Determinants of Efficacy of Studying in the Republic Croatia - Comparing Neural Networks and Decision Trees: Research Framework Proposition
Ključne reči:
efficacy of studying, education, neural network, decision treesApstrakt
Rapid technological development and progress lead to the need for better and more efficient education which should prepare the applicant for increasingly flexible labour market. The goal of this research is to create models for prediction of student’s efficacy, compare them, find the key factors that contribute to more efficient studying in the Republic of Croatia, and finally determine how efficient studying is related to first employment. Models will be based on students’ data and hypothesis will be tested using multivariate statistical methods (multiple regressions, Cronbach’s alpha), decision trees and neural networks. Data will be collected by structured questionnaire and will consist of demographic and economic data, information about previous education, attitudes towards learning, and goals after completing studies and information about the first employment. Students’ efficacy will be measured by grade point average in college. This research will try to increase our understanding of how different factors influence students’ performance and how students’ efficacy affects the speed and conditions of finding the first employment.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Reference
Ali, D. M. M. (2013), „Role of data mining in education sector”, International Journal of Computer Science and Mobile Computing, Vol. 2 No. 4, pp. 374-383.
Baradwaj, B. K., Pal, S. (2012), „Mining educational data to analyze students' performance”, Computer Science, Vol. 2 No. 6, pp. 63-69.
Bekele, R., Menzel, W. (2005), „A Bayesian Approach to Predict Performance of a Student (BAPPS): A Case with Ethiopian Students”, Artificial Intelligence and Applications, Vienna, Austria, pp. 189–194.
Cen, H., Koedinger, K., Junker, B. (2006), „Learning Factors Analysis A General Method for Cognitive Model Evaluation and Improvement”, Intelligent Tutoring Systems, Springer Berlin Heidelberg, Vol. 4053, pp. 164–175.
Clark, E. E., & Ramsay, W. (1990), „The importance of family and network of other relationships in children's success in school”, International Journal of Sociology of the Family, pp. 237-254.
Ermisch, J., Francesconi, M., (2001), „Family matters: Impacts of family background on educational attainments”, Economica, pp.137-156.
Feng, M., Heffernan N., Koedinger, K. (2009), „Addressing the assessment challenge with an online system that tutors as it assesses”, User Modelling and User Adapted Interaction, Vol. 3 No. 19, pp. 243–266.
Fu, T.C., Lui, C.L (2007), „Agent-oriented Network Intrusion Detection System Using Data Mining Approaches", International Journal on Agent-Oriented Software Engineering, Vol. 1 No. 2, pp. 158-174.
Garcez, C., (2007), „The Effects of Career Goals on Students”, available at: http://aplawrence.com/Misc/cgcareergoals.html (4/10/2015)
Gross, J. P., Hossler, D., Ziskin, M. (2007), „ Institutional aid and student persistence: An analysis of the effects of institutional financial aid at public four-year institutions”, Journal of Student Financial Aid, Vol. 1 No. 37, pp. 6.
Himelstein, H. C. (1992), „Early identification of high-risk students-using noncognitive indicators”, Journal of College Student Development, Vol.1 No. 33, pp. 89-90.
Ishitani, T. (2006), „Studying attrition and degree completion behaviour among first-generation college students in the United States”, Journal of Higher Education, Vol. 5 No. 77, pp. 861-885.
Johnes, G., McNabb, R. (2004), „Never give up on the good times: Student attrition in the UK”, Oxford Bulletin of Economics & Statistics, Vol.1 No. 66, pp. 23- 47.
Lecompte, D., Kaufman, L., Rousseeuw, P., Tassin, A. (1983), „Search for the relationship between academic performance and some psychosocial factors”, Acta Psychiatria Belgia, pp. 598-608.
Magdalena, S. M. (2013), „Research Directions on the Psychosocial Predictors of Education Success at the Beginning of School”, Procedia-Social and Behavioural Sciences, Vol. 1 No. 76, pp. 785-789.
McInnis, C., James, R. H., McNaught, C. (1995), „First year on campus: Diversity in the initial experiences of Australian undergraduates”, AGPS for Centre for the Study of Higher Education, University of Melbourne.
Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., Punch, W. (2003), „Predicting student performance: an application of data mining methods with an educational web-based system”, Frontiers in education, Vol. 1 No. 33, pp. T2A-13.
Ortiz, E. A., Dehon, C. (2008), „What are the factors of success at university? A case study in Belgium”, CESifo Economic Studies, Vol. 2 No. 54, pp.121-148.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., Carlstrom, A. (2004), „Do psychosocial and study skill factors predict college outcomes?”, Psychological bulletin, Vol. 2 No. 130, pp. 261.
Romero, C., Ventura, S., (2013), „WIREs Data Mining KnowlDiscov", Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol.1 No. 3, pp. 12–27.
Singell, L. D. (2004), „Come and stay a while: does financial aid effect retention conditioned on enrolment at a large public university?”, Economics of Education Review, Vol.5 No. 23, pp.459-471.
St.John, E. P., Paulsen, M. B., Carter, D. F. (2005), „Diversity, college costs, and postsecondary opportunity: An examination of the financial nexus between college choice and persistence for African Americans and Whites”, The Journal of Higher Education, Vol. 5 No. 76, pp. 545-569.
Stratton, L., O’Toole, D., Wetzel, J. (2008), „A multinomial logit model of college stopout and dropout behaviour”, Economics of Education Review, Vol. 3 No. 27, pp. 319-331.
Sulaiman, A., Mohezar, S. (2006), „Student success factors: Identifying key predictors”, Journal of Education for Business, Vol. 6 No. 81, pp. 328-333.
Thai-Nghe, N., Busche A., Schmidt-Thieme L. (2009), „Improving Academic Performance Prediction by Dealing with Class Imbalance”, Proceeding of 9th IEEE International Conference on Intelligent Systems Design and Applications (ISDA’09), Pisa, Italy, IEEE Computer Society,pp. 878–883.
Thai-Nghe, N., Janecek, P., Haddawy, P. (2007), „A Comparative Analysis of Techniques for Predicting Academic Performance”, in: Proceeding of 37th IEEE Frontiers in Education Conference (FIE’07), Milwaukee, USA, IEEE Xplore, T2G7–T2G12,
Ventura, S., Espejo P. G., Hervs, C. (2008), „Data Mining Algorithms to Classify Students”, in: 1st International Conference on Educational Data Mining (EDM’08), Montral, Canada, pp.8–17.
Wise, D. A. (1975), „Academic achievement and job performance”, The American Economic Review, pp. 350-366.
Zekić-Sušac, M., Frajman-Jakšić, A., Drvenkar, N. (2009), „Neuronske mreže i stabla odlučivanja za predviđanje uspješnosti studiranja” (“Neural Nets and Decision Trees used for Prediction of Success of Studying”), Ekonomski Vjesnik/Econviews: Review of contemporary business, entrepreneurship and economic issues, Vol. 2 No.22, pp. 314-327.
