Personalized Recommendation Classification Model of Students’ Social Well-being Based on Personality Trait Determinants Using Machine Learning Algorithms
DOI:
https://doi.org/10.32890/jict2023.22.4.2Keywords:
WEKA, Personality Traits, Social Well-being, Data Mining, Classification AlgorithmsAbstract
The global trend of student social well-being has steadily declined in recent years. As a result, the need for a personalized recommendation classification model that can accurately assess and identify the individual student’s social well-being has become increasingly important. This article will discuss the development of an adaptive personalized recommendation classification model for students’ social well-being based on personality trait determinants. Social well-being is a field that analyses society, individual behavioural patterns, behavioural networks, and cultural elements of daily life. Social well-being develops critical thinking by understanding the social frameworks that affect humans by exposing the social basis of daily actions. For instance, when students are pleased, their academic achievement, behaviour, social integration, and happiness improve. This study classifies the effects of the Big 5 Personality Traits (Extraversion, Openness, Agreeableness, Emotional Stability, and Conscientiousness) on students’ Industry 4.0 Social Well-being levels by analyzing their demographic and personality traits. A dataset was gathered through a survey distributed to students in a selected institution. The classifier’s accuracy was assessed using the WEKA tool on a data set of 286 occurrences and 19 traits, and a confusion matrix was constructed. After analyzing the results of all algorithms, it was determined that the IBk and Randomizable Filtered Classifier algorithms give the best accuracy on social well-being readiness, with a comparable percentage value of 91.26%. The agreeableness personality trait, which represents a person’s level of pleasantness, politeness, and helpfulness, had the greatest influence on the social well-being of the students. They have a positive outlook on human behaviour and get along well with others. Since social well-being contributes to a person’s increased quality of life and happiness, improving students’ current quality of life would lead to the development of a social parameter that can assess the growth of a country and the increased happiness of
families and communities. Personality traits models have become an increasingly important tool for understanding and predicting human behavior. By analyzing different personality trait models, we can gain insights into how accurately and reliably they can predict individual behavior. This is especially useful in fields such as psychology, marketing, and recruitment, where understanding the nuances of individual personalities can be critical to success. In this study, how different personality trait models compare in terms of accuracy and reliability is explored using different machine learning algorithms using the WEKA tool. Personality trait models are increasingly being used to measure social well-being. This model is based on the idea that individuals’ personalities are composed of a set of underlying traits which can be measured and compared. By understanding these traits, we can better understand the students’ social well-being and how the environment around them may impact it.
References
Abdelhamid, E. A., Ismail, S. S., & Aref, M. M. (2023). Architecture for personality detection using enneagram knowledge: Case study. International Journal of Intelligent Computing and Information Sciences, 23(1), 18-28. https://doi.org/10.21608/ ijicis.2023.137912.1181
Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37(April), 13-49. https://doi.org/10.1016/j.tele.2019.01.007
Amirhosseini, M. H., & Kazemian, H. (2020). Machine learning approach to personality type prediction based on the Myers– Briggs Type Indicator®. Multimodal Technologies and Interaction, 4(1), 9. https://www.mdpi.com/2414-4088/4/1/9
Anderson, J., & Rainie, L. (April 2018). The Future of Well-being in a Tech-Saturated World. PEW Research Center, Washington, USA. https://www.pewresearch.org/internet/2018/04/17/the-future-of-well-being-in-a-tech-saturated-world/
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.
Ashton, M. C., & Lee, K. (2020). Recovering the HEXACO Personality factors – and psychoticism – from variable sets assessing normal and abnormal personality. Journal of Individual Differences, 41(2), 68-77. https://doi.org/10.1027/1614-0001/a000305
Bakar, A. A., Osman, M. M., Bachok, S., & Ibrahim, M. (2016). Investigating rationales of Malaysia quality of life and well-being components and indicators. Procedia-Social and Behavioral Sciences, 222 (June), 132-142. https://cyberleninka. org/article/n/675796.pdf Journal of ICT, 22, No. 4 (Oct) 2023, pp: 545-
Bleidorn, W., & Hopwood, C. J. (2019). Using machine learning to advance personality assessment and theory. Personality and Social Psychology Review, 23(2), 190-203.Cervone, Cervone, D., D., && Pervin, Pervin, L. L. A. A. (2(2
Cervone, D., & Cervone, Pervin, L.D.,A.& (2018). Pervin, Personality: L. A. (2018). Personality: Theory and Theory and Wiley. Research. Research. Wiley. Research. Research. Wiley. Wiley. https://www.wiley.com/en-us/ https://www.wiley.com/en-us/Personality%3A+Theory+an us/Personality%3A+Theory+and p-9781119492061 us/Personality%3A+Theory+and+Research%2C+14th+Edition-p-9781119492061 Clarke, p-9781119492061 E., Hull, L., Loomes, R., McCormick, C. E., Sheinkopf, S. J., & Mandy, W. (2021). Assessing gender differences in autism spectrum disorder using the Gendered Autism Behavioral Scale (GABS): An exploratory study. Research in Autism Spectrum Disorders, 881(October), 101844.
Cobb-Clark, D., & Schurer, S. (2012). The stability of Big-Five personality traits. Economics Letters, 115(1), 11-15. https://EconPapers.repec.org/RePEc:eee:ecolet:v:115:y:2012:i:1 :p:11-15
Demong, N. A. R., Shahrom, M., Omar, E. N., Abdul Rahim, R., & Yahya, M. (2021). Industry 4.0 readiness assessment tool: A conceptual framework from social well-being perspective. Revista Română de Informatică și Automatică, 31(1), 53-64. https://doi.org/10.33436/v31i1y202104
Doost-Mohammadian, H., & Rezaie, F. (2020). The role of IoE-Education in the fifth wave theory readiness & its effect on SME 4.0 HR competencies. In 2020 IEEE Global Engineering Education Conference (EDUCON), 1604-1613. https://doi.org/10.1109/EDUCON45650.2020.9125249
Drayton-Brooks, S. M., Gray, P. A., Turner, N. P., & Newland, J. A. (2020). The use of big data and data mining in nurse practitioner clinical education. Journal of Professional Nursing, 36(6), 484-489. https://doi.org/10.1016/j.profnurs.2020.03.012
Fazio, R. H., Ruisch, B. C., Moore, C. A., Granados-Samayoa, J. A., Boggs, S. T., & Ladanyi, J. T. (2021). Social distancing decreases an individual’s likelihood of contracting COVID-19. Proceedings of the National Academy of Sciences, 118(8), e2023131118.
Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43, 1-15.
Gubler, D. A., Makowski, L. M., Troche, S. J., & Schlegel, K. (2021). Loneliness and well-being during the Covid-19 pandemic: Associations with personality and emotion regulation. Journal 505 of Happiness Studies, 22, 2323-2342. Journal of ICT, 22, No. 4 (Oct) 2023, pp: 545-
Hadziahmetovic, N., & Mujezinovic, M. (2021). The role of the Big Five personality profiles in employee affective commitment and engagement: Case of small and medium enterprises. International Journal of Business and Administrative Studies, 7(2), 74-88. https://doi.org/10.20469/ijbas.7.10003-2
Hansen, B. G., Bugge, C. T., & Skibrek, P. K. (2020). Automatic milking systems and farmer well-being–exploring the effects of automation and digitalization in dairy farming. Journal of Rural Studies, 80, 469-480. https://doi.org/10.1016/j. jrurstud.2020.10.028
Hollebeek, L. D., & Belk, R. (2021). Consumers’ technology-facilitated brand engagement and well-being: Positivist TAM/PERMA-vs. Consumer Culture Theory perspectives. International Journal of Research in Marketing, 38(2), 387-401. https://doi.org/10.1016/j.ijresmar.2021.03.001
Ismail, N. H., Ahmad, F., & Aziz, A. (2013). Implementing WEKA as a data mining tool to analyze students’ academic performances using Naïve Bayes classifier. https://doi.org/10.13140/2.1.4937.8565
Jani, D., & Han, H. (2015). Influence of environmental stimuli on hotel customer emotional loyalty response: Testing the moderating effect of the Big Five personality factors. International Journal of Hospitality Management, 44, 48-57. https://doi.org/10.1016/j.ijhm.2014.10.006
Jerath, R., & Beveridge, C. (2020). Respiratory rhythm, autonomic modulation, and the spectrum of emotions: the future of emotion recognition and modulation. Frontiers in Psychology, 11, 1980.
Joshanloo, M., Rastegar, P., & Bakhshi, A. (2012). The Big Five personality domains as predictors of social well-being in Iranian university students. Journal of Social and Personal Relationships. 29(5). https://doi.org/10.1177/0265407512443432
Kim, S., & Kim, S. (2020). Analysis of the impact of health beliefs and resource factors on preventive behaviors against the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 17(22), 8666.
Lavy, S. (2020). A review of character strengths interventions in twenty-first-century schools: Their importance and how they can be fostered. Applied Research in Quality of Life, 15, 573-596.
Magsamen-Conrad, K., Billotte-Verhoff, C., & Greene, K. (2014). Technology addiction’s contribution to mental well-being: The positive effect of online social capital. Computers in Human Behavior, 40, 23-30. https://doi.org/10.1016/j.chb.2014.07.014 Journal of ICT, 22, No. 4 (Oct) 2023, pp: 545-
Mairesse, F., Walker, M., Mehl, M., & Moore, R. (2007). Using linguistic cues for the automatic recognition of personality in conversation and text. Journal of Artificial Intelligence Research (JAIR), 30, 457-500. https://doi.org/10.1613/jair.2349
Martínez-Domínguez, M., & Fierros-González, I. (2021). Determinants of Internet use by school-age children: The challenges for Mexico during the COVID-19 pandemic. Telecommunications Policy, 102241. https://doi.org/10.1016/j.telpol.2021.102241
Middlemiss, L., Ambrosio-Albalá, P., Emmel, N., Gillard, R., Gilbertson, J., Hargreaves, T., Mullen, C., Ryan, T., Snell, C., & Tod, A. (2019). Energy poverty and social relations: A capabilities approach. Energy Research & Social Science, 55, 227-235.
Nafie, F., & Hamed, A. (2018). Usage Apriori and clustering algorithms in WEKA tools to mining dataset of traffic accidents. Journal of Information and Telecommunication, 2, 1-15. https://doi.org /10.1080/24751839.2018.1448205
Nunsina, T., & Situmorang, Z. (2020, 25-27 June 2020). Analysis optimization K-Nearest Neighbor algorithm with certainty factor in determining student career. 2020 3rd International Conference on Mechanical, Electronics, Computer, and Industrial Technology (MECnIT), 306 -310. https://doi.org/1010.1109/MECnIT48290.2020.9166669
Parmar, K., Vaghela, D., & Sharma, P. (2015). Performance prediction of students using distributed data mining. 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 1-5. https://doi.org /10.1109/ICIIECS.2015.7192899
Pekruna, R. (2020). Commentary: Self-report is indispensable to assess students’ learning. Frontline Learning Research, 8(3), 185-193.
Radisavljević, D., Batalo, B., Rzepka, R., & Araki, K. (2022). Myers-Briggs type indicator and the Big Five model: How our personality affects language use. 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE).
Razavi, R. (2020). Personality segmentation of users through mining their mobile usage patterns. International Journal of Human-Computer Studies, 143, 102470. https://doi.org/10.1016/j. ijhcs.2020.102470
Robeyns, I. (2020). Well-being, place and technology. Well-being, Space and Society, 1, 100013. https://doi.org/10.1016/j. wss.2020.100013 Journal of ICT, 22, No. 4 (Oct) 2023, pp: 545-
Salehi, A. M. M. P., Marzban, M. M., Sourosh, M. P., Sharif, F. P., Nejabat, M. M., & Imanieh, M. H. M. (2017). Social well-being and related factors in students of School of Nursing and Midwifery. International Journal of Community Based Nursing and Midwifery, 5(1), 82-90.
Shankhdhar, A., Agrawal, A., Sharma, D., Chaturvedi, S., & Pushkarna, M. (2020). Intelligent decision support system using Decision Tree method for student career. 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). https://doi.org/10.1109/ PARC49193.2020.246974
Sima, V., Gheorghe, I., Subić, J., & Nancu, D. (2020). Influences of the Industry 4.0 Revolution on the human capital development and consumer behavior: A systematic review. Sustainability, 12, 4035. https://doi.org/10.3390/su12104035
Simendinger, T., & Stibe, A. (2016). How Increasing Technology Reliance is Influencing Behaviors and Happiness. ECSW@ PERSUASIVE. http://ceur-ws.org/Vol-1817/paper8.pdf
Sudirwan, J., & Pelawi, D. (2019). Indonesian higher education learning strategy facing Industry 4.0: program for implementing the SPOCs learning model. 2019 International Conference on Information Management and Technology (ICIMTech), pp. 548-553. https://doi.org/10.1109/ICIMTech.2019.8843717.
Swartz, B. (2020, 16-19 Nov. 2020). Ethics in Engineering Education 4.0. 2020 IFEES World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC), 112. 181-188.https://www.researchgate.net/publication/355874381_ Ethics_in_Engineering_Education_40_The_Educator%27s_ perspective
Tuzovic, S., & Kabadayi, S. (2021). The influence of social distancing on employee well-being: a conceptual framework and research agenda. Journal of Service Management, 32(2), 145-160.
Uddin, M. F., & Lee, J. (2016). Utilizing relevant academic and personality features from big unstructured data to identify good and bad fit students. Procedia Computer Science, 95, 383-391. https://doi.org/10.1016/j.procs.2016.09.349
Utami, N. A., Maharani, W., & Atastina, I. (2021). Personality classification of Facebook users according to Big Five personality using SVM (Support Vector Machine) Method. Procedia Computer Science, 179, 177-184. https://doi.org/10.1016/j.procs.2020.12.023
Van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3), 1557-1580. Journal of ICT, 22, No. 4 (Oct) 2023, pp: 545-
Villavicencio, C. N., Macrohon, J. J. E., Inbaraj, X. A., Jeng, J.-H., & Hsieh, J.-G. (2021). COVID-19 prediction applying supervised machine learning algorithms with comparative analysis using Weka. Algorithms, 14(7), 201.
Wang, S., & Chen, X. (2020). Recognizing CEO personality and its impact on business performance: Mining linguistic cues from social media. Information & Management, 57(5), 103173. https://doi.org/10.1016/j.im.2019.103173
Widdicks, K. (2020). When the good turns ugly: Speculating next steps for digital well-being tools. Proceedings of the 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, Tallinn, Estonia. https://doi.org/10.1145/3419249.3420117
Yu, Y., Zhao, Y., Li, D., Zhang, J., & Li. J. (2021). Social well-being of Chinese residents: The mediating effect of social support. Frontiers in Psychology, 11, 613659. https://doi.org/10/3389/ fpsyg.2020.613659
Zaidi, N., Wajid, R., Zaidi, S. F., Zaidi, G., & Zaidi, S. (2013). The Big Five personality traits and their relationship with work engagement among public sector university teachers of Lahore. African Journal of Business Management, 7, 1344-1353. https://doi.org/10.5897/AJBM12.290
Zhong, X.-y. (2011). The research and application of web log mining based on the platform Weka. Procedia Engineering, 15, 4073-4078. https://doi.org/10.1016/j.proeng.2011.08.764
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