A machine learning based approach for mapping personality traits and perceived stress scale of undergraduate students
Автор: Ahmed A. Marouf, Adnan F. Ashrafi, Tanveer Ahmed, Tarikuzzaman Emon
Журнал: International Journal of Modern Education and Computer Science @ijmecs
Статья в выпуске: 8 vol.11, 2019 года.
Бесплатный доступ
This paper focuses on the personality traits of students and stress scale they had to face in undergraduate level. With the advancement of computer science and machine learning based applications, we have tried to inter-correlate the terms. In the area of computational psychology, it is important to understand participants’ psychological behavior using personality traits and predict how he/she is going to react on a certain level of the stressed situation. For the experiment, we have collected data of around 150 participants. The personality traits data are collected using the standard survey named The Big Five Personality Test created by IPIP organization and stress scale measurements are collected using scale devised by Sheldon Cohen named as Perceived Stress Scale hosted by Mind garden. The data are taken from Bangladeshi computer science undergraduate students and kept anonymous. In this paper, we have applied nine different machine learning based classification models are built for mapping the traits with stress scales. For performance evaluation, we have utilized precision, recall, f1-score, and accuracy. From the experimental findings, we found that Sequential Minimal Optimization (SMO) and k-NN classifier gives the highest prediction accuracy which is approximately 70%.
Big Five Personality Traits, Perceived Stress Scale (PSS), Machine learning Approach, Data mining, Sequential Minimal Optimization (SMO)
Короткий адрес: https://sciup.org/15016872
IDR: 15016872 | DOI: 10.5815/ijmecs.2019.08.05
Список литературы A machine learning based approach for mapping personality traits and perceived stress scale of undergraduate students
- S. Rothmann, E. P. Coetzer, "The big five personality dimensions and job performance". SA Journal of Industrial Psychology. 29. doi:10.4102/sajip.v29i1.88. Retrieved 27 June 2013.
- S. Cohen, T. Kamarck, R. Mermelstein, “A global measure of perceived stress”, Journal of Health and Social Behavior, 24, 386-396, 1983.
- S. Cohen, G. Williamson, “Perceived Stress in a Probability Sample of the United States. Spacapan, S. and Oskamp, S. (Eds.) The Social Psychology of Health. Newbury Park, CA, Sage, 1988.
- D. Quercia, M. Kosinski, D. Stillwell, J. Crowcroft, “Our twitter profiles, our selves: Predicting personality with twitter.”, In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third International Conference on Social Computing (SocialCom), pp. 180–185, 2011.
- P. Howlader, K. K. Pal, A. Cuzzocrea, S. D. M. Kumar, “Predicting facebook-users' personality based on status and linguistic features via flexible regression analysis techniques”, Proceeedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France, pp. 339- 345, April 09-13, 2018.
- T. Tandera, D. Suhartono, R. Wong so, Y. L. Presidio, “Personality prediction system from Facebook users.” Procardia Computer Science, vol. 116, pp. 604–611, 2017.
- M. Kosinski, S. Mats, S. Gosling, V. Popov, D. Stillwell, “Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines.” American Psychologist, 2015 Feb; 70(6): pp. 543.
- M. Kosinski, D. Stillwell, T. Grapple, “Private traits and attributes are predictable from digital records of human behavior”, In Proceedings of the National Academy of Sciences of the United States of America; 2013: PNAS. pp. 5802-5805.
- P. Holder, K. K. Pal, A. Cuzzocrea, S. D. M. Kumar, “Predicting Facebook-users' personality based on status and linguistic features via flexible regression analysis techniques”, SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 339-345, Pau, France, April, 2018.
- V. Kaushal, M. Patwardhan, “Emerging trends in personality identification using online social networks-A literature survey”, ACM Transactions on Knowledge Discovery from Data, vol. 12, no. 2, Article. 15, January 2018.
- I. Rish, “An empirical study of the naive Bayes classifier”, In Proceedings of IJCAI-01 workshop on Empirical Methods in AI, pp. 41–46, Sicily, Italy, 2001.
- S. R. Safavian, D. Landgrebe, “A survey of decision tree classifier methodology”, IEEE Transactions on Systems, Man and Cybernetics, pp. 660–674, 1991.
- L. Breiman, “Random forests”, Machine Learning, 45:5– 32, 2001
- Niels Landwehr, Mark Hall, Eibe Frank, “Logistic Model Trees”, pp. 161-205, vol. 95, 2005.
- R. A. Fisher, “The Use of Multiple Measurements in Taxonomic Problems”, Annals of Eugenics. vol. 7, issue. 2, pp. 179–188, 1936.
- L. Breiman. Bagging predictors. Machine Learning, vol. 24 issue. 2, pp. 123–140, 1996.
- R. Collobert, S. Bengio, “Links between Perceptrons, MLPs and SVMs”. Proceedings of International Conference on Machine Learning (ICML), 2004.
- J. C. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines”, Technical Report MSR-TR_98_14, Microsoft Research, 1998.
- N. S. Altman, “An introduction to kernel and nearest- neighbor nonparametric regression”, The American Statistician, vol. 46, issue. 3, pp. 175–185, 1992.
- G. Holmes, A. Donkin, I.H. Witten, “Weka: A machine learning workbench”, Proceedings of Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia.
- M. M. Hasan, N. H. Shaon, A. A. Marouf, M. K. Hasan, H. Mahmud, and M. M. Khan, “Friend Recommendation Framework for Social Networking Sites using User‘s Online Behavior”, IEEE- Computer and Information Technology (ICCIT), December 2015, pp. 539-543.
- A. Denovan, N. Dagnall, K. Dhingra and S. Grogan, “Evaluating the Perceived Stress Scale among UK university students: implications for stress measurement and management”, Journal of Studies in Higher Education, Vol. 44, Issue. 1, pp. 120-133, 2019.
- K. Kumar, S. Kuma, D. Mehrotra, S. C. Tiwari, V. Kumar and R. C. Dwivedi, “Reliability and psychometric validity of Hindi version of Depression, Anxiety and Stress Scale- 21 (DASS-21) for Hindi speaking Head Neck Cancer and Oral Potentially Malignant Disorders Patients”, The Journal of Cancer Research and Therapeutics, January, 2019.
- A. A. Marouf, M. K. Hasan, H. Mahmud, “Identifying Neuroticism from User Generated Content of Social Media based on Psycholinguistic Cues”, 2019 2nd IEEE Conference on Electrical, Computer and Communication Engineering (ECCE 2019), CUET, 7-9 February, 2019.
- Muhammad U. Fahri, Sani M. Isa, " Data Mining to Prediction Student Achievement based on Motivation, Learning and Emotional Intelligence in MAN 1 Ketapang", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 53-60, 2018.DOI: 10.5815/ijmecs.2018.06.07
- C.Bhanuprakash, Y.S. Nijagunarya, M.A. Jayaram, " An Informal Approach to Identify Bright Graduate Students by Evaluating their Classroom Behavioral Patterns by Using Kohonen Self Organizing Feature Map ", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.8, pp. 22-32, 2018.DOI: 10.5815/ijmecs.2018.08.03
- Bekim Fetaji, Majlinda Fetaji, Mirlinda Ebibi, Samet Kera, " Analyses of Impacting Factors of ICT in Education Management: Case Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.2, pp. 26-34, 2018.DOI: 10.5815/ijmecs.2018.02.03
- Abimbola R. Iyanda, Olufemi D. Ninan, Anuoluwapo O. Ajayi, Ogochukwu G. Anyabolu, " Predicting Student Academic Performance in Computer Science Courses: A Comparison of Neural Network Models", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.6, pp. 1-9, 2018.DOI: 10.5815/ijmecs.2018.06.01
