Adaptation of the Different Types of Cognitive Load Scale into Turkish: A Validity and Reliability Stud

Abstract

This study aims to adapt the "Different Types of Cognitive Load Scale" developed by Leppink et al. (2013) into Turkish and to conduct validity-reliability analyses. The study aimed to address the lack of a comprehensive tool in the Turkish literature that can separate and measure internal, external and effective load types within the framework of Cognitive Load Theory. The scale was translated into Turkish in line with expert opinions, language equivalence tests were applied and its three-factor structure was confirmed with confirmatory factor analysis. In the study, analyses were conducted on data collected from 221 associate degree students receiving distance education at a state university. The fit indices (RMSEA, CFI, TLI etc.) of the scale were found to be at an acceptable level and Cronbach's Alpha coefficients were .93 in general and ranged between .86 and .98 in the sub-dimensions. The results showed that the scale offered high reliability and validity both in general and in the sub-dimensions. It is anticipated that this scale will make a significant contribution to the Turkish literature and will be an effective tool in research on measuring cognitive load in educational environments.

Keywords

• Cognitive Load
• Intrinsic Cognitive Load
• Extraneous Cognitive Load
• Germane Cognitive Load

Farklı Bilişsel Yük Türleri Ölçeğinin Türkçe Uyarlaması: Geçerlilik ve Güvenirlik Çalışması

Öz

Bu çalışma, Leppink ve diğerleri (2013) tarafından geliştirilen "Farklı Bilişsel Yük Türleri Ölçeği"nin Türkçe uyarlamasını ve geçerlik-güvenirlik analizlerini gerçekleştirmeyi amaçlamaktadır. Araştırma, bilişsel yük kuramı çerçevesinde içsel, dışsal ve etkili yük türlerini ayrıştırarak ölçebilen kapsamlı bir aracın Türkçe literatürdeki eksikliğini gidermeyi hedeflemiştir. Ölçek, uzman görüşleri doğrultusunda Türkçeye çevrilmiş, dil eşdeğerliği testleri uygulanmış ve doğrulayıcı faktör analizi ile üç faktörlü yapısı doğrulanmıştır. Çalışmada, bir devlet üniversitesinde uzaktan eğitim alan 221 ön lisans öğrencisinin katılımıyla toplanan veriler üzerinde analizler yapılmıştır. Ölçeğin uyum indeksleri (RMSEA, CFI, TLI vb.) kabul edilebilir düzeyde bulunmuş ve Cronbach Alfa katsayıları genel olarak .93, alt boyutlarda ise .86 ile .98 arasında değişmiştir. Sonuçlar, ölçeğin hem genel hem de alt boyutlar düzeyinde yüksek güvenilirlik ve geçerlilik sunduğunu göstermiştir. Bu ölçeğin, Türkçe literatüre önemli bir katkı sunacağı ve eğitim ortamlarında bilişsel yük ölçümüne yönelik araştırmalarda etkili bir araç olacağı öngörülmektedir.

Anahtar Kelimeler

• Bilişsel Yük
• İçsel Bilişsel Yük
• Dışsal Bilişsel Yük
• Etkili Bilişsel Yük

Kaynakça

1. Albus, P., Vogt, A., & Seufert, T. (2021). Signaling in virtual reality influences learning outcome and cognitive load. Computers & Education, 166, 104154. https://doi.org/10.1016/j.compedu.2021.104154
2. Ayres, P. (2006). Impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20(3), 287–298. https://doi.org/10.1002/acp.1245
3. Büyüköztürk, Ş. (2017). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum. Pegem Akademi.
4. Büyüköztürk, Ş. (2021). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni SPSS uygulamaları ve yorum. Pegem Akademi.
5. Caldiroli, C. L., Gasparini, F., Corchs, S., Mangiatordi, A., Garbo, R., Antonietti, A., & Mantovani, F. (2023). Comparing online cognitive load on mobile versus PC-based devices. Personal and Ubiquitous Computing, 27(2), 495–505. https://doi.org/10.1007/s00779-022-01707-8
6. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8(4), 293–332. https://doi.org/10.1207/s1532690xci0804_2
7. Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, 25(2), 315–324. https://doi.org/10.1016/j.chb.2008.12.020
8. Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 173–178.

9. Çakıroğlu, Ü., Suiçmez, S. S., Kurtoğlu, Y. B., Sarı, A., Yıldız, S., & Öztürk, M. (2018). Exploring perceived cognitive load in learning programming via Scratch. Research in Learning Technology, 26, Article 2034. https://doi.org/10.25304/rlt.v26.2034
10. Çokluk, Ö., Şekercioğlu, G. ve Büyüköztürk, Ş. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları (Vol. 2). Ankara: Pegem akademi.
11. Dindar, M., Korkmaz, Ö., & Özdemir, E. (2014). The effect of static and animated graphics on cognitive load in test items. Educational Sciences: Theory & Practice, 14(3), 1077–1084. https://doi.org/10.12738/estp.2014.3.2028
12. Fraenkel, J. R., Wallen, N. E., & Hyun, H. (2012). How to design and evaluate education research. McGraw-Hill.
13. Guilford, J. P. (1954). Psychometric methods. McGraw-Hill.
14. Guo, J., Dai, Y., Wang, C., Wu, H., Xu, T., & Lin, K. (2020). A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification. Software: Practice and Experience, 50(11), 2046–2064. https://doi.org/10.1002/spe.2730
15. Gupta, U., & Zheng, R. Z. (2020). Cognitive load in solving mathematics problems: Validating the role of motivation and the interaction among prior knowledge, worked examples, and task difficulty. European Journal of STEM Education, 5(1), Article 5. https://doi.org/10.20897/ejsteme/9252
16. Güngör, D. (2016). Psikolojide ölçme araçlarının geliştirilmesi ve uyarlanması kılavuzu. Türk Psikoloji Yazıları, 19(38), 104–112.
17. Hair, J. F., Black, W. C., Tatham, R. L., & Anderson, R. E. (2010). Multivariate data analysis. Prentice Hall.
18. Han, P., Yang, L., & Xu, L. (2020, Ağustos 28). Analysis and optimization of cognitive load in the design of online teaching on the internet. In 2020 4th International Seminar on Education, Management and Social Sciences (pp. 734–737). Atlantis Press. https://doi.org/10.2991/assehr.k.200826.148
19. Kılıç, E. ve Karadeniz, Ş. (2004). Hiper ortamlarda öğrencilerin bilişsel yüklenme ve kaybolma düzeylerinin belirlenmesi. Kuram ve Uygulamada Eğitim Yönetimi Dergisi, 10(4), 562–579.
20. Küçük, S., Kapakin, S., & Göktaş, Y. (2016). Learning anatomy through mobile augmented reality: Effects on achievement and cognitive load. Educational Technology & Society, 19(3), 134–142. https://doi.org/10.1002/ase.1603
21. Leppink, J., & Duvivier, R. (2016). Twelve tips for medical curriculum design from a cognitive load theory perspective. Medical Teacher. 38(7), 669–674. https://doi.org/10.3109/0142159x.2015.1132829
22. Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., & Van Merriënboer, J. J. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1
23. Liao, C.-W., Chen, C.-H., & Shih, S.-J. (2019). The interactivity of video and collaboration for learning achievement, intrinsic motivation, cognitive load, and behavior patterns in a digital game-based learning environment. Computers & Education, 133, 43–55. https://doi.org/10.1016/j.compedu.2019.01.013
24. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99. https://doi.org/10.1037/1082-989X.4.1.84
25. Montrieux, H., & Schellens, T. (2018, Mart 5–7). The impact of tablet devices on high school students' cognitive load and learning. In 12th International Technology, Education and Development Conference (pp. 1591–1596). Valencia, Spain. https://doi.org/10.21125/inted.2018.0277
26. Özer, O., & Kılıç, F. (2018). The effect of mobile-assisted language learning environment on EFL students’ academic achievement, cognitive load, and acceptance of mobile learning tools. EURASIA Journal of Mathematics, Science and Technology Education, 14(7), 2915–2928. https://doi.org/10.29333/ejmste/90992
27. Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84(4), 429–434. https://doi.org/10.1037/0022-0663.84.4.429
28. Paas, F. G., & Van Merriënboer, J. J. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122–133. https://doi.org/10.1037/0022-0663.86.1.122
29. Skulmowski, A. (2023). Learners emphasize their intrinsic load if asked about it first: Communicative aspects of cognitive load measurement. Mind, Brain, and Education, 17(3), 165–169. https://doi.org/10.1111/mbe.12369
30. Skulmowski, A., & Rey, G. D. (2017). Measuring cognitive load in embodied learning settings. Frontiers in Psychology, 8, Article 1191. https://doi.org/10.3389/fpsyg.2017.01191
31. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5
32. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics Allyn and Bacon.
33. Thees, M., Kapp, S., Strzys, M. P., Beil, F., Lukowicz, P., & Kuhn, J. (2020). Effects of augmented reality on learning and cognitive load in university physics laboratory courses. Computers in Human Behavior, 108, 106316. https://doi.org/10.1016/j.chb.2020.106316
34. Velicer, W. F., & Fava, J. L. (1998). Affects of variable and subject sampling on factor pattern recovery. Psychological Methods, 3(2), 231. https://doi.org/10.1037/1082-989X.3.2.231
35. Wang, C. C., Cheng, P. K. H., & Wang, T. H. (2022). Measurement of extraneous and germane cognitive load in the mathematics addition task: An event-related potential study. Brain Sciences, 12(8), Article 1036. https://doi.org/10.3390/brainsci12081036
36. Young, J. Q., Ten Cate, O., O'Sullivan, P. S., & Irby, D. M. (2016). Unpacking the complexity of patient handoffs through the lens of cognitive load theory. Teaching and Learning in Medicine, 28(1), 88–96. https://doi.org/10.1080/10401334.2015.1107491
37. Young, J. Q., van Merrienboer, J., Durning, S., & Ten Cate, O. (2014). Cognitive load theory: Implications for medical education: AMEE guide no. 86. Medical Teacher, 36(5), 371–384. https://doi.org/10.3109/0142159x.2014.889290
38. Yoo, G., Kim, H., & Hong, S. (2023). Prediction of cognitive load from electroencephalography signals using long short-term memory network. Bioengineering, 10(3), Article 361. https://doi.org/10.3390/bioengineering10030361