Ersin KIRAL, Can MAVRUK, Gülsen KIRAL

EKONOMETRİ ÖĞRENCİLERİNİN SAYISAL DERSLERDEKİ AKADEMİK PERFORMANSI: MARKOV MODELİ İLE BİR HESAPLAMA

Bu makale Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Ekonometri bölümü üst sınıf öğrencilerinin sayısal derslerdeki başarısını araştırmaktadır. Matematiksel İktisat 1-2, Ekonometri 1-2 ve Zaman Serisi 1-2 derslerindeki başarıyı ölçmek için Markov modeli kullanılmıştır. Bu amaçla başarının kalıcılığı ve derslerin etkenliği geçiş olasılıkları matrisinden hesaplanmıştır. Ekonometri Türkçe öğretimde başarıda kalıcılığı ve etkenliği en yüksek olan iki ders, Ekonometri II üzerinde, sırasıyla Mikro İktisat II ve Mikro İktisat I olarak bulunmuştur. Uzun vadede, Ekonometri bölümü Türkçe öğretimde başarının ilerleme olasılığının ve İngilizce öğretimde başarının düşme olasılığının en yüksek olacağı ders Ekonometri II olarak bulunmuştur. Kısa vadede başarıda kalıcılığın Türkçe öğretimde Mikro İktisat II’den Ekonometri I’e geçişte, İngilizce öğretimde ise İstatistik derslerinden Zaman Serisi I dersine geçişte en düşük olduğu sonucuna ulaşılmıştır.

Anahtar Kelimeler:

Ekonometri, Yüksek öğrenim, Markov modeli, ki kare testi

ACADEMIC PERFORMANCE OF ECONOMETRICS STUDENTS IN QUANTITATIVE COURSES: A PREDICTION USING MARKOV MODEL

This article investigates the academic performance of junior and senior students in quantitative courses at Econometrics Department of Cukurova University. Markov model is used to measure the success in core quantitative courses such as Mathematical Economics, Econometrics and Time Series. To this end, persistence and effectiveness of success are estimated from transition probability matrix. We have found that Micro Economics II and Micro Economics I have the highest persistence and effectiveness over Econometrics II in Turkish program. In the long run, Econometrics II has the highest probability of improvement and of decline in academic progress in Turkish program and English program respectively. In the short run, persistence in success is the lowest in transition from Micro Economics II to Econometrics I and from Statistics to Time Series I in Turkish and English program respectively.

Keywords:

Econometrics, Higher education, Markov model, Chi Square tests,

PDF

___

Baasch, A., Tischew, S. ve Bruelheide, H. (2010). Twelve years of succession on sandy substrates in a post-mining landscape: A Markov chain analysis, Ecological Applications, 20(4), 1136-1147.
Cavers, M.S. ve Vasudevan, K. (2015). Brief Communication: Earthquake sequencing: Analysis of Time Series constructed from the Markov Chain Model, Nonlinear Process Geophysics, 22, 589-599.
Farg, M. H. M. ve Khalil, F. M. H. (2015). Statistical Analysis of Academic Level of Student in Quantitative Methods Courses By Using Chi-Square Test and Markov Chains - Case Study of Faculty of Sciences and Humanities (Thadiq). Nat Sci,12(12), 182- 186.
Grimshaw, S.D. ve Alexander, W.P. (2011). Markov Chain Models for Deliquency: Transition Matrix Estimation and Forecasting, John Wiley&Sons, Applied Stochastic Models in Business and Industry, 27, 267-279.
Industrial Engineering Operations Research. (2008). Derman, E., Park, K.S. ve Whitt, W. http://ieor.columbia.edu/files/seasdepts/industrial-engineering-operations-research/pdf-files/Park_Informs.pdf
Lazri, M., Ameur, S., Brucker, J.M., Lahdir, M. ve Sehad, M. (2015). Analysis of drought areas in northern Algeria using Markov chains, J. Earth Syst. Sci., 124(1), 61–70.
Lukić, P.,Gocić, M. ve Trajković, S. (2013). Prediction of annual precipitation on the territory of south Serbia using Markov chains, Bulletin of the Faculty of Forestry, 108, 81-92.
Mavruk, C. ve Kıral, E. (2016). Academic progress of students in quantitative courses at Nigde University Vocatıonal School of Social Sciences: A predictıon using Markov model, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 9(3), 267-276.
Mieruch, S., Noel, S., Bovensmann, H., Burrows, J. P. ve Freund, J. A. (2010). Markov chain analysis of regional climates. Nonlinear Processes in Goephysics, 17, 651–661.
Ng, W. S. (2013). Persistence of Mutual Fund Ratings: A Markov Chain Approach. Indian Finance Conference: Econometrics February 2013, Singapore Management University, Singapore. http://ink.library.smu.edu.sg/etd_coll/99
Pehkonen, J. ve Tervo, H. (1998). Persistence and turnover ın regional unemployment disparities, Regional Studies, 32(5), 445-458.
Serfozo, R. (2009). Basics of Applied Stochastic Processes,(1st Edition). Berlin Heidelberg: Springer-Verlag.
Usher, M.B. (1979). Markovian Approaches to Ecological Succession, Journal of Animal Ecology, 48(2), 413-426.
Yeh, H.W., Chan, W., Symanski, E. ve Davis, B.R. (2010). Estimating Transition Probabilities for Ignorable Intermittent Missing Data in a Discrete-Time Markov Chain, Communications in Statistics- Simulation and Computation, 39(2), 433-448.