Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Year 2018, Volume: 5 Issue: 3, 491 - 509, 19.09.2018

Ergün Akgün , Metin Demir

https://doi.org/10.21449/ijate.444073

Cited By: 4

Abstract

In this study, it was aimed to predict elementary education teacher candidates’ achievements in “Science and Technology Education I and II” courses by using artificial neural networks. It was also aimed to show the independent variables importance in the prediction. In the data set used in this study, variables of gender, type of education, field of study in high school and transcript information of 14 courses including end-of-term letter grades were collected. The fact that the artificial neural network performance in this study was R=0.84 for the Science and Technology Education I course, and R=0.84 for the Science and Technology Education II course shows that the network performance overlaps with the findings obtained from the related studies.

Keywords

Elementary Education, Science and Technology Teaching, Data Mining, Artificial Neural Networks

Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks

Abstract

In this study, it was aimed to predict
elementary education teacher candidates’ achievements in “Science and
Technology Education I and II” courses by using artificial neural networks. It
was also aimed to show the independent variables importance in the prediction.
In the data set used in this study, variables of gender, type of education,
field of study in high school and transcript information of 14 courses
including end-of-term letter grades were collected. The fact that the
artificial neural network performance in this study was R=0.84 for the Science
and Technology Education I course, and R=0.84 for the Science and Technology
Education II course shows that the network performance overlaps with the
findings obtained from the related studies.

Keywords

Elementary Education, Science and Technology Teaching, Data Mining, Artificial Neural Networks

References

• Açıl, Ü. (2010). Öğretmen adaylarının akademik başarıları ile KPSS puanları arasındaki ilişkinin çeşitli değişkenler açısından incelenmesi [Examination of the relationship between academic success of teacher candidates and civil servant selection examination (KPSS) scores, in terms of different variables (Master’s Thesis)]. Mustafa Kemal Üniversitesi Sosyal Bilimler Enstitüsü, Hatay.
• Adıyaman, F. (2007). Talep tahmininde yapay sinir ağlarının kullanılması [Sales forecasting using artifical neural networks (Master’s Thesis)]. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
• Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), 65-74. doi:10.1016/s0957-4174(99)00053-6
• Alam, M., & Briggs, A. (2011). Artificial neural network meta-models in cost-effectiveness analysis of intensive blood-glucose control: A case study applied to the UK prospective diabetes study (Ukpds) individual patient outcome simulation model. Value in Health, 14(7), 234-235.
• Anemone, R., Emerson, C., & Conroy, G. (2011). Finding fossils in new ways: An artificial neural network approach to predicting the location of productive fossil localities. Evol Anthropol, 20(5), 169-180. doi:10.1002/evan.20324
• Anıl, D. (2009). Uluslararası öğrenci başarılarını değerlendirme programı (PISA)’nda Türkiye’deki öğrencilerin fen bilimleri başarılarını etkileyen faktörler [Factors effecting science achievement of science students in programme for international students’ achievement (PISA) in Turkey]. Eğitim ve Bilim, 34(152), 87-100.
• Arbib, M. A. (1987). Brains, machines, and mathematics. New York: Springer-Verlag.
• Arbib, M. A. (2003). The handbook of brain theory and neural networks. Cambridge, Massachusetts, London, England: MIT press.
• Ayık, Y. Z., Özdemir, A. ve Yavuz, U. (2007). Lise türü ve lise mezuniyet başarısının kazanılan fakülte ile ilişkisinin veri madenciliği tekniği ile analizi [An analysis of the correlation between the type of high school and high school graduation score and university placement examination using data mining technique.]. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(2), 441-454.
• Azadeh, A., Saberi, M., & Anvari, M. (2011). An integrated artificial neural network fuzzy C-means-normalization algorithm for performance assessment of decision-making units: The cases of auto industry and power plant. Computers & Industrial Engineering, 60(2), 328-340. doi:10.1016/j.cie.2010.11.016
• Bahadır, E. (2013). Yapay sinir ağları ve lojistik regresyon analizi yaklaşımları ile öğretmen adaylarının akademik başarılarının tahmini [Prediction of student teachers' academic success with logistic regression analysis and artificial neural networks methods (Doctoral Thesis)]. Marmara Üniversitesi Eğitim Bilimleri Enstitüsü, İstanbul.
• Bahar, H. H. (2011). ÖSS puanı ve lisans mezuniyet notunun KPSS 10 puanını yordama gücü [KPSS 10 score prediction power of bachelor graduation mark and OSS Score]. Education and Science, 36(162), 168-181.
• Baştürk, R. (2008). Fen ve teknoloji alanı öğretmen adaylarının kamu personeli seçme sınavı başarılarının yordanması [Predictive validity of the science and technology pre-service teachers’ civil servant selection examination]. İlköğretim Online, 7, 323-332.
• Berberoğlu, G., Çelebi, Ö., Özdemir, E., Uysal, E. ve Yayan, B. (2003). Üçüncü uluslararası matematik ve fen çalışmasında Türk öğrencilerinin başarı düzeylerini etkileyen etmenler [Factors effecting achievement level of Turkish students in the third international mathematics and science study (TIMSS)]. Eğitim Bilimleri ve Uygulama, 2(3), 3-14.
• Cai, M., Yin, Y. F., & Xie, M. (2009). Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transportation Research Part D-Transport and Environment, 14(1), 32-41.
• Ceylan, E. ve Berberoğlu, G. (2007). Öğrencilerin fen başarısını açıklayan etmenler: Bir modelleme çalışması [Factors related with students’ science achievement: A modeling study]. Eğitim ve Bilim, 32(144), 36-48.
• Cohen, I. L. (1994). An artificial neural network analogue of learning in autism. Biological Psychiatry, 36(1), 5-20. doi:10.1016/0006-3223(94)90057-4
• Çırak, G. (2012). Yükseköğretimde öğrenci başarılarının sınıflandırılmasında yapay sinir ağları ve lojistik regresyon yöntemlerinin kullanılması [The usage of artifical neural network and logistic regression methods in the classification of student achievement at higher education (Master’s Thesis)]. Ankara Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
• Çırak, G. ve Çokluk, Ö. (2013). Yükseköğretimde öğrenci başarılarının sınıflandırılmasında yapay sinir ağları ve lojistik regresyon yöntemlerinin kullanılması [The usage of artifical neural network and logistic regresssion methods in the classification of student achievement in higher education]. Mediterranean Journal of Humanities, 3(2), 71-79. doi:10.13114/mjh/201322471
• Çuhadar, M. (2006). Turizm sektöründe talep tahmini için yapay sinir ağları kullanımı ve diğer yöntemlerle karşılaştırmalı analizi (Antalya İli’nin dış turizm talebinde uygulama) [Forecasting tourism demand by artificial neural networks and time series methods (A comparative analysis in inbound tourism demand to Antalya) (Doctoral Thesis)] Süleymen Demirel Üniversitesi Sosyal Bilimler Enstitüsü, Isparta.
• de Haen, C. (2009). Knowledge culture conditioned bounded rationality and human artificial neural network processes (HANNP decision theory): How risks of accidents and environmental impact of a new chemical production process and plant site have entered decisions. Safety Science, 47(6), 843-852.
• Demir, M. (2015). Predicting pre-service classroom teachers’ civil servant recruitment examination’s educational sciences test scores using artificial neural networks. Educational Sciences: Theory & Practice, 5, 1169-1177.
• Doğan, N., & Şahin, A. E. (2009). The variables predicting the appointment of candidate teachers to primary schools. Inonu University Journal of the Faculty of Education, 10, 183-199.
• Dowman, R., & Ben-Avraham, D. (2008). An artificial neural network model of orienting attention toward threatening somatosensory stimuli. Psychophysiology, 45(2), 229-239. doi:10.1111/j.1469-8986.2007.00614.x
• Emir, Ş. (2013). Yapay sinir ağları ve destek vektör makineleri yöntemlerinin sınıflandırma performanslarının karşılaştırılması: Borsa endeks yönünün tahmini üzerine bir uygulama [Classification performance comparison of artificial neural networks andsupport vector machines methods: An empirical study on predicting stockmarket index movement direction (Doctoral Thesis)]. İstanbul Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
• Fleming, M. L., & Malone, M. R. (1983). The relationship of student characteristics and student performance in science as viewed by meta-analysis research. Journal of Research in Science Teaching, 20, 481-495.
• Foody, G. M. (1995). Land cover classification by an artificial neural network with ancillary information. International Journal of Geographical Information Systems, 9(5), 527-542. doi:10.1080/02693799508902054
• Geem, Z. W. (2010). Response to “Comments on ‘Energy demand estimation of South Korea using artificial neural network’ by Zong Woo Geem and William E. Roper” by Serhat Kucukali. Energy Policy, 38(10), 6381-6382. doi:10.1016/j.enpol.2009.08.062
• Germann, P. J. (1994). Testing a model of science process skills acquisition: An interaction with parents’ education, preferred language, gender, science attitude, cognitive development, academic ability, and biology knowledge. Journal of Research in Science Teaching, 31(7), 749-783.
• Ghiassi, M., Saidane, H., & Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21(2), 341-362. doi:10.1016/j.ijforecast.2004.10.008
• Gonzalez, J. M. B., & DesJardins, S. L. (2002). Artificial neural networks: A new approach to predicting application behavior. Research in Higher Education, 43(2), 235-258. doi:Doi 10.1023/A:1014423925000
• Graupe, D. (2007). Principles of artificial neural networks. Advenced Series on Circuits and Systems, 6.
• Guo, W. W. (2010). Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction. Expert Systems with Applications, 37(4), 3358-3365. doi:10.1016/j.eswa.2009.10.014
• Gupta, L., Molfese, D. L., & Tammana, R. (1995). An artificial neural network approach to ERP classification. Brain Cogn, 27(3), 311-330. doi:10.1006/brcg.1995.1025
• Gülçe, G. (2010). Veri ambarı ve veri madenciliği teknikleri kullanılarak öğrenci karar destek sistemi oluşturma [Developing decision support system for student information system by using data warehouse and data mining technics (Master’s Thesis)]. Pamukkale Üniversitesi Fen Bilimleri Enstitüsü, Denizli.
• Haykin, S. (2009). Neural networks and learning machines. Prentice Hall. New Jersey
• Heaton, J. (2008). Introduction to neural networks for C #. Heaton Research, Inc.
• Ibrahim, Z., & Rusli, D. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. Paper presented at the 21st Annual SAS Malaysia Forum, 5th September.
• Kablan, Z. (2010). Akademik mezuniyet ortalama puanı ile KPSS başarı puanı arasındaki ilişki [Relationship between undergraduate grade point average and student performance on civil cervant selection exam (KPSS)]. Uludağ Eğitim Fakültesi Dergisi, 23, 451-470.
• Karamouzis, S. T., & Vrettos, A. (2008). An artificial neural network for predicting student graduation outcomes. Paper presented at the Proceedings of the World Congress on Engineering and Computer Science.
• Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1-11. doi:10.1016/j.compedu.2013.01.015
• Kayıkçı, Ş. (2014). Web sayfalarının yapay sinir ağları ile sınıflandırılması [Classification of web pages using neural networks (Doctoral Thesis)]. Marmara Üniversitesi Sosyal Bilimler Enstitüsü, İstanbul.
• Koç, M. L., Balas, C. E. ve Arslan, A. (2004). Taş dolgu dalgakıranların yapay sinir ağları ile ön tasarımı [Preliminary design of ruble mound breakwaters by using artificial neural networks]. İMO Teknik Dergi, 15(4), 3351-3375.
• Kösterelioğlu, İ., Kösterelioğlu, M. ve Kilmen, S. (2008). Kamu personeli seçme sınavı (KPSS) puanları ile lisans diploma notu arasındaki ilişkilerin çeşitli değişkenlere göre incelenmesi [A study of the relationship between the civil servant selection examination and undergraduate cumulative general point averages according to a number of variables]. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 8, 151-160.
• Kuyucu, Y. E. (2012). Lojistik regresyon analizi (LRA), yapay sinir ağları (YSA) ve sınıflandırma ve regresyon ağaçları (C&RT) yöntemlerinin karşılaştırılması ve tıp alanında bir uygulama [Comparison of logistic regression analysis (LRA), artificial neural networks (ANN) and classfication and regression trees (C&RT) methods and an aplication in medicine (Master’s Thesis)]. Gaziosmanpaşa Üniversitesi Sağlık Bilimleri Enstitüsü, Tokat.
• MATLAB and Neural Network Toolbox Release 2017b, The MathWorks, Inc., Natick, Massachusetts, United States. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115-133.
• Moridis, C. N., & Economides, A. A. (2009). Prediction of student’s mood during an online test using formula-based and neural network-based method. Computers & Education, 53(3), 644-652. doi:10.1016/j.compedu.2009.04.002
• Naser, S. A., Zaqout, I., Ghosh, M. A., Atallah, R., & Alajrami, E. (2015). Predicting student performance using artificial neural network: In the faculty of engineering and information technology. International Journal of Hybrid Information Technology, 8(2), 221-228.
• Nussbaum, M. A., & Chaffin, N. B. (1996). Evaluation of artificial neural network modelling to predict torso muscle activity. Ergonomics, 39(12), 1430-1444. doi:10.1080/00140139608964562
• Oancea, B., Dragoescu, R., & Ciucu, S. (2013). Predicting students’ results in higher education using neural networks. Paper presented at the The 6-th Conference Applied Information and Communication Technology, Faculty of Information Technology, Latvia University of Agriculture, Jelgava, Latvia.
• Oladokun, V., Adebanjo, A., & Charles-Owaba, O. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72-79.
• Paliwal, M., & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865-7872. doi:10.1016/j.eswa.2008.11.003
• Palmer, A., José Montaño, J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781-790. doi:10.1016/j.tourman.2005.05.006
• Polanco, X., Francois, C., & Keim, J. P. (1998). Artificial neural network technology for the classification and cartography of scientific and technical information. Scientometrics, 41(1-2), 69-82.
• Raju, K. A., Sikdar, P. K., & Dhingra, S. L. (1996). Modelling mode choice by means of an artificial neural network. Environment and Planning B-Planning & Design, 23(6), 677-683.
• Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. doi:10.1016/j.compedu.2007.05.016
• Rusli, N. M., Ibrahim, Z., & Janor, R. M. (2008, 26-28 Aug. 2008). Predicting students' academic achievement: Comparison between logistic regression, artificial neural network, and Neuro-fuzzy. Paper presented at the Information Technology, 2008. ITSim 2008. International Symposium on.
• Schibeci, R., & Riley, J. (1986). Influence of students’ background and perceptions on science attitudes and achievement. Journal of Research in Science Teaching, 23, 177-187.
• Selim, H. (2009). Determinants of house prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36(2), 2843-2852.
• Sim, J. J., Tan, G. W. H., Wong, J. C. J., Ooi, K. B., & Hew, T. S. (2014). Understanding and predicting the motivators of mobile music acceptance - A multi-stage MRA-artificial neural network approach. Telematics and Informatics, 31(4), 569-584.
• Sittirug, H. (1997). The predictive value of science process skills, attitude toward science, and cognitive development on achievement in a Thai teacher institution. Unpublished PhD thesis, University of Missouri, Columbia.
• Snyder, M. R. (1998). A functionally equivalent artificial neural network model of the prey orientation behavior of waterstriders (Gerridae). Ethology, 104(4), 285-297.
• Stevens, R., Ikeda, J., Casillas, A., Palacio-Cayetano, J., & Clyman, S. (1999). Artificial neural network-based performance assessments. Computers in Human Behavior, 15(3-4), 295-313.
• Suzuki, Y. (2001). An artificial neural network test for structural change with unspecified parametric form. Japanese Economic Review, 52(3), 339-365.
• Şen, B., Uçar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores: A data mining approach. Expert Systems with Applications, 39(10), 9468-9476. doi:10.1016/j.eswa.2012.02.112
• Şengür, D. (2013). Öğrencilerin akademik başarılarının veri madenciliği metotları ile tahmini [Prediction of student's academic achievements by using the data mining methods (Master’s Thesis)]. Fırat Üniversitesi Eğitim Bilimleri Enstitüsü, Elazığ.
• Şengür, D. ve Tekin, A. (2013). Öğrencilerin mezuniyet notlarının veri madenciliği metotları ile tahmini [Prediction of student’s grade point average by using the data mining methods]. Bilişim Teknolojileri Dergisi, 6(3), 7-16.
• Tang, Y. C. (2009). An approach to budget allocation for an aerospace company-Fuzzy analytic hierarchy process and artificial neural network. Neurocomputing, 72(16-18), 3477-3489. Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography, 6(1), 35-39.
• Tepehan, T. (2011). Türk öğrencilerinin PISA başarılarının yordanmasında yapay sinir ağı ve lojistik regresyon modeli performanslarının karşılaştırılması [Performance comparison of artificial neural network and logistic regression model in predicting Turkish students’ PISA success (Doctoral Thesis)]. Hacettepe Üniversitesi Sosyal Bilimler Enstitüsü, Ankara.
• Tosun, S. (2007). Sınıflandırmada yapay sinir ağları ve karar ağaçları karşılaştırması: Öğrenci başarıları üzerine bir uygulama [Artificial neural networks-decision tree comparison in classification analysis: An application on students’ success (Master’s Thesis)]. İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
• Turhan, K., Kurt, B., & Engin, Y. Z. (2013). Estimation of student success with artificial neural networks. Education and Science, 38(170), 112-120.
• Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405-419. doi:10.1080/09645290701409939
• Verhagen, J. V., & Scott, T. R. (2004). Artificial neural network analysis of gustatory responses in the thalamic taste relay of the rat. Physiology & Behavior, 80(4), 499-513.
• Vokey, J. R., & Higham, P. A. (2004). Opposition logic and neural network models in artificial grammar learning. Consciousness and Cognition, 13(3), 565-578. doi:10.1016/j.concog.2004.05.008
• Wei, C. H., & Lee, Y. (2007). Sequential forecast of incident duration using artificial neural network models. Accident Analysis and Prevention, 39(5), 944-954.
• Wyatt, R. (1996). Evaluating strategies by means of an artificial neural network. Environment and Planning B-Planning & Design, 23(6), 685-695.
• Yavuz, S., & Deveci, M. (2012). İstatistiksel normalizasyon tekniklerinin yapay sinir ağın performansına etkisi [The effect of statistical normalization techniques on the performance of artificial neural network]. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 40, 167-187.
• Yurdakul, E. M. (2014). Türkiye'de ithalatın gelişimi ve ithalatın yapay sinir ağları yöntemi ile tahmin edilebilirliğine yönelik bir analiz [The evolution of import in Turkey and an analysis abaut predictability of import by artificial neural network method (Doctoral Thesis)]. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü, Aydın.