Abir Abyaa
ABSTRACT
Differences in the learners' personality have an impact on their learning outcomes and achievements. Therefore, there is a need to automatically predict and identify their personalities in an unobtrusive way, and build the learner model accordingly. In this paper, we try to identify the learner's personality dimensions, according to the big five personality model, using educational data features in order to develop an automatic classifier that predicts the learner's personality discreetly based on his/her traces in an online learning system. We applied seven different supervised learning classification algorithms, using personality scores for each dimension (high or low) as target values, and analyzed the results. The findings were encouraging and revealed that most of Big Five personality dimensions can in fact be predicted using mainly educational data features, which could have an added value on unobtrusive dynamic learner modelling.
- A. Abyaa, M.K. Idrissi and S. Bennani. 2016. Towards an adult learner model in an online learning environment. In Proceeding of the 15th International Conference on Information Technology Based Higher Education and Training (ITHET 2016). IEEE, Istanbul, Turkey, 1--5.Google Scholar
- G. Blickle, 1996. Personality traits, learning strategies, and performance. European Journal of personality. 10, 5 (1996), 337--352.Google Scholar
- B. De Raad. 2000. The Big Five Personality Factors: The psycholexical approach to personality. Hogrefe & Huber Publishers.Google Scholar
- A. R. Faria, A. Almeida, C. Martins, and R. Gonçalves. 2015. Emotional adaptive platform for learning. Methodologies and Intelligent Systems for Technology Enhanced Learning, vol 374. Springer, Cham, 9--16.Google Scholar
- M. S. Halawa, M. E. Shehab, and E. M. R. Hamed. 2015. Predicting student personality based on a data-driven model from student behavior on LMS and social networks. In Proceedings of the Fifth International Conference on Digital Information Processing and Communications (ICDIPC). IEEE, Sierre, Switzerland, 294--299.Google ScholarCross Ref
- J. Heinström. 2000. The impact of personality and approaches to learning on information behavior. Information research. 5, 3 (2000), 1--10.Google Scholar
- F. Keshtkar, C. Burkett, H. Li, and A. C. Graesser. 2014. Using data mining techniques to detect the personality of players in an educational game. Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham, 125--150.Google Scholar
- N. Omheni, A. Kalboussi, O. Mazhoud, and A. H. Kacem. 2017. Computing of Learner's Personality Traits Based on Digital Annotations. International Journal of Artificial Intelligence in Education, 27, 2 ((2017), 241--267.Google Scholar
- A. Pena-Ayala and H. Sossa. 2013. Proactive sequencing based on a causal and fuzzy student model. Intelligent and Adaptive Educational-Learning Systems. Smart Innovation, Systems and Technologies, vol 17. Springer, Berlin, Heidelberg. Google ScholarDigital Library
- A.E Poropat. 2009. A meta-analysis of the five-factor model of personality and academic performance. Psychological bulletin. 135, 2 (2009), 322--338.Google Scholar
- A. Tlili, F. Essalmi, L. J. Ben Ayed, M. Jemni and Kinshuk. 2017. A Smart Educational Game to Model Personality Using Learning Analytics. In Proceedings of the 17th International conference of Advanced Learning Technologies (ICALT ' 2017) IEEE, Timisoara, Romania, 131--135.Google Scholar
- R. Wang, F. Chen, Z. Chen, T. Li, G. Harari, S. Tignor, X. Zhou, D. Ben-Zeev, and A.T. Campbell. 2014. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (UbiComp '14). ACM, New York, NY, USA, 3--14. Google ScholarDigital Library
- I.H. Witten and E. Frank. 2016. Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, Fourth Edition. Morgan Kaufmann. Google ScholarDigital Library
Index Terms
- Predicting the learner's personality from educational data using supervised learning
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