Araştırma Makalesi
BibTex RIS Kaynak Göster

ÇOKLUORTAM ÖĞRENME MATERYALİNE BAĞLANMA ÖLÇEĞİ: GELİŞTİRME, GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI

Yıl 2020, Cilt: 10 Sayı: 2, 321 - 344, 31.07.2020
https://doi.org/10.17943/etku.625811

Öz

Bu çalışmada, e-öğrenme kapsamında öğrencilerin sözel
ve görsel bilgi gösterimlerinin birlikte kullanıldığı çokluortam öğrenme
materyallerine bağlanmasını bilişsel, duygusal ve davranışsal boyutlarda ölçmek
amacıyla likert türü bir ölçek geliştirilmesi amaçlanmıştır. Öncelikle,
alanyazında hazırlanan ölçekler, bilişsel, duygusal ve davranışsal bağlanma
göstergeleri incelenerek madde havuzu oluşturulmuştur. Daha sonra uzman görüşü
alınarak ölçeğin pilot uygulaması yapılmış ve sonucunda maddeler düzenlenmiştir.
Araştırmanın çalışma grubunu çeşitli bölümlerde okumakta 403 üniversite
öğrencisi oluşturmaktadır. Katılımcılara öncelikle önceden hazırlanmış bir çokluortam
öğrenme materyali çalıştırılmış, ardından 46 maddelik ölçek uygulanmıştır.
Açımlayıcı faktör analizi sonucunda bilişsel, duygusal ve davranışsal üç
boyutlu yapının ortaya çıktığı belirlenmiştir. Açıklanan toplam varyans %62,86
olarak bulunmuştur. Doğrulayıcı faktör analizinde ilişkili üç faktörlü model
ile en iyi uyum değerleri yakalanmıştır. Daha sonra yapı geçerliliği kapsamında
yapılan yakınsama ve ıraksama geçerliliği analizleriyle birlikte bilişsel
faktörde dört, duygusal faktörde beş ve davranışsal faktörde dört olmak üzere
toplam 13 maddelik ölçek formuna ulaşılmıştır. Son olarak ikinci sıralı faktör
analizi bilişsel, duygusal ve davranışsal faktörlerin genel bir bağlanma yapısı
altında toplandığı gözlemlenmiştir. Analizler sonucunda ölçeğin geçerli ve
güvenilir olduğu raporlanmıştır.

Kaynakça

  • Akbulut, Y. (2010). Sosyal bilimlerde SPSS uygulamaları. İstanbul: İdeal Kültür ve Yayıncılık.
  • Arnold, C., Villagonzalo, K., Meyer, D., Farhall, J., Foley, F., Kyrios, M., & Thomas, N. (2019). Predicting engagement with an online psychosocial intervention for psychosis: Exploring individual and interventional level predictors. Internet Interventions. Retrieved by: https://www.sciencedirect.com/science/article/pii/S221478291930034X
  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodical, and analytical issues. Educational Psychologist, 50(1), 84-94.
  • Balwant, P. T. (2017). The meaning of student engagement and disengagement in the classroom context: lessons from organisational behaviour. Journal of Further and Higher Education, 42(3), 389-401.
  • Bangert-Drowns, R. L., & Pyke, C. (2001). A taxonomy of student engagement with educational software: An exploration of literate thinking with electronic text. Journal of Educational Computing Research, 24(3), 231-234.
  • Boucheix, J. M., Lowe, R., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71-84.
  • Chapman, E. (2003). Alternative approaches to assessing student engagement rates. Practical Assessment, Research & Development, 8(13), 1-7.
  • Chapman, P., Selvarajah, S., & Webster, J. (1999). Engagement in multimedia training systems. Paper presented at the 32nd Annual Hawaii International Conference on Systems Sciences, Maui, HI, USA. Retrieved from https://ieeexplore.ieee.org/document/772808.
  • Clark, R. C., & Mayer, R. E. (2016). e-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th Edition). Hooken, New Jersey: John Wiley & Sons.
  • Cohen, S. S., Madsen, J., Touchan, G., Robles, D., Lima, S. F. A., Henin, S., Parra, L. C. (2018). Neural engagement with online educational videos predict learning performance for individual students. Neurobiology of Learning and Memory, 155, 60-64.
  • Corno, L., & Mandinach, E. B. (1983). The role of cognitive engagement in classroom learning and motivation. Educational Psychologist, 18(2), 88-108.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Fredricks, J. A. (2015). Academic Engagement. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (2nd Ed., Vol. 1, pp. 31-36). Elsevier.
  • Fredricks, J. A., Blumenfeld, P. C., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore, & L. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305-321). New York, NY: Kluwer Academic/Plenum Press.
  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
  • Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 763-782). New York: Springer Science.
  • Greene, B. A. (2015). Measuring Cognitive Engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14-30.
  • Gunuc, S., & Kuzu, A. (2015). Student engagement scale: development, reliability and validity. Assessment & Evaluation in Higher Education, 40(4), 587-610.
  • Gürer, M. D., & Yıldırım, Z. (2014). Öğrenme nesnesi değerlendirme ölçeği’nin (ÖNDÖ) Geliştirilmesi, Geçerlik ve Güvenirlik Çalışması. Eğitim ve Bilim, 39(176), 121-130.
  • Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Educational Research, 98(3), 184-192.
  • Heidig, S., Müller, J., & Reichelt, M. (2015). Emotional design in multimedia learning: Differentiation on relevant design features and their effects on emotions and learning. Computers in Human Behavior, 44, 81-95.
  • Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53.
  • Jacques, R. D. (1996). The nature of engagement and its role in hypermedia evaluation and design (Doctoral dissertation). London: South Bank University.
  • Kay, R. H., & Knaack, L. (2009). Assessing learning, quality and engagement in learning objects: the Learning Object Evaluation Scale for Studens (LOES-S). Educational Technology Research and Development, 57, 147-168.
  • Kline, R. B. (2005). Principles and Practices of Structural Equation Modeling (2nd Ed.). New York: Guilford Publications.
  • Lee, O., & Anderson, C. W. (1993). Task engagement and conceptual change in middle school science classrooms. American Educational Research Journal, 20(3), 585-610.
  • Leutner, D. (2014). Motivation and emotion as mediators in multimedia learning. Learning and Instruction, 29, 174-175.
  • Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21-33.
  • Mayer, R. E. (2009). Multimedia learning. New York: Cambridge University Press.
  • Mayer, R. E., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12-18.
  • Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal Design for Learning. Wakefield, MA: CAST Professional Publishing.
  • Moreno, R. (2006). Learning in high-tech and multimedia environments. Current directions in psychological science, 15(2), 63-67.
  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309-326.
  • Newmann, F. M. (Ed). (1992). Student Engagement and Achievement in American Secondary School. New York: Teacher College Press.
  • Newman, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and source of student engagement. In F. M. Newmann (Ed.), Student Engagement and Achievement in American Secondary School (pp. 11-39). New York: Teacher College Press.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
  • Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling, 10(4), 622-647.
  • O’Brien, H. L., & Cairns, P. (2015). An empirical evaluation of the user engagement scale (UES) in online news environments. Information Processing and Management, 51, 413-427.
  • O’Brien, H. L., & Toms, E. G. (2009). The development and evaluation survey of a survey to measure engagement. Journal of The American Society for Information Science and Technology, 61(1), 50-69.
  • Park, B., Knörzer, L., Plass, J. L., & Brünken, R. (2015). Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Computers & Education, 86, 30-42.
  • Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128-140.
  • Reeve, J., & Tseng, C. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36 , 257-267.
  • Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evaluation and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 3-43). New York: Springer Science.
  • Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning using emotional data to improve learning in pervasive learning environment. Educational Technology & Society, 12(2), 176-189.
  • Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychology, 50(1), 1-13.
  • Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 21-44). New York: Springer Science.
  • Sun, J. C., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204.
  • Trowler, V. (2010). Student engagement literature review. Heslington: The higher education economy.
  • Topu, F. B. (2015). 3 boyutlu sanal ortamdaki rehberli ve rehbersiz öğrenmenin öğrenci meşguliyeti ve başarısına etkisi (Yayımlanmamış doktora tezi). Erzurum: Atatürk Üniversitesi.
  • van der Meij, H. (2017). Reviews in instructional video. Computers & Education, 114, 164-174.
  • Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentation. The DATA BASE for Advances in Information Systems, 28(2), 63-77.
  • Yang, Y. (2011). Engaging students in an online situated language learning environment. Computer Assisted Language Learning, 24(2), 181-198.

MULTIMEDIA LEARNING MATERIAL ENGAGEMENT SCALE: DEVELOPMENT, VALIDITY AND RELIABILITY STUDY

Yıl 2020, Cilt: 10 Sayı: 2, 321 - 344, 31.07.2020
https://doi.org/10.17943/etku.625811

Öz

In
this study, within the context of e-learning, it is aimed to develop a likert
type scale in order to measure student engagement to multimedia learning
materials that include visual and verbal representations. First of all, an item
pool was generated for student engagement which is reported to have cognitive,
emotional and behavioral dimensions in the literature. Pilot implementation of
the scale was run after taking experts’ opinions and then items we reorganized
according to this feedback. The participants consisted of 403 undergraduate
students from various departments. They were first requested study a multimedia
learning material and then asked to fill out the engagement scale with 46
items. As a result of exploratory factor analysis, it was determined that
student engagement has three dimensional structure; cognitive engagement,
emotional engagement and behavioral engagement. The total variance explained
was 62.86%. In the confirmatory factor analysis, the best fit index values were
obtained for correlated three-factor model. Later, convergence and divergence
validity analyzes were conducted for construct validity, and we reached a scale
form consisting of 13 items; four in cognitive factor, five in emotional factor
and four in behavioral factor. Finally, it was found that these three dimension
were gathered under a general engagement structure as a result of the second
order factor analysis. These findings showed that multimedia learning material
engagement scale was valid and reliable.

Kaynakça

  • Akbulut, Y. (2010). Sosyal bilimlerde SPSS uygulamaları. İstanbul: İdeal Kültür ve Yayıncılık.
  • Arnold, C., Villagonzalo, K., Meyer, D., Farhall, J., Foley, F., Kyrios, M., & Thomas, N. (2019). Predicting engagement with an online psychosocial intervention for psychosis: Exploring individual and interventional level predictors. Internet Interventions. Retrieved by: https://www.sciencedirect.com/science/article/pii/S221478291930034X
  • Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodical, and analytical issues. Educational Psychologist, 50(1), 84-94.
  • Balwant, P. T. (2017). The meaning of student engagement and disengagement in the classroom context: lessons from organisational behaviour. Journal of Further and Higher Education, 42(3), 389-401.
  • Bangert-Drowns, R. L., & Pyke, C. (2001). A taxonomy of student engagement with educational software: An exploration of literate thinking with electronic text. Journal of Educational Computing Research, 24(3), 231-234.
  • Boucheix, J. M., Lowe, R., Putri, D. K., & Groff, J. (2013). Cueing animations: Dynamic signaling aids information extraction and comprehension. Learning and Instruction, 25, 71-84.
  • Chapman, E. (2003). Alternative approaches to assessing student engagement rates. Practical Assessment, Research & Development, 8(13), 1-7.
  • Chapman, P., Selvarajah, S., & Webster, J. (1999). Engagement in multimedia training systems. Paper presented at the 32nd Annual Hawaii International Conference on Systems Sciences, Maui, HI, USA. Retrieved from https://ieeexplore.ieee.org/document/772808.
  • Clark, R. C., & Mayer, R. E. (2016). e-Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (4th Edition). Hooken, New Jersey: John Wiley & Sons.
  • Cohen, S. S., Madsen, J., Touchan, G., Robles, D., Lima, S. F. A., Henin, S., Parra, L. C. (2018). Neural engagement with online educational videos predict learning performance for individual students. Neurobiology of Learning and Memory, 155, 60-64.
  • Corno, L., & Mandinach, E. B. (1983). The role of cognitive engagement in classroom learning and motivation. Educational Psychologist, 18(2), 88-108.
  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  • Fredricks, J. A. (2015). Academic Engagement. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (2nd Ed., Vol. 1, pp. 31-36). Elsevier.
  • Fredricks, J. A., Blumenfeld, P. C., Friedel, J., & Paris, A. (2005). School engagement. In K. A. Moore, & L. Lippman (Eds.), What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 305-321). New York, NY: Kluwer Academic/Plenum Press.
  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.
  • Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 763-782). New York: Springer Science.
  • Greene, B. A. (2015). Measuring Cognitive Engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14-30.
  • Gunuc, S., & Kuzu, A. (2015). Student engagement scale: development, reliability and validity. Assessment & Evaluation in Higher Education, 40(4), 587-610.
  • Gürer, M. D., & Yıldırım, Z. (2014). Öğrenme nesnesi değerlendirme ölçeği’nin (ÖNDÖ) Geliştirilmesi, Geçerlik ve Güvenirlik Çalışması. Eğitim ve Bilim, 39(176), 121-130.
  • Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Educational Research, 98(3), 184-192.
  • Heidig, S., Müller, J., & Reichelt, M. (2015). Emotional design in multimedia learning: Differentiation on relevant design features and their effects on emotions and learning. Computers in Human Behavior, 44, 81-95.
  • Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36-53.
  • Jacques, R. D. (1996). The nature of engagement and its role in hypermedia evaluation and design (Doctoral dissertation). London: South Bank University.
  • Kay, R. H., & Knaack, L. (2009). Assessing learning, quality and engagement in learning objects: the Learning Object Evaluation Scale for Studens (LOES-S). Educational Technology Research and Development, 57, 147-168.
  • Kline, R. B. (2005). Principles and Practices of Structural Equation Modeling (2nd Ed.). New York: Guilford Publications.
  • Lee, O., & Anderson, C. W. (1993). Task engagement and conceptual change in middle school science classrooms. American Educational Research Journal, 20(3), 585-610.
  • Leutner, D. (2014). Motivation and emotion as mediators in multimedia learning. Learning and Instruction, 29, 174-175.
  • Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., & Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. The Internet and Higher Education, 35, 21-33.
  • Mayer, R. E. (2009). Multimedia learning. New York: Cambridge University Press.
  • Mayer, R. E., & Estrella, G. (2014). Benefits of emotional design in multimedia instruction. Learning and Instruction, 33, 12-18.
  • Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal Design for Learning. Wakefield, MA: CAST Professional Publishing.
  • Moreno, R. (2006). Learning in high-tech and multimedia environments. Current directions in psychological science, 15(2), 63-67.
  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309-326.
  • Newmann, F. M. (Ed). (1992). Student Engagement and Achievement in American Secondary School. New York: Teacher College Press.
  • Newman, F. M., Wehlage, G. G., & Lamborn, S. D. (1992). The significance and source of student engagement. In F. M. Newmann (Ed.), Student Engagement and Achievement in American Secondary School (pp. 11-39). New York: Teacher College Press.
  • Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
  • Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling, 10(4), 622-647.
  • O’Brien, H. L., & Cairns, P. (2015). An empirical evaluation of the user engagement scale (UES) in online news environments. Information Processing and Management, 51, 413-427.
  • O’Brien, H. L., & Toms, E. G. (2009). The development and evaluation survey of a survey to measure engagement. Journal of The American Society for Information Science and Technology, 61(1), 50-69.
  • Park, B., Knörzer, L., Plass, J. L., & Brünken, R. (2015). Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Computers & Education, 86, 30-42.
  • Plass, J. L., Heidig, S., Hayward, E. O., Homer, B. D., & Um, E. (2014). Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learning and Instruction, 29, 128-140.
  • Reeve, J., & Tseng, C. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36 , 257-267.
  • Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evaluation and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 3-43). New York: Springer Science.
  • Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning using emotional data to improve learning in pervasive learning environment. Educational Technology & Society, 12(2), 176-189.
  • Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychology, 50(1), 1-13.
  • Skinner, E. A., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, C. Wylie (Eds.). Handbook of research on student engagement (pp. 21-44). New York: Springer Science.
  • Sun, J. C., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204.
  • Trowler, V. (2010). Student engagement literature review. Heslington: The higher education economy.
  • Topu, F. B. (2015). 3 boyutlu sanal ortamdaki rehberli ve rehbersiz öğrenmenin öğrenci meşguliyeti ve başarısına etkisi (Yayımlanmamış doktora tezi). Erzurum: Atatürk Üniversitesi.
  • van der Meij, H. (2017). Reviews in instructional video. Computers & Education, 114, 164-174.
  • Webster, J., & Ho, H. (1997). Audience engagement in multimedia presentation. The DATA BASE for Advances in Information Systems, 28(2), 63-77.
  • Yang, Y. (2011). Engaging students in an online situated language learning environment. Computer Assisted Language Learning, 24(2), 181-198.
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Adem Özgür 0000-0003-2019-2014

Arif Altun 0000-0003-4060-6157

Sacide Güzin Mazman Akar 0000-0003-2188-221X

Yayımlanma Tarihi 31 Temmuz 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 10 Sayı: 2

Kaynak Göster

APA Özgür, A., Altun, A., & Mazman Akar, S. G. (2020). ÇOKLUORTAM ÖĞRENME MATERYALİNE BAĞLANMA ÖLÇEĞİ: GELİŞTİRME, GEÇERLİK VE GÜVENİRLİK ÇALIŞMASI. Eğitim Teknolojisi Kuram Ve Uygulama, 10(2), 321-344. https://doi.org/10.17943/etku.625811