Araştırma Makalesi
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Reliability and Validity of Turkish Version of the Multidimensional Cognitive Load Scale for Virtual Environments

Yıl 2025, Cilt: 13 Sayı: 25, 431 - 453

Öz

This study aimed to adapt the “Multidimensional Cognitive Load Scale for Virtual Environments Scale (MCLSVE)” into Turkish, while evaluating its validity and reliability. A survey model was used for the adaptation process, utilizing the scale developed by Andersen and Makransky (2021), which is now referred to as the “MCLSVE-TR” The scale comprises five subscales: Intrinsic Load, Extraneous Load Instruction, Extraneous Load Interactions, Extraneous Load Environment, and Germane Load. The sample group for the study was 203 volunteer university students selected using a convenience sampling technique. Exploratory Factor Analysis (EFA) was conducted to determine the factor structure of the scale. As a result of EFA, it was revealed that the scale consisted of 18 items and 5 sub-dimensions, and these dimensions explained 82,34% of the total variance. In addition, Confirmatory Factor Analysis (CFA) confirmed the five-factor structure. Pearson correlation analysis to determine the relationship between scale factors, and Cronbach Alpha coefficient to determine the reliability level of scale factors were used. The findings confirmed that the Turkish adaptation of the MCLSVE is both valid and reliable.

Etik Beyan

Ethical Committee Permission Information Name of the board that carries out ethical assessment: Hacettepe University Scientific Research Ethics Committee for Social and Human Sciences The date and number of the ethical assessment decision: 22.10.2024- 00003838706

Kaynakça

  • Akan, S., & Keskin, S. (2023). Etkileşimli öğretimsel videoların başarı, bilişsel yük ve video kapılma üzerine etkisi [The effects of interactive instructional videos on achievement, cognitive load and video engagement]. Erzincan University Journal of Education Faculty, 25(2), 198-208.
  • Akhter, F. (2017). Virtual learning environment: how well designed multimedia lowers the learners'cognitive load. Journal of International Business Research, 16(1), 1-6.
  • Andersen, M. S., & Makransky, G. (2021). The validation and further development of a multidimensional cognitive load scale for virtual environments. Journal of Computer Assisted Learning, 37(1), 183-196.
  • Anderson, L. W. (1988). Attitudes and their measurement. In J. P. Keeves (Ed.), Educational research, methodology and measurement: An international handbook. Pergamon.
  • Ayres, P. (2018). Subjective measures of cognitive load—What can they reliably measure? In Cognitive load measurement and application—A theoretical framework for meaningful research and practice (1st ed.). Routledge.
  • Baddeley, A. (1999). Human memory. Allyn & Bacon.
  • Bartlett, M.S. (1950). Tests of significance in factor analysis. British Journal of Psychology, 3, 77-85.
  • Barut Tuğtekin, E. (2020). Çoklu ortamla öğrenmede farklı etiketleme yaklaşımlarının başarı, bilişsel yük ve motivasyona etkisi [Effect of different tagging approaches on achievement, cognitive load and motivation in multimedia learning]. Anadolu Üniversitesi: Yayımlanmamış doktora tezi.
  • Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191. Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford.
  • Büyüköztürk, Ş. (2013). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (18. bs.) [Data analysis handbook for social sciences: Statistics, research design, SPSS applications and interpretation (18th ed.)]. Pegem Akademi.
  • Cerny, B. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate behavioral research, 12(1), 43-47.
  • Chi, M.,Glaser, R.,& Rees, E. (1982). Expertise in problemsolving. InR. Sternberg (Ed.), Advances in thepsychologyofhumanintelligence (pp. 7–75). Erlbaum.
  • Choi, Y., & Lee, H. (2022). Psychometric properties for multidimensional cognitive load scale in an E-learning environment. International Journal of Environmental Research and Public Health, 19(10), 5822.
  • Cierniak, G., Gerjets, P., & Scheiter, K. (2009). Expertise reversal in multimedia learning: Subjective load ratings and viewing behavior as cognitive process indicators. Proceedings of the Annual Meeting of the Cognitive Science Society, 31(31), 1906–1911.
  • Clark, R. C. (1999). Developing technical training (2nd ed.). International Society for Performance Improvement.
  • Costley, J. (2020). Using cognitive strategies overcomes cognitive load in online learning environments. Interactive Technology and Smart Education, 17(2), 215-228.
  • Council of Higher Education [CHE-YÖK] (2020). Yök sanal laboratuvar projesi"nin tanıtımı yapıldı: 2024. Retrieved from https://www.yok.gov.tr/Sayfalar/Haberler/2020/yok-sanal-laboratuvar-projesi-tanitildi.aspx
  • Dönmez, O., Akbulut, Y., Telli, E., Kaptan, M., Özdemir, İ. H., & Erdem, M. (2022). In search of a measure to address different sources of cognitive load in computer-based learning environments. Education and Information Technologies, 27(7), 10013-10034.
  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Rev. ed.). Bradford Books/ MIT Press.
  • Frederiksen, J. G., Sørensen, S. M. D., Konge, L., Svendsen, M. B. S., Nobel-Jørgensen, M., Bjerrum, F., & Andersen, S. A. W. (2020). Cognitive load and performance in immersive virtual reality versus conventional virtual reality simulation training of laparoscopic surgery: a randomized trial. Surgical Endoscopy, 34(3), 1244-1252.
  • Gorsuch, R. L. (1974). Factor analysis. Saunders
  • Guilford, J. P. (1954). Psychometric methods (2nd ed.). McGraw-Hill
  • Gürcan, F., & Özyurt, Ö. (2020). E-öğrenme araştırmalarındaki temel eğilimler ve bilgi alanları: 2008-2018 yılları arasında yayımlanan makalelerle konu modelleme analizi [Emerging trends and knowledge domains in e-learning researches: topic modeling analysis with the articles published between 2008-2018]. Journal of Computer and Education Research, 8(16), 738-756.
  • Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998). Multivariate Data Analysis (5th ed.). Prentice Hall
  • Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1, 1-13.
  • Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
  • Huang, C. L., Luo, Y. F., Yang, S. C., Lu, C. M., & Chen, A. S. (2020). Influence of students’ learning style, sense of presence, and cognitive load on learning outcomes in an immersive virtual reality learning environment. Journal of Educational Computing Research, 58(3), 596-615.
  • Karaçam, Z. (2019). Ölçme araçlarının Türkçeye uyarlanması [Adaptation of scales to Turkish]. Ebelik ve Sağlık Bilimleri Dergisi, 2(1), 28-37.
  • Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 8, 1–8.
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4. Edition). Guilford Publications, Inc.
  • Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023). Development and validation of a theory-based questionnaire to measure different types of cognitive load. Educational Psychology Review, 35(1), 9.
  • Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.
  • Leung, M., Low, R., & Sweller, J. (1997). Learning from equations or words. Instructional Science, 25, 37–70.
  • Marton, F., & Saljö, R. (1984). Approaches to learning. In F. Marton, D. Hounsell, & D. Entwistle (Eds.), The experience of learning (pp. 39–58). Scottish Academic Press.
  • Mayer, R. E. (2001).Multimedia learning. Cambridge University Press.
  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
  • Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.
  • Mayer, R. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36(1), 8.
  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.
  • Plass, J. L., Moreno, R., & Brünken, R. (2010). Cognitive load theory. Cambridge University Press.
  • Scherer, R. F. (1988). Dimensionality of coping: Factor stability using the ways of coping questionnaire. Psychological Report, 62, 76-770. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469-508.
  • Serbest, Y., Aydın, M. K., & Kuş, M. (2023). Üniversite dijital eğitim ortamını değerlendirme ölçeği (ÜDEODÖ): Uyarlama, geçerlik ve güvenirlik çalışması [Assessing university digital educational environment (AUDEE scale): Adaptation, validity and reliability study]. Journal of Computer and Education Research, 11(21), 356-375.
  • Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171-196.
  • Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12(2), 257-285.
  • Sweller, J. (1994). Instructional design in technical areas. Australian Society for Educational Technology.
  • Sweller, J. (1999). Instructional design in technical areas. ACER Press.
  • Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19–30). University Press.
  • Sweller, J. (2011). Cognitive load theory. In psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press.
  • Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68, 1–16.
  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitiveload theory. Springer.
  • Tavşancıl, E. (2005). Tutumların ölçülmesi ve SPSS ile veri analizi [Measurement of attitudes and data analysis with SPSS]. Nobel Yayın.
  • van Merriënboer, J. J. G. (1997). Training complex cognitive skills. Educational Tech. Press.

Reliability and Validity of Turkish Version of the Multidimensional Cognitive Load Scale for Virtual Environments

Yıl 2025, Cilt: 13 Sayı: 25, 431 - 453

Öz

This study aimed to adapt the “Multidimensional Cognitive Load Scale for Virtual Environments Scale (MCLSVE)” into Turkish, while evaluating its validity and reliability. A survey model was used for the adaptation process, utilizing the scale developed by Andersen and Makransky (2021), which is now referred to as the “MCLSVE-TR” The scale comprises five subscales: Intrinsic Load, Extraneous Load Instruction, Extraneous Load Interactions, Extraneous Load Environment, and Germane Load. The sample group for the study was 203 volunteer university students selected using a convenience sampling technique. Exploratory Factor Analysis (EFA) was conducted to determine the factor structure of the scale. As a result of EFA, it was revealed that the scale consisted of 18 items and 5 sub-dimensions, and these dimensions explained 82,34% of the total variance. In addition, Confirmatory Factor Analysis (CFA) confirmed the five-factor structure. Pearson correlation analysis to determine the relationship between scale factors, and Cronbach Alpha coefficient to determine the reliability level of scale factors were used. The findings confirmed that the Turkish adaptation of the MCLSVE is both valid and reliable.

Etik Beyan

Ethical Committee Permission Information Name of the board that carries out ethical assessment: Hacettepe University Scientific Research Ethics Committee for Social and Human Sciences The date and number of the ethical assessment decision: 22.10.2024- 00003838706

Kaynakça

  • Akan, S., & Keskin, S. (2023). Etkileşimli öğretimsel videoların başarı, bilişsel yük ve video kapılma üzerine etkisi [The effects of interactive instructional videos on achievement, cognitive load and video engagement]. Erzincan University Journal of Education Faculty, 25(2), 198-208.
  • Akhter, F. (2017). Virtual learning environment: how well designed multimedia lowers the learners'cognitive load. Journal of International Business Research, 16(1), 1-6.
  • Andersen, M. S., & Makransky, G. (2021). The validation and further development of a multidimensional cognitive load scale for virtual environments. Journal of Computer Assisted Learning, 37(1), 183-196.
  • Anderson, L. W. (1988). Attitudes and their measurement. In J. P. Keeves (Ed.), Educational research, methodology and measurement: An international handbook. Pergamon.
  • Ayres, P. (2018). Subjective measures of cognitive load—What can they reliably measure? In Cognitive load measurement and application—A theoretical framework for meaningful research and practice (1st ed.). Routledge.
  • Baddeley, A. (1999). Human memory. Allyn & Bacon.
  • Bartlett, M.S. (1950). Tests of significance in factor analysis. British Journal of Psychology, 3, 77-85.
  • Barut Tuğtekin, E. (2020). Çoklu ortamla öğrenmede farklı etiketleme yaklaşımlarının başarı, bilişsel yük ve motivasyona etkisi [Effect of different tagging approaches on achievement, cognitive load and motivation in multimedia learning]. Anadolu Üniversitesi: Yayımlanmamış doktora tezi.
  • Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191. Brown, T. A. (2006). Confirmatory factor analysis for applied research. Guilford.
  • Büyüköztürk, Ş. (2013). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (18. bs.) [Data analysis handbook for social sciences: Statistics, research design, SPSS applications and interpretation (18th ed.)]. Pegem Akademi.
  • Cerny, B. A., & Kaiser, H. F. (1977). A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate behavioral research, 12(1), 43-47.
  • Chi, M.,Glaser, R.,& Rees, E. (1982). Expertise in problemsolving. InR. Sternberg (Ed.), Advances in thepsychologyofhumanintelligence (pp. 7–75). Erlbaum.
  • Choi, Y., & Lee, H. (2022). Psychometric properties for multidimensional cognitive load scale in an E-learning environment. International Journal of Environmental Research and Public Health, 19(10), 5822.
  • Cierniak, G., Gerjets, P., & Scheiter, K. (2009). Expertise reversal in multimedia learning: Subjective load ratings and viewing behavior as cognitive process indicators. Proceedings of the Annual Meeting of the Cognitive Science Society, 31(31), 1906–1911.
  • Clark, R. C. (1999). Developing technical training (2nd ed.). International Society for Performance Improvement.
  • Costley, J. (2020). Using cognitive strategies overcomes cognitive load in online learning environments. Interactive Technology and Smart Education, 17(2), 215-228.
  • Council of Higher Education [CHE-YÖK] (2020). Yök sanal laboratuvar projesi"nin tanıtımı yapıldı: 2024. Retrieved from https://www.yok.gov.tr/Sayfalar/Haberler/2020/yok-sanal-laboratuvar-projesi-tanitildi.aspx
  • Dönmez, O., Akbulut, Y., Telli, E., Kaptan, M., Özdemir, İ. H., & Erdem, M. (2022). In search of a measure to address different sources of cognitive load in computer-based learning environments. Education and Information Technologies, 27(7), 10013-10034.
  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Rev. ed.). Bradford Books/ MIT Press.
  • Frederiksen, J. G., Sørensen, S. M. D., Konge, L., Svendsen, M. B. S., Nobel-Jørgensen, M., Bjerrum, F., & Andersen, S. A. W. (2020). Cognitive load and performance in immersive virtual reality versus conventional virtual reality simulation training of laparoscopic surgery: a randomized trial. Surgical Endoscopy, 34(3), 1244-1252.
  • Gorsuch, R. L. (1974). Factor analysis. Saunders
  • Guilford, J. P. (1954). Psychometric methods (2nd ed.). McGraw-Hill
  • Gürcan, F., & Özyurt, Ö. (2020). E-öğrenme araştırmalarındaki temel eğilimler ve bilgi alanları: 2008-2018 yılları arasında yayımlanan makalelerle konu modelleme analizi [Emerging trends and knowledge domains in e-learning researches: topic modeling analysis with the articles published between 2008-2018]. Journal of Computer and Education Research, 8(16), 738-756.
  • Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998). Multivariate Data Analysis (5th ed.). Prentice Hall
  • Hambleton, R. K., & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1, 1-13.
  • Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
  • Huang, C. L., Luo, Y. F., Yang, S. C., Lu, C. M., & Chen, A. S. (2020). Influence of students’ learning style, sense of presence, and cognitive load on learning outcomes in an immersive virtual reality learning environment. Journal of Educational Computing Research, 58(3), 596-615.
  • Karaçam, Z. (2019). Ölçme araçlarının Türkçeye uyarlanması [Adaptation of scales to Turkish]. Ebelik ve Sağlık Bilimleri Dergisi, 2(1), 28-37.
  • Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 8, 1–8.
  • Kline, R. B. (2015). Principles and practice of structural equation modeling (4. Edition). Guilford Publications, Inc.
  • Krieglstein, F., Beege, M., Rey, G. D., Sanchez-Stockhammer, C., & Schneider, S. (2023). Development and validation of a theory-based questionnaire to measure different types of cognitive load. Educational Psychology Review, 35(1), 9.
  • Leppink, J., Paas, F., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.
  • Leung, M., Low, R., & Sweller, J. (1997). Learning from equations or words. Instructional Science, 25, 37–70.
  • Marton, F., & Saljö, R. (1984). Approaches to learning. In F. Marton, D. Hounsell, & D. Entwistle (Eds.), The experience of learning (pp. 39–58). Scottish Academic Press.
  • Mayer, R. E. (2001).Multimedia learning. Cambridge University Press.
  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.
  • Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press.
  • Mayer, R. E. (2024). The past, present, and future of the cognitive theory of multimedia learning. Educational Psychology Review, 36(1), 8.
  • Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.
  • Plass, J. L., Moreno, R., & Brünken, R. (2010). Cognitive load theory. Cambridge University Press.
  • Scherer, R. F. (1988). Dimensionality of coping: Factor stability using the ways of coping questionnaire. Psychological Report, 62, 76-770. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469-508.
  • Serbest, Y., Aydın, M. K., & Kuş, M. (2023). Üniversite dijital eğitim ortamını değerlendirme ölçeği (ÜDEODÖ): Uyarlama, geçerlik ve güvenirlik çalışması [Assessing university digital educational environment (AUDEE scale): Adaptation, validity and reliability study]. Journal of Computer and Education Research, 11(21), 356-375.
  • Skulmowski, A., & Xu, K. M. (2022). Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 34(1), 171-196.
  • Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12(2), 257-285.
  • Sweller, J. (1994). Instructional design in technical areas. Australian Society for Educational Technology.
  • Sweller, J. (1999). Instructional design in technical areas. ACER Press.
  • Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19–30). University Press.
  • Sweller, J. (2011). Cognitive load theory. In psychology of learning and motivation (Vol. 55, pp. 37-76). Academic Press.
  • Sweller, J. (2020). Cognitive load theory and educational technology. Educational Technology Research and Development, 68, 1–16.
  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitiveload theory. Springer.
  • Tavşancıl, E. (2005). Tutumların ölçülmesi ve SPSS ile veri analizi [Measurement of attitudes and data analysis with SPSS]. Nobel Yayın.
  • van Merriënboer, J. J. G. (1997). Training complex cognitive skills. Educational Tech. Press.
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Kültürlerarası Ölçek Uyarlama
Bölüm Araştırma Makalesi
Yazarlar

Talha Yıldız 0000-0002-2553-8777

G. Alev Özkök 0000-0003-4519-6521

Erken Görünüm Tarihi 4 Mart 2025
Yayımlanma Tarihi
Gönderilme Tarihi 16 Aralık 2024
Kabul Tarihi 4 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 25

Kaynak Göster

APA Yıldız, T., & Özkök, G. A. (2025). Reliability and Validity of Turkish Version of the Multidimensional Cognitive Load Scale for Virtual Environments. Journal of Computer and Education Research, 13(25), 431-453.

Creative Commons Lisansı


Bu eser Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır.


Değerli Yazarlar,

JCER dergisi 2018 yılından itibaren yayımlanacak sayılarda yazarlarından ORCID bilgilerini isteyecektir. Bu konuda hassasiyet göstermeniz önemle rica olunur.

Önemli: "Yazar adından yapılan yayın/atıf taramalarında isim benzerlikleri, soyadı değişikliği, Türkçe harf içeren isimler, farklı yazımlar, kurum değişiklikleri gibi durumlar sorun oluşturabilmektedir. Bu nedenle araştırmacıların tanımlayıcı kimlik/numara (ID) edinmeleri önem taşımaktadır. ULAKBİM TR Dizin sistemlerinde tanımlayıcı ID bilgilerine yer verilecektir.

Standardizasyonun sağlanabilmesi ve YÖK ile birlikte yürütülecek ortak çalışmalarda ORCID kullanılacağı için, TR Dizin’de yer alan veya yer almak üzere başvuran dergilerin, yazarlardan ORCID bilgilerini talep etmeleri ve dergide/makalelerde bu bilgiye yer vermeleri tavsiye edilmektedir. ORCID, Open Researcher ve Contributor ID'nin kısaltmasıdır.  ORCID, Uluslararası Standart Ad Tanımlayıcı (ISNI) olarak da bilinen ISO Standardı (ISO 27729) ile uyumlu 16 haneli bir numaralı bir URI'dir. http://orcid.org adresinden bireysel ORCID için ücretsiz kayıt oluşturabilirsiniz. "