Research Article
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Year 2021, Volume: 5 Issue: 2, 246 - 266, 31.12.2021
https://doi.org/10.54535/rep.1017070

Abstract

References

  • Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Date of access: 31 October 2021, https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf.
  • Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K.A., & Long, J.S. (Eds.), Testing Structural Equation Models (pp. 136-162), Sage, Beverly Hills, CA.
  • Christensen, R. (2002). Effects of technology integration education on the attitudes of teachers and students. Journal of Research on Technology in Education, 34(4), 411-433.
  • Colosi L. (2006). Designing an effective questionnaire. Date of access: 31 October 2021, https://www.gateshead.gov.uk/DocumentLibrary/council/consultation/Questionnaire-design-guidance-web.pdf.
  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Spychology, 78(1), 98-104.
  • Dodigovic, M. (2009). Speech processing technology in second language testing. In: Proceedings of the Conference on Language & Technology. https://www.cle.org.pk/clt09/download/Papers/Paper17.pdf
  • Hampel, R., & Stickler, U. (2005). New skills for new classrooms: Training tutors to teach languages online. Computer Assisted Language Learning, 18(4), 311-326.
  • Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424-453.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: Guilford.
  • Knezek, G., & Christensen, R. (2008). The importance of information technology attitudes and competencies in primary and secondary education. International handbook of information technology in primary and secondary education (pp. 321-331). Boston, MA: Springer.
  • Lee, Y. (2019). An analysis of the influence of block-type programming language-based artificial intelligence education on the learner’s attitude in artificial intelligence. Journal of The Korean Association of Information Education, 23(2), 189-196.
  • Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838.
  • Metlek, S., & Kayaalp, K. (2020). Otistik spektrum bozukluğunun makine öğrenme algoritmaları ile tespiti [Detection of autistic spectrum disorder with machine learning algorithms]. Journal of Intelligent Systems: Theory and Applications, 3(2), 60-68.
  • Meyers, L. S., Glenn, G., & Guarino, A. J. (2006). Applied multivariate research: design and interpretation. CA: Sage, Thousand Oaks.
  • Orcan, F. (2018). Exploratory and confirmatory factor analysis: Which one to use first? Journal of Measurement and Evaluation in Education and Psychology, 9(4), 414-421.
  • Öztürk, A, Karatekin, M, Saylar, İ., & Bardakcı, N. (2021). Recognition of sign language letters using image processing and deep learning methods. Journal of Intelligent Systems: Theory and Applications, 4(1), 17-23.
  • Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. CA: Thomson/Brooks/Cole, Belmont.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136.
  • Yücel, A. (2021). Tüketici yorumları üzerine bir metin madenciliği ve veri boyutu indirgeme yaklaşımı [A text mining and data size reduction approach on consumer reviews]. Journal of Intelligent Systems: Theory and Applications, 4(1), 8-16.

Validity and Reliability Study of a Turkish Form of the Machine Learning Attitude Scale

Year 2021, Volume: 5 Issue: 2, 246 - 266, 31.12.2021
https://doi.org/10.54535/rep.1017070

Abstract

This study aims to adapt the learners’ machine learning technologies attitude scale to Turkish. Participants of the study are 309 university students. Confirmatory factor analysis (CFA) was used on data obtained from Turkish students for construct validity of the scale. Following this, 23 items were excluded. A confirmatory factor analysis was performed again, completing adaptation of the scale to Turkish. Three reasons for excluding the items and factor following the confirmatory factor analysis emerged: item structure, domain self-efficacy, and the cultural adaptation process. This study has enabled the scale of attitudes toward artificial intelligence to be adapted to Turkish specifically for machine learning techniques and technologies. The scale can be used as a resource for further studies.

References

  • Baker, T., Smith, L., & Anissa, N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Date of access: 31 October 2021, https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf.
  • Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In: Bollen, K.A., & Long, J.S. (Eds.), Testing Structural Equation Models (pp. 136-162), Sage, Beverly Hills, CA.
  • Christensen, R. (2002). Effects of technology integration education on the attitudes of teachers and students. Journal of Research on Technology in Education, 34(4), 411-433.
  • Colosi L. (2006). Designing an effective questionnaire. Date of access: 31 October 2021, https://www.gateshead.gov.uk/DocumentLibrary/council/consultation/Questionnaire-design-guidance-web.pdf.
  • Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Spychology, 78(1), 98-104.
  • Dodigovic, M. (2009). Speech processing technology in second language testing. In: Proceedings of the Conference on Language & Technology. https://www.cle.org.pk/clt09/download/Papers/Paper17.pdf
  • Hampel, R., & Stickler, U. (2005). New skills for new classrooms: Training tutors to teach languages online. Computer Assisted Language Learning, 18(4), 311-326.
  • Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424-453.
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
  • Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). New York: Guilford.
  • Knezek, G., & Christensen, R. (2008). The importance of information technology attitudes and competencies in primary and secondary education. International handbook of information technology in primary and secondary education (pp. 321-331). Boston, MA: Springer.
  • Lee, Y. (2019). An analysis of the influence of block-type programming language-based artificial intelligence education on the learner’s attitude in artificial intelligence. Journal of The Korean Association of Information Education, 23(2), 189-196.
  • Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838.
  • Metlek, S., & Kayaalp, K. (2020). Otistik spektrum bozukluğunun makine öğrenme algoritmaları ile tespiti [Detection of autistic spectrum disorder with machine learning algorithms]. Journal of Intelligent Systems: Theory and Applications, 3(2), 60-68.
  • Meyers, L. S., Glenn, G., & Guarino, A. J. (2006). Applied multivariate research: design and interpretation. CA: Sage, Thousand Oaks.
  • Orcan, F. (2018). Exploratory and confirmatory factor analysis: Which one to use first? Journal of Measurement and Evaluation in Education and Psychology, 9(4), 414-421.
  • Öztürk, A, Karatekin, M, Saylar, İ., & Bardakcı, N. (2021). Recognition of sign language letters using image processing and deep learning methods. Journal of Intelligent Systems: Theory and Applications, 4(1), 17-23.
  • Pokrivcakova, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153.
  • Tabachnick, B. G., & Fidell, L. S. (2007). Experimental designs using ANOVA. CA: Thomson/Brooks/Cole, Belmont.
  • Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability and stability in panel models. Sociological Methodology, 8, 84-136.
  • Yücel, A. (2021). Tüketici yorumları üzerine bir metin madenciliği ve veri boyutu indirgeme yaklaşımı [A text mining and data size reduction approach on consumer reviews]. Journal of Intelligent Systems: Theory and Applications, 4(1), 8-16.
There are 21 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Articles
Authors

Sinan Hopcan 0000-0001-8911-3463

Elif Polat 0000-0002-6086-9002

Gamze Türkmen 0000-0002-4695-9159

Early Pub Date December 5, 2021
Publication Date December 31, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Hopcan, S., Polat, E., & Türkmen, G. (2021). Validity and Reliability Study of a Turkish Form of the Machine Learning Attitude Scale. Research on Education and Psychology, 5(2), 246-266. https://doi.org/10.54535/rep.1017070

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