Aleksandrowicz, J. W., Klasa, K., Sobański, J. A., & Stolarska, D. (2007). KON-2006-Neurotic Personality Questionnaire. Psychiatria polska, 41(6), 759- 778.
Allport, G. W. (1961). Pattern and growth in personality. Holt, Reinhart & Winston.
Anglim, J., Horwood, S., Smillie, L. D., Marrero, R. J., & Wood, J. K. (2020). Predicting psychological and subjective well-being from personality: A meta-analysis. Psychological bulletin, 146(4), 279.
Backmann, J., Weiss, M., Schippers, M. C., & Hoegl, M. (2019). Personality factors, student resiliency, and the moderating role of achievement values in study progress. Learning and Individual Differences, 72, 39-48.
Bashiri Haddadan, G., Mahmoodi, F., Rezapoor, Y., & Adib, Y. (2016). Describing the experience and perception of teachers and experts from education in multi-grade classes of primary schools in rural areas of Kalibar. Teaching and Learning Research, 12(2), 107-120.
Briley, D.A. & Tucker-Drob, E.M. (2014). Genetic and environmental continuity in personality development: a meta-analysis. Psychological Bulletin, 140(5), 1303.
Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18(6), 683-695.
Costa, L., Demers, L. M., Gouveia-Oliveira, A., Schaller, J., Costa, E. B., De Moura, M. C., & Lipton, A. (2002). Prospective evaluation of the peptide-bound collagen type I cross-links N-telopeptide and C-telopeptide in predicting bone metastases status. Journal of Clinical Oncology, 20(3), 850-856.
Dietz-Uhler, B. & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of interactive online learning, 12(1), 17-26.
Elias, T. (2011). Learning analytics. Learning, 1-22.
Eysenck, H. J., & Eysenck, S. B. (2013). The biological basis of personality. In Personality Structure and Measurement (Psychology Revivals) (pp. 49-62). Routledge.
Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. Automated machine learning: Methods, systems, challenges, 3-33.
Furr, R. M., & Funder, D. C. (2004). Situational similarity and behavioral consistency: Subjective, objective, variable-centered, and person-centered approaches. Journal of Research in Personality, 38(5), 421-447.
Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68- 84.
Harris, J. R. (1995). Where is the child's environment? A group socialization theory of development. Psychological review, 102(3), 458.
Horstmann, K. T., & Ziegler, M. (2020). Assessing personality states: What to consider when constructing personality state measures. European Journal of Personality, 34(6), 1037- 1059.
Jalili, S., & Mall-Amiri, B. (2015). The difference between extrovert and introvert EFL teachers' classroom management. Theory and Practice in Language Studies, 5(4), 826.
Jeronimus, B. F., Riese, H., Sanderman, R., & Ormel, J. (2014). Mutual reinforcement between neuroticism and life experiences: a five-wave, 16-year study to test reciprocal causation. Journal of personality and social psychology, 107(4), 751.
Kawamoto, R., Ninomiya, D., Kasai, Y., Kusunoki, T., Ohtsuka, N., Kumagi, T., & Abe, M. (2016). Factors associated with the choice of general medicine as a career among Japanese medical students. Medical Education Online, 21(1), 29448.
Kweik, O. M. A., Hamid, M. A. A., Sheqlieh, S. O., Abu-Nasser, B. S., & Abu-Naser, S. S. (2020). Artificial Neural Network for Lung Cancer Detection. International Journal of Academic Engineering Research (IJAER),4(11).
Marbouti, F., Diefes-Dux, H. A., & Madhavan, K. (2016). Models for early prediction of at-risk students in a course using standards-based grading. Computers & Education, 103, 1-15.
Matthews, G. (2009). 23 Personality and performance: cognitive processes and models. The Cambridge handbook of personality psychology, 400.
Mayilvaganan, M., & Kalpanadevi, D. (2014, December). Comparison of classification techniques for predicting the performance of students academic environment. In 2014 International Conference on Communication and Network Technologies (pp. 113-118). IEEE.
McAdams, D. P., & Pals, J. L. (2006). A new Big Five: fundamental principles for an integrative science of personality. American psychologist, 61(3), 204.
Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51.
Nix, R., & Zhang, J. (2017). Classification of Android apps and malware using deep neural networks. 2017 International joint conference on neural networks (IJCNN).
Park, N. (2004). The role of subjective well-being in positive youth development. The annals of the American academy of political and social science, 591(1), 25-39.
Ranjeeth, S., Latchoumi, T., & Paul, P. V. (2020). A survey on predictive models of learning analytics. Procedia Computer Science, 167, 37-46.
Revelle, W.R. (2017). PSYCH: Procedures for personality and psychological research. Software.
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2013). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135- 146.
Sadler-Smith, E. (2016). The role of intuition in entrepreneurship and business venturing decisions. European Journal of Work and Organizational Psychology, 25(2), 212-225.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
Strecht, P., Cruz, L., Soares, C., & Mendes-Moreira, J. (2015). A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance. International Educational Data Mining Society.
Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013, August). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 847-855).
Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405.
Wirth, R., & Hipp, J. (2000, April). CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (Vol. 1, pp. 29-39).
Wong, B. T. M. (2017). Learning analytics in higher education: an analysis of case studies. Asian Association of Open Universities Journal, 12(1), 21-40.
Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering, 89, 106903.