An Examination Of The Role Of Vocational Training Centers In Ensuring School-Industry Cooperation From Employer's Perspective: The Ostim Case
Öz
This study examines the role of Vocational Training Centers in school-industry cooperation from the perspective of employers. Within the scope of the research, face-to-face interviews were conducted with 12 business owners operating in Ankara Organised Industrial Zone who have cooperated with Vocational Training Centers. The research reveals that vocational education centers in Turkey are of great importance in terms of training graduates in line with the needs of the business world. However, employers have serious difficulties in recruiting staff and mostly meet their labour force needs by employing foreign workers. Furthermore, school-industry cooperation is essential so that the education curriculum can better meet the needs of the business world and provide students with workplace experience.Employers value the "certificate of mastery" obtained at the end of training to increase employability. The study also underlines the need for effective school-industry co-operation mechanisms. A strong partnership between business and educational institutions can improve the vocational education system in Turkey and prepare the future workforce.
Anahtar Kelimeler
Education School-Industry co-operation Social Policy Employment
Kaynakça
- Atar, B. (2011). Tanımlayıcı ve açıklayıcı madde tepki modellerinin TIMSS 2007 Türkiye matematik verisine uyarlanması. Eğitim ve Bilim, 36(159).
- Atar, B., & Aktan, D. Ç. (2013). Birey açıklayıcı madde tepki kuramı analizi: örtük regresyon iki parametreli lojistik modeli. Eğitim ve Bilim, 38(168).
- Baker, F. B. (2001). The basics of item response theory. http://ericae. net/irt/baker. Berberoğlu G. ve Kalender İ. (2005). Öğrenci Başarısının Yıllara, Okul Türlerine, Bölgelere Göre İncelenmesi: ÖSS ve PISA Analizi, ODTÜ Eğitim Bilimleri ve Uygulama Dergisi, Sayfa 27-28.
- Blozis, S. A., Conger K. J., & Harring, J. R. (2007). Nonlinear latent curve models for multivariate longitudinal data. International Journal of Behavioral Development: Special Issue on Longitudinal Modeling of Developmental Processes, 31, 340-346
- Boeck, P. de, Cho, S. J., & Wilson, M. (2011). Explanatory secondary dimension modeling of latent differential item functioning. Applied Psychological Measurement, 35(8), 583–603.
- Boeck, P. de., &Wilson, M. (2004). Explanatory item response models. New York, NY: Springer New York.
- Briggs, D. C. (2008). Using explanatory item response models to analyze group differences in science achievement. Applied Measurement in Education, 21(2), 89–118.
- Bulut, O. (2021). eirm: Explanatory item response modeling for dichotomous and polytomous item responses, R package version 0.4. doi: 10.5281/zenodo.4556285 Available from https://CRAN.R-project.org/package=eirm.
- Bulut, O., Palma, J., Rodriguez, M. C., & Stanke, L. (2015). Evaluating measurement invariance in the measurement of developmental assets in Latino English language groups across developmental stages. Sage Open, 5(2), 2158244015586238.
- Büyükkıdık, S., & Bulut, O. (2022). Analyzing the Effects of Test, Student, and School Predictors on Science Achievement: An Explanatory IRT Modeling Approach. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 40-53.
- Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in math achievement: Evidence from a large-scale US sample. Research in Education, 90(1), 98-112.
- Chen, F., Yang, H., Bulut, O., Cui, Y., & Xin, T. (2019). Examining the relation of personality factors to substance use disorder by explanatory item response modeling of DSM-5 symptoms. PloS One, 14(6), e0217630. https://doi.org/10.1371/journal.pone.0217630
- Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265-289.
- Chiu, T. (2016). Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards (Doctoral dissertation, UC Berkeley).
- Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston, 6277 Sea Harbor Drive, Orlando, FL 32887.
- De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
- De Ayala, R. J. (2022). The theory and practice of item response theory, Second Edition. Guilford Publications.
- DeMars, C. (2010). Item response theory. Oxford University Press.
- Desjardins, C. D., & Bulut, O. (2018). Handbook of educational measurement and psychometrics using R. CRC Press.
- Ellison, G., & Swanson, A. (2018). Dynamics of the gender gap in high math achievement (No. w24910). National Bureau of Economic Research.
- Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Maheah.
- Fleiss,J.L.(1971) "Measuring nominal scale agreement among many raters." Psychological Bulletin, Cilt 76, Sayi 5 say. 378-382
- Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th edt.). New York: McGram-Hill Companies.
- Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Measurement methods for the social sciences series. Newbury Park, Calif.: Sage Publications.
- Kahraman, N. (2014). An explanatory item response theory approach for a computer-based case simulation test. Eurasian Journal of Educational Research, 14(54), 117–134. https://doi.org/10.14689/ejer.2014.54.7
- Kim, J., & Wilson, M. (2020). Polytomous item explanatory item response theory models. Educational and Psychological Measurement, 80(4), 726-755.
- Landis, J. R. ve Koch, G. G. (1977) "The measurement of observer agreement for categorical data", Biometrics. Cilt. 33, say. 159-174
- Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
- Min, H., Zickar, M., & Yankov, G. (2018). Understanding item parameters in personality scales: An explanatory item response modeling approach. Personality and Individual Differences, 128, 1–6. https://doi.org/10.1016/j.paid.2018.02.012
- Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous Item Response Theory models. Applied Psychological Measurement, 24(1), 24-50
- Petscher, Y., Compton, D. L., Steacy, L., & Kinnon, H. (2020). Past perspectives and new opportunities for the explanatory item response model. Annals of Dyslexia, 70(2), 160-179.
- Randall, J., Cheong, Y. F., & Engelhard, G. (2010). Using explanatory item response theory modeling to investigate context effects of differential item functioning for students with disabilities. Educational and Psychological Measurement, 71(1), 129–147.
- Sijtsma, K. (2020). Measurement models for psychological attributes: Classical test theory, factor analysis, item response theory, and latent class models. CRC Press.
- Tat, O. (2020). Açıklayıcı Madde Tepki Modellerinin Bilgisayar Ortamında Bireye Uyarlanmış Testlerde Kullanımı. [Doktora Tezi]. Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
- Yavuz, H. C. (2019). The effects of log data on students’ performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
- Yen, W. M. (1981). Using simulation results to choose a latent trait model. Applied Psychological Measurement, 5, 245–262.
- Yücel, Z., & Koç, M. (2011). İlköğretim öğrencilerinin matematik dersine karşı tutumlarının başarı düzeylerini yordama gücü ile cinsiyet arasındaki ilişki. İlköğretim Online, 10(1), 133-143.
MESLEKİ EĞİTİM MERKEZLERİ’NİN OKUL-SANAYİ İŞ BİRLİĞİNİN SAĞLANMASINDAKİ ROLÜNÜN İŞVEREN BAKIŞ AÇISIYLA İNCELENMESİ: OSTİM ÖRNEĞİ
Öz
Bu çalışma, işverenlerin perspektifinden Mesleki Eğitim Merkezleri'nin okul-sanayi işbirliği içindeki rolünü incelemektedir. Araştırma kapsamında, Mesleki Eğitim Merkezleri ile işbirliği yapmış ve Ankara Organize Sanayi Bölgesi'nde faaliyet gösteren 12 işletme sahibi ile yüz yüze görüşmeler gerçekleştirilmiştir. Araştırma, Türkiye'deki mesleki eğitim merkezlerinin, iş dünyasının ihtiyaçlarına uygun mezunlar yetiştirme açısından büyük önem taşıdığını ortaya koyuyor. Ancak işverenler eleman temini konusunda ciddi anlamda zorluk yaşamakta ve işgücü ihtiyacını çoğunlukla yabancı işçi istihdamı ile karşılamaktadır. Ayrıca eğitim müfradatın iş dünyasının ihtiyaçlarını daha iyi karşılamasını sağlamak ve öğrencilere işyeri deneyimi sağlamak için okul-sanayi işbirliği esastır. İşverenler istihdam edilebilirliği artırmak için eğitim sonunda elde edilen "ustalık sertifikasına" değer veriyor. Çalışma aynı zamanda etkili okul-sanayi işbirliği mekanizmalarına olan ihtiyacın altını çiziyor. İş dünyası ile eğitim kurumları arasındaki güçlü bir ortaklık, Türkiye'deki mesleki eğitim sistemini geliştirebilir ve geleceğin işgücünü hazırlayabilir.
Anahtar Kelimeler
Mesleki Eğitim Okul- Sanayi İş Birliği Sosyal Politika İstihdam
Kaynakça
- Atar, B. (2011). Tanımlayıcı ve açıklayıcı madde tepki modellerinin TIMSS 2007 Türkiye matematik verisine uyarlanması. Eğitim ve Bilim, 36(159).
- Atar, B., & Aktan, D. Ç. (2013). Birey açıklayıcı madde tepki kuramı analizi: örtük regresyon iki parametreli lojistik modeli. Eğitim ve Bilim, 38(168).
- Baker, F. B. (2001). The basics of item response theory. http://ericae. net/irt/baker. Berberoğlu G. ve Kalender İ. (2005). Öğrenci Başarısının Yıllara, Okul Türlerine, Bölgelere Göre İncelenmesi: ÖSS ve PISA Analizi, ODTÜ Eğitim Bilimleri ve Uygulama Dergisi, Sayfa 27-28.
- Blozis, S. A., Conger K. J., & Harring, J. R. (2007). Nonlinear latent curve models for multivariate longitudinal data. International Journal of Behavioral Development: Special Issue on Longitudinal Modeling of Developmental Processes, 31, 340-346
- Boeck, P. de, Cho, S. J., & Wilson, M. (2011). Explanatory secondary dimension modeling of latent differential item functioning. Applied Psychological Measurement, 35(8), 583–603.
- Boeck, P. de., &Wilson, M. (2004). Explanatory item response models. New York, NY: Springer New York.
- Briggs, D. C. (2008). Using explanatory item response models to analyze group differences in science achievement. Applied Measurement in Education, 21(2), 89–118.
- Bulut, O. (2021). eirm: Explanatory item response modeling for dichotomous and polytomous item responses, R package version 0.4. doi: 10.5281/zenodo.4556285 Available from https://CRAN.R-project.org/package=eirm.
- Bulut, O., Palma, J., Rodriguez, M. C., & Stanke, L. (2015). Evaluating measurement invariance in the measurement of developmental assets in Latino English language groups across developmental stages. Sage Open, 5(2), 2158244015586238.
- Büyükkıdık, S., & Bulut, O. (2022). Analyzing the Effects of Test, Student, and School Predictors on Science Achievement: An Explanatory IRT Modeling Approach. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 40-53.
- Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in math achievement: Evidence from a large-scale US sample. Research in Education, 90(1), 98-112.
- Chen, F., Yang, H., Bulut, O., Cui, Y., & Xin, T. (2019). Examining the relation of personality factors to substance use disorder by explanatory item response modeling of DSM-5 symptoms. PloS One, 14(6), e0217630. https://doi.org/10.1371/journal.pone.0217630
- Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265-289.
- Chiu, T. (2016). Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards (Doctoral dissertation, UC Berkeley).
- Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston, 6277 Sea Harbor Drive, Orlando, FL 32887.
- De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
- De Ayala, R. J. (2022). The theory and practice of item response theory, Second Edition. Guilford Publications.
- DeMars, C. (2010). Item response theory. Oxford University Press.
- Desjardins, C. D., & Bulut, O. (2018). Handbook of educational measurement and psychometrics using R. CRC Press.
- Ellison, G., & Swanson, A. (2018). Dynamics of the gender gap in high math achievement (No. w24910). National Bureau of Economic Research.
- Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Maheah.
- Fleiss,J.L.(1971) "Measuring nominal scale agreement among many raters." Psychological Bulletin, Cilt 76, Sayi 5 say. 378-382
- Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th edt.). New York: McGram-Hill Companies.
- Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Measurement methods for the social sciences series. Newbury Park, Calif.: Sage Publications.
- Kahraman, N. (2014). An explanatory item response theory approach for a computer-based case simulation test. Eurasian Journal of Educational Research, 14(54), 117–134. https://doi.org/10.14689/ejer.2014.54.7
- Kim, J., & Wilson, M. (2020). Polytomous item explanatory item response theory models. Educational and Psychological Measurement, 80(4), 726-755.
- Landis, J. R. ve Koch, G. G. (1977) "The measurement of observer agreement for categorical data", Biometrics. Cilt. 33, say. 159-174
- Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
- Min, H., Zickar, M., & Yankov, G. (2018). Understanding item parameters in personality scales: An explanatory item response modeling approach. Personality and Individual Differences, 128, 1–6. https://doi.org/10.1016/j.paid.2018.02.012
- Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous Item Response Theory models. Applied Psychological Measurement, 24(1), 24-50
- Petscher, Y., Compton, D. L., Steacy, L., & Kinnon, H. (2020). Past perspectives and new opportunities for the explanatory item response model. Annals of Dyslexia, 70(2), 160-179.
- Randall, J., Cheong, Y. F., & Engelhard, G. (2010). Using explanatory item response theory modeling to investigate context effects of differential item functioning for students with disabilities. Educational and Psychological Measurement, 71(1), 129–147.
- Sijtsma, K. (2020). Measurement models for psychological attributes: Classical test theory, factor analysis, item response theory, and latent class models. CRC Press.
- Tat, O. (2020). Açıklayıcı Madde Tepki Modellerinin Bilgisayar Ortamında Bireye Uyarlanmış Testlerde Kullanımı. [Doktora Tezi]. Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
- Yavuz, H. C. (2019). The effects of log data on students’ performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
- Yen, W. M. (1981). Using simulation results to choose a latent trait model. Applied Psychological Measurement, 5, 245–262.
- Yücel, Z., & Koç, M. (2011). İlköğretim öğrencilerinin matematik dersine karşı tutumlarının başarı düzeylerini yordama gücü ile cinsiyet arasındaki ilişki. İlköğretim Online, 10(1), 133-143.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Çalışma Ekonomisi ve Endüstri İlişkileri |
| Bölüm | Research Articles |
| Yazarlar | |
| Erken Görünüm Tarihi | 26 Ekim 2023 |
| Yayımlanma Tarihi | 30 Ekim 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 20 Sayı: Human Behavior and Social Institutions |
