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

Year 2018, Volume: 5 Issue: 3, 491 - 509, 19.09.2018
https://doi.org/10.21449/ijate.444073

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.

References

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

Year 2018, Volume: 5 Issue: 3, 491 - 509, 19.09.2018
https://doi.org/10.21449/ijate.444073

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.

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.
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  • 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
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There are 78 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Ergün Akgün 0000-0002-7271-6900

Metin Demir

Publication Date September 19, 2018
Submission Date April 16, 2018
Published in Issue Year 2018 Volume: 5 Issue: 3

Cite

APA Akgün, E., & Demir, M. (2018). Modeling Course Achievements of Elementary Education Teacher Candidates with Artificial Neural Networks. International Journal of Assessment Tools in Education, 5(3), 491-509. https://doi.org/10.21449/ijate.444073

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