Bayesian Model Average for Student Learning Location
DOI:
https://doi.org/10.13052/jicts2245-800X.10211Keywords:
Bayesian Model Selection, Students' perception, Price perception, perception of university, city, student, learning locationAbstract
The paper was conducted to understand the factors affecting the student’s learning location. The official study carried out an online survey through Google forms using a questionnaire with the participation of 125 samples. The Bayesian Model Selection shows that 03 factors are affecting student studying location (SSL), which are Students’ perception (PP), Price perception (PRI), Perception of universities in a big city (UNI). From the results, we have proposed many implications for improving student learning. This study uses the optimal choice of Bayesian Model Selection for the student learning location. Students’ perceptions (PP), price perceptions (PRI), and university perceptions in big cities (UNI) all have a 97.1 percent impact on student studying places (SSL). Model 1 is the best option by BIC, and four variables have a probability of 100%.
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Daivietsaigon. (2021). Why choose Ho Chi Minh City to study (Vietnamese)? Available: https://daivietsaigon.edu.vn/dao-tao/bai-viet/vi-sao-chon-thanh-pho-hcm-hoc-tap-dvsg-1350.html
N. Yusya and A. A. Ariyanto, ”Understanding Civil Behavior from Jakarta Urban Citizens: Study on High and Low Education Level Group,” presented at the International Conference on Psychology, 2020.
A. Wang, S. Wang, and X. Ye, “Religion and Motivated Cognition: When Ramadan Meets the College Entrance Exam,” ed, 2018.
C. Wright, “Choose Wisely: A Study of College Major Choice and Major Switching,” The Pardee RAND Graduate School, 2018.
P. Belló, “The Importance of New Paths in Mentoring for Graduate Students in Their Search for Academic Opportunities After Graduation: An Auto-Ethnography,” in From Student to Scholar, ed: Palgrave Macmillan, Cham, 2020, pp. 103–122.
D. Roberts, “Making decisions as a student: decision-making opportunities,” Nursing: decision-making skills for practice, pp. 20–34, 2013.
S. E. Jackson, R. L. Schwab, and R. S. Schuler, “Toward an understanding of the burnout phenomenon,” Journal of applied psychology, vol. 71, p. 630, 1986.
P. Kotler, K. L. Keller, S. H. Ang, C. T. Tan, and S. M. Leong, Marketing management: an Asian perspective: Pearson Education, 2021.
N. Chamhuri and P. J. Batt, “Consumer perceptions of food quality in Malaysia,” British Food Journal, 2015.
J. M. Dukert, “It depends on where you live,” in High Energy Costs, ed: Routledge, 2015, pp. 57–76.
G. B. Turna, “Being a university student in a small city: students’perception of rize/turkey,” VFAST Transactions on Education and Social Sciences, vol. 3, pp. 138–140, 2015.
B. Tabachnick and L. Fidell, “Using multivariate statistics. 4th edn.: 139–179,” New York: HarperCollins, 2001.
T. Bayes, “LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S,” Philosophical transactions of the Royal Society of London, pp. 370–418, 1763.
L. D. Thang, “The Bayesian statistical application research analyzes the willingness to join in area yield index coffee insurance of farmers in Dak Lak province,” University of Economics Ho Chi Minh City, 2021.
A. Gelman and C. R. Shalizi, “Philosophy and the practice of Bayesian statistics,” British Journal of Mathematical and Statistical Psychology, vol. 66, pp. 8–38, 2013.
A. E. Raftery, “Bayesian model selection in social research,” Sociological methodology, pp. 111–163, 1995.
A. E. Raftery, D. Madigan, and J. A. Hoeting, “Bayesian model averaging for linear regression models,” Journal of the American Statistical Association, vol. 92, pp. 179–191, 1997.
N. T. Ngan and B. H. Khoi, “Consumer’s Organic Food Buying Intention in COVID-19 Pandemic: Evidence from Vietnam,” in The International Conference on Information, Communication & Cybersecurity, 2021, pp. 345–353.
N. T. Ngan and B. H. Khoi, “BIC Algorithm for Heineken Brand Awareness in Vietnam Market,” in International Conference on Innovations in Bio-Inspired Computing and Applications, 2021, pp. 3–14.
D. Kaplan, “On the Quantification of Model Uncertainty: A Bayesian Perspective,” Psychometrika, vol. 86, pp. 215–238, 2021.
