that the learner may not assess himself correctly or his/her answers’ may be biased towards the more socially acceptable responses rather than being truthful. In this paper, we explore the idea of having an alternative method of learning a person’s personality model and thus to better create interactive and engaging methods to assist learners in their studies. A story-based game with gamified learning elements was created for helping the learners study and evaluate their knowledge in the programming language C. The students learn by evaluating code snippets and depending on their response the game would give constructive feedback. At the same time students’ in-game behavior is captured and thus their personality traits could be determined. For modeling the learner’s personality we used the Five-Factor Model (OCEAN), a taxonomy of five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism), each of which combines many personality characteristics. To evaluate the efficiency of the proposed serious game, we gathered data from 107 first year Computer Science students from the University of Macedonia. The students played the game and filled in the Big Five Inventory (BFI) questionnaire to capture their OCEAN traits. The BFI questionnaire was used as a ground truth. After the data gathering, we used machine learning techniques and also classification algorithms to create our model. The goodness of the model was assessed using different metrics and the results showed that it is effective to model both the extraversion and openness personality dimensions using serious games instead of questionnaires.