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TENDENCY SCALE FOR TECHNOLOGY USE IN CLASS: DEVELOPMENT, RELIABILITY AND VALIDITY

Year 2014, Volume: 10 Issue: 4, 863 - 884, 12.08.2014

Abstract

In related literature, there is a need for a measurement tool to examine students’ tendencies towards technology. For this reason, the present study aimed at developing a scale for the tendency towards technology use in class. As there is limited research in literature, the item pool was developed mostly based on the data collected via the interviews and the written compositions. The participants of the study were 796 student teachers attending the Education Faculty at Anadolu University in Turkey in the Spring Term of the academic year of 2013-2014. EFA and CFA were conducted with different samples, and a five-point Likert-type scale made up of 16 items and two factors (emotional and behavioral tendencies) was developed. The total explained variance for the two factors of TSTUC was calculated as around 60%. The Cronbach’s Alpha (α) internal consistency reliability coefficient of the total scale was calculated as .93 as a result of EFA and .953 as a result of CFA. Higher scores to be produced by TSTUC refer to the fact that there is a higher tendency towards technology use in classes or that technology use is favored more by students in classes. 

References

  • Allison, B. and Rehm, M. (2007). Effective teaching strategies for middle school learners in multicultural, multilingual classrooms. Middle School Journal, 39(2), 12-18.
  • Brown, J. (2000). Growing up digital: How the web changes work, education, and the way people learn. Journal of the United States Distance Learning Association, 16(2), 31-36.
  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. NY: Guilford Publications, Inc.
  • DeVellis, R.F. (2003). Scale development: Theory and applications (2. ed.). Sage.
  • DeWitt, D. and Siraj, S. (2010). Learners’ perceptions of technology for design of a collaborative m-learning module. World Journal on Educational Technology, 2(3), 169-185.
  • Doppelt, Y. (2006). Teachers’ and pupils’ perceptions of science–technology learning environments. Learning Environ Res, 9, 163–178.
  • Field, A. P. (2009). Discovering statistics using SPSS: And sex and drugs and rock ‘n’ roll ( ed.). London: Sage Publications.
  • Frand, J. L. (2000). The ınformation-age mindset: changes in students and implications for higher education. Educause Review, 35(5), 14-24.
  • Fredricks, J. A., Blumenfeld, P.C. and Paris, A.H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
  • Gibbs, R and Poskitt, J. (2010). Student engagement in the middle years of schooling (years 7-10): A literature review. Wellington: Ministry of Education.
  • Golubski, P. M. (2012). Utilizing Interactive Technologies to Engage, Integrate, Involve, and Increase Community amongst College Students. V. Wang, L. Farmer, J. Parker, and P. Golubski (Eds.) In Pedagogical and Andragogical Teaching and Learning with Information Communication Technologies (pp.1327). Hershey, PA: Information Science Publishing.
  • Gunuc, S. (2013). Determining the role of technology in student engagement and examining of the relationships between student engagement and technology use in class. Unpublished doctoral dissertation, The Graduate School of Educational Sciences, Anadolu University, Turkey.
  • Hooper, D., Coughlan, J. and Mullen, M. (2008). Structural equation modelling: guidelines for determining model fit. Th Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Hu, L.T. and Bentler, P.M. (1999), Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
  • Huck, S. (2012). Reading statistics and research (6. ed.). Boston: Pearson.
  • Hussain, I. and Safdar, M. (2008). Role of information technologies in teaching learning process: perception of the faculty. Turkish Online Journal of Distance Education – TOJDE, 9(2), 46-56.
  • Hutcheson, G. D. and Sofroniou, N. (1999). The multivariate social scientist: An introduction to generalized linear models. Sage Publications.
  • Jukes, I. and Dosaj, A. (2003). The differences between digital native learners and digital immigrant teachers. The InfoSavvy Group, 02013 http://www.apple.com/au/education/digitalkids/disconnect/landscape.html adresinden elde edilmiştir.
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3.ed.). New York: The Guilford Press.
  • Kline, R. B. (2009). Becoming a behavioral science researcher: A guide to producing research that matters. New York: GuildfordPress.
  • Kolikant, Y.B.D. (2009). Digital students in a book-oriented school: Students’ perceptions of school and the usability of digital technology in schools. Educational Technology & Society, 12(2), 131-143.
  • Kolikant, Y.B.D. (2010). Digital natives, better learners? Students’ beliefs about how the Internet influenced their ability to learn. Computers in Human Behavior, 26, 1384-1391.
  • Krause, K. and Coates, H. (2008). Students’ engagement in first-year university. Assessment and Evaluation in Higher Education, 33(5), 493-505.
  • Kvavik, R. B., Caruso, J. B. and Morgan, G. (2004). ECAR study of students and information technology 2004: convenience, connection, and control. Boulder, CO: EDUCAUSE Center for Applied Research.
  • Liburd, J.J. and Christensen, I. F. (2013). Using web 2.0 in higher tourism education. Journal of Hospitality, Leisure, Sport and Tourism Education,12(1), 99-108.
  • Margaryan, A., Littlejohn, A. and Vojt, G. (2011). Are digital natives a myth or reality? University students’ use of digital Technologies. Computers & Education, 56, 429-440.
  • Marzilli, C., Delello, J., Marmion, S., McWhorter, R., Roberts, P. and Marzilli, T. S. (2014). Faculty attitudes towards integrating technology and innovation. International Journal on Integrating Technology in Innovation, 3(1).
  • McMahon, M. and Pospisil, R. (2005). Laptops for a digital lifestyle: Millennial students and wireless mobile technologies. Proceedings of ASCILITE 2005.
  • Naish, R. (2008). The digital ages of man. E-learning Age, ABI/FORM Global, 10-11. Nelson Laird, T. F. and Kuh, G. D. (2005). Student experiences with information technology and their relationship to other aspects of student engagement. Research in Higher Education, 46(2), 211-233.
  • Oblinger, D. and Oblinger, J. (2005). Is it age or IT: First steps towards understanding the net generation. D. Oblinger and J. Oblinger (Eds.). In Educating the Net Generation (pp.2.1–2.20). Boulder, CO: EDUCAUSE.
  • Palfrey, J. and Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. NY: Basic Books.
  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. Maidenhead: Open University Press.
  • Parker, R. E., Bianchi, A., ve Cheah, T. Y. (2008). Perceptions of Instructional Technology: Factors of Influence and Anticipated Consequences. Educational Technology & Society, 11(2), 274-293.
  • Pedró, F. (2006). The new millennium learners: Challenging our views on ICT and learning. Paris: OECD-CERI.
  • Pickens, M. and Eick, C. (2009). Studying motivational strategies used by two teachers in differently tracked science courses. The Journal of Educational Research, 102(5), 349-362.
  • Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1-5.
  • Prensky, M. (2004). The emerging online life of the digital native: What they do differently because of technology, and how they do it. Aralık 03, 2010 tarihinde http://www.marcprensky.com/writing/PrenskyThe_Emerging_Online_Life_of_the_Digital_ Native-03.pdf adresinden alınmıştır.
  • Raykov, T. and Marcoulides, G.A. (2006). A first course in structural equation modeling (2. ed.). Lawrence Erlbaum Associates, Inc: Publishers.
  • Reynolds, S, ve Rucker, J. 2002. “Technology, methodology, and business education.” National Business Year Book (number 4), NBEA Association Publishers.
  • Sheard, J., Carbone, A. and Hurst, A.J. (2010). Student engagement in first year of an ICT degree: staff and student perceptions. Computer Science Education, 20(1), 1Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4. ed.). Hillsdale, NJ: Erlbaum.
  • Tabachnick, G. G. and Fidell, L. S. (2007). Experimental designs using ANOVA. Belmont, CA: Duxbury.
  • Teo, T. and Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing In Higher Education, 26(2), 124-143.
  • Thompson, B. (2008). Exploratory and confirmatory factor analysis: Understanding concepts and applications (3. Ed.). Washington, DC: American Psychological Association.
  • Waycott, J., Bennett, S., Kennedy, G., Dalgarno, B. and Gray, K. (2010). Digital divides Student and staff perceptions of information and communication Technologies. Computers & Education, 54, 1202–1211.
  • Weiß, S. and Bader, H. J. (2010). How to improve media literacy and media skills of secondary school teachers in order to prepare them for the next generation of learners: A new type of in-service training for teachers. M. Ebner and M. Schiefner (Eds.), Looking toward the future of technology-enhanced education: ubiquitous learning and the digital native İçinde (pp.37-54). Hershey. PA: Information Science Reference.
  • Welch, B.K. and Bonnan-White, J. (2012). Twittering to increase student engagement in the university classroom. Knowledge Management & E-Learning: An International Journal, 4(3), 325-345.
  • Wong, K.T., Teo, T. and Russo, S. (2013). Interactive whiteboard acceptance: Applicability of the UTAUT model among student teachers. The Asia Pacific Education Researcher, 22(1), 1-10.
  • Yazzie-Mintz, E. (2010). Charting the path from engage-ment to achievement: A report on the 2009 High School Survey of Student Engagement. Bloomington, IN: Center for Evaluation & Education Policy.

DERSTE TEKNOLOJİ KULLANIMINA YÖNELİK EĞİLİM ÖLÇEĞİ: GELİŞTİRME, GÜVENİRLİK ve GEÇERLİK

Year 2014, Volume: 10 Issue: 4, 863 - 884, 12.08.2014

Abstract

Alanyazında, öğrencilerin teknolojiye yönelik eğilimlerini incelemek için bir ölçme aracına ihtiyaç duyulmaktadır. Bu nedenle, bu çalışmada derste teknoloji kullanımına yönelik eğilim ölçeğinin geliştirilmesi amaçlanmıştır. Alanyazındaki sınırlı araşmalar nedeniyle, madde havuzu önemli ölçüde katılımcılarla yapılan görüşme ve yazdırılan kompozisyonlardan elde edilen verilerle geliştirilmiştir. Araştırmanın katılımcılarını 20132014 öğretim yılı bahar döneminde Anadolu Üniversitesi Eğitim Fakültesi’nde öğrenim gören 796 öğretmen adayı oluşturmuştur. AFA ve DFA farklı örneklem gruplarla gerçekleştirilmiştir. Analizler sonucunda, iki faktör (duyuşşal ve davranışsal eğilim) ve toplam 16 maddeden oluşsan beşli likert tipinde bir ölçek geliştirilmiştir. İki faktöre ilişkin toplam açıklanan varyans %60 olarak hesaplanmıştır. Ölçeğe ilişkin Cronbach Alfa iç tutarlılık güvenirlik katsayısı AFA sonucunda .93 ve DFA sonucunda .953 olarak hesaplanmıştır. Ölçekten alınan yüksek puan, derste teknoloji kullanımına yönelik yüksek eğilime ya da derste teknoloji kullanımının daha çok tercih edildiğine işaret etmektedir.

References

  • Allison, B. and Rehm, M. (2007). Effective teaching strategies for middle school learners in multicultural, multilingual classrooms. Middle School Journal, 39(2), 12-18.
  • Brown, J. (2000). Growing up digital: How the web changes work, education, and the way people learn. Journal of the United States Distance Learning Association, 16(2), 31-36.
  • Brown, T. A. (2006). Confirmatory factor analysis for applied research. NY: Guilford Publications, Inc.
  • DeVellis, R.F. (2003). Scale development: Theory and applications (2. ed.). Sage.
  • DeWitt, D. and Siraj, S. (2010). Learners’ perceptions of technology for design of a collaborative m-learning module. World Journal on Educational Technology, 2(3), 169-185.
  • Doppelt, Y. (2006). Teachers’ and pupils’ perceptions of science–technology learning environments. Learning Environ Res, 9, 163–178.
  • Field, A. P. (2009). Discovering statistics using SPSS: And sex and drugs and rock ‘n’ roll ( ed.). London: Sage Publications.
  • Frand, J. L. (2000). The ınformation-age mindset: changes in students and implications for higher education. Educause Review, 35(5), 14-24.
  • Fredricks, J. A., Blumenfeld, P.C. and Paris, A.H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
  • Gibbs, R and Poskitt, J. (2010). Student engagement in the middle years of schooling (years 7-10): A literature review. Wellington: Ministry of Education.
  • Golubski, P. M. (2012). Utilizing Interactive Technologies to Engage, Integrate, Involve, and Increase Community amongst College Students. V. Wang, L. Farmer, J. Parker, and P. Golubski (Eds.) In Pedagogical and Andragogical Teaching and Learning with Information Communication Technologies (pp.1327). Hershey, PA: Information Science Publishing.
  • Gunuc, S. (2013). Determining the role of technology in student engagement and examining of the relationships between student engagement and technology use in class. Unpublished doctoral dissertation, The Graduate School of Educational Sciences, Anadolu University, Turkey.
  • Hooper, D., Coughlan, J. and Mullen, M. (2008). Structural equation modelling: guidelines for determining model fit. Th Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Hu, L.T. and Bentler, P.M. (1999), Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
  • Huck, S. (2012). Reading statistics and research (6. ed.). Boston: Pearson.
  • Hussain, I. and Safdar, M. (2008). Role of information technologies in teaching learning process: perception of the faculty. Turkish Online Journal of Distance Education – TOJDE, 9(2), 46-56.
  • Hutcheson, G. D. and Sofroniou, N. (1999). The multivariate social scientist: An introduction to generalized linear models. Sage Publications.
  • Jukes, I. and Dosaj, A. (2003). The differences between digital native learners and digital immigrant teachers. The InfoSavvy Group, 02013 http://www.apple.com/au/education/digitalkids/disconnect/landscape.html adresinden elde edilmiştir.
  • Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
  • Kline, R. B. (2011). Principles and practice of structural equation modeling (3.ed.). New York: The Guilford Press.
  • Kline, R. B. (2009). Becoming a behavioral science researcher: A guide to producing research that matters. New York: GuildfordPress.
  • Kolikant, Y.B.D. (2009). Digital students in a book-oriented school: Students’ perceptions of school and the usability of digital technology in schools. Educational Technology & Society, 12(2), 131-143.
  • Kolikant, Y.B.D. (2010). Digital natives, better learners? Students’ beliefs about how the Internet influenced their ability to learn. Computers in Human Behavior, 26, 1384-1391.
  • Krause, K. and Coates, H. (2008). Students’ engagement in first-year university. Assessment and Evaluation in Higher Education, 33(5), 493-505.
  • Kvavik, R. B., Caruso, J. B. and Morgan, G. (2004). ECAR study of students and information technology 2004: convenience, connection, and control. Boulder, CO: EDUCAUSE Center for Applied Research.
  • Liburd, J.J. and Christensen, I. F. (2013). Using web 2.0 in higher tourism education. Journal of Hospitality, Leisure, Sport and Tourism Education,12(1), 99-108.
  • Margaryan, A., Littlejohn, A. and Vojt, G. (2011). Are digital natives a myth or reality? University students’ use of digital Technologies. Computers & Education, 56, 429-440.
  • Marzilli, C., Delello, J., Marmion, S., McWhorter, R., Roberts, P. and Marzilli, T. S. (2014). Faculty attitudes towards integrating technology and innovation. International Journal on Integrating Technology in Innovation, 3(1).
  • McMahon, M. and Pospisil, R. (2005). Laptops for a digital lifestyle: Millennial students and wireless mobile technologies. Proceedings of ASCILITE 2005.
  • Naish, R. (2008). The digital ages of man. E-learning Age, ABI/FORM Global, 10-11. Nelson Laird, T. F. and Kuh, G. D. (2005). Student experiences with information technology and their relationship to other aspects of student engagement. Research in Higher Education, 46(2), 211-233.
  • Oblinger, D. and Oblinger, J. (2005). Is it age or IT: First steps towards understanding the net generation. D. Oblinger and J. Oblinger (Eds.). In Educating the Net Generation (pp.2.1–2.20). Boulder, CO: EDUCAUSE.
  • Palfrey, J. and Gasser, U. (2008). Born digital: Understanding the first generation of digital natives. NY: Basic Books.
  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS for Windows. Maidenhead: Open University Press.
  • Parker, R. E., Bianchi, A., ve Cheah, T. Y. (2008). Perceptions of Instructional Technology: Factors of Influence and Anticipated Consequences. Educational Technology & Society, 11(2), 274-293.
  • Pedró, F. (2006). The new millennium learners: Challenging our views on ICT and learning. Paris: OECD-CERI.
  • Pickens, M. and Eick, C. (2009). Studying motivational strategies used by two teachers in differently tracked science courses. The Journal of Educational Research, 102(5), 349-362.
  • Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1-5.
  • Prensky, M. (2004). The emerging online life of the digital native: What they do differently because of technology, and how they do it. Aralık 03, 2010 tarihinde http://www.marcprensky.com/writing/PrenskyThe_Emerging_Online_Life_of_the_Digital_ Native-03.pdf adresinden alınmıştır.
  • Raykov, T. and Marcoulides, G.A. (2006). A first course in structural equation modeling (2. ed.). Lawrence Erlbaum Associates, Inc: Publishers.
  • Reynolds, S, ve Rucker, J. 2002. “Technology, methodology, and business education.” National Business Year Book (number 4), NBEA Association Publishers.
  • Sheard, J., Carbone, A. and Hurst, A.J. (2010). Student engagement in first year of an ICT degree: staff and student perceptions. Computer Science Education, 20(1), 1Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4. ed.). Hillsdale, NJ: Erlbaum.
  • Tabachnick, G. G. and Fidell, L. S. (2007). Experimental designs using ANOVA. Belmont, CA: Duxbury.
  • Teo, T. and Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing In Higher Education, 26(2), 124-143.
  • Thompson, B. (2008). Exploratory and confirmatory factor analysis: Understanding concepts and applications (3. Ed.). Washington, DC: American Psychological Association.
  • Waycott, J., Bennett, S., Kennedy, G., Dalgarno, B. and Gray, K. (2010). Digital divides Student and staff perceptions of information and communication Technologies. Computers & Education, 54, 1202–1211.
  • Weiß, S. and Bader, H. J. (2010). How to improve media literacy and media skills of secondary school teachers in order to prepare them for the next generation of learners: A new type of in-service training for teachers. M. Ebner and M. Schiefner (Eds.), Looking toward the future of technology-enhanced education: ubiquitous learning and the digital native İçinde (pp.37-54). Hershey. PA: Information Science Reference.
  • Welch, B.K. and Bonnan-White, J. (2012). Twittering to increase student engagement in the university classroom. Knowledge Management & E-Learning: An International Journal, 4(3), 325-345.
  • Wong, K.T., Teo, T. and Russo, S. (2013). Interactive whiteboard acceptance: Applicability of the UTAUT model among student teachers. The Asia Pacific Education Researcher, 22(1), 1-10.
  • Yazzie-Mintz, E. (2010). Charting the path from engage-ment to achievement: A report on the 2009 High School Survey of Student Engagement. Bloomington, IN: Center for Evaluation & Education Policy.
There are 49 citations in total.

Details

Primary Language English
Subjects Studies on Education
Journal Section Makaleler
Authors

Selim Günüç

Abdullah Kuzu

Publication Date August 12, 2014
Submission Date August 12, 2014
Published in Issue Year 2014 Volume: 10 Issue: 4

Cite

APA Günüç, S., & Kuzu, A. (2014). TENDENCY SCALE FOR TECHNOLOGY USE IN CLASS: DEVELOPMENT, RELIABILITY AND VALIDITY. Eğitimde Kuram Ve Uygulama, 10(4), 863-884.