E-ÖĞRENME ORTAMLARINDA ALGILANAN KARMAŞIK GÖREV PERFORMANS ÖLÇEĞİ

Year 2019, Volume: 9 Issue: 1, 276 - 291, 31.01.2019

Sacide Güzin Mazman Akar Arif Altun

https://doi.org/10.17943/etku.466073

Abstract

Bu çalışmanın amacı e-öğrenme ortamlarında algılanan
karmaşık görev performansını belirlemeye yönelik bir ölçme aracı
geliştirmektir. Geliştirilmesi hedeflenen ölçek Mazman ve Altun (2012)
tarafından ortaya konulan “Karmaşık Görev Sürecindeki Bilişsel Stratejiler”
modeli temelinde hazırlanmıştır. Modeldeki stratejiler temel alınarak madde
havuzu hazırlanmış, uzman görüşleri ve küçük grupla pilot uygulama sonucunda
ölçek formu düzenlenmiştir. Araştırmanın çalışma grubunu öğrenim görmekte olan
232 lisans öğrencisi oluşturmaktadır. Çalışmada yapı geçerliliği için öncelikle
açımlayıcı faktör analizi uygulanmış ve iki faktör (“bilgi toplama”,
“çözüm-kontrol”) altında 10 maddeden oluşan bir yapı elde edilmiştir. Ardından
doğrulayıcı faktör analizi uygulanarak elde edilen yapının iyi uyum gösterdiği
ortaya konulmuştur. Yakınsama ve ayırt edici geçerlilik için ise açıklanan
ortalama varyanslar incelenmiştir. Ölçeğin güvenirlik çalışmaları için iç tutarlık
katsayısı ve yapı güvenirliği hesaplanmış, ayrıca madde toplam korelasyonları
raporlanmıştır. Analizler sonucunda ölçeğin geçerli ve güvenilir olduğu
bulunmuştur. Uygulanan ölçeğe ilişkin toplam puanlar incelendiğinde kadınların
erkeklerden anlamlı derecede performans algılarının yüksek olduğu bulunurken,
sınıf düzeyine göre algılanan performansın değişmediği ortaya konulmuştur.

Keywords

karmaşık görev, e-öğrenme, bilişsel stratejileri

References

• Aula, A.and Nordhausen, K. (2006). Modeling successful performance in Web searching, Journal of the American Society of Information Science and Technology 57 (12). 1678-1693.
• Bainbridge, L. (1997). The change in concepts needed to account for human behaviour in complex dynamic tasks. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 27, 351–359.
• Brand-Gruwel, S., Wopereis, I., & Vermetten, Y. (2005). Information problem solving by experts and novice: Analysis of a complex cognitive skill. Computers in Human Behavior, 21, 487–508.
• Care, E., Scoular, C., & Griffin, P. (2016). Assessment of collaborative problem solving in education environments. Applied Measurement in Education, 29(4), 250–264.
• Csapó, B., & Joachim, F. (Eds.). (2017). Educational Research and Innovation The Nature of Problem Solving Using Research to Inspire 21st Century Learning: Using Research to Inspire 21st Century Learning. OECD Publishing.
• Ertmer, P. A., Stepich, D. A., York, C. S., Stickman, A., Wu, X., Zurek, S., & Goktas, Y. (2008a). How instructional design experts use knowledge and experience to solve ill‐structured problems. Performance Improvement Quarterly, 21(1), 17-42.
• Ertmer, P. A., Stepich, D. A., Flanagan, S., Kocaman, A., Reiner, C., Reyes, L., Santone, A. & Ushigusa, S. (2008b). Ill-structured problem solving: Helping instructional design novices perform like experts.
• Field, A.P. (2005). Discovering Statistics using SPSS: (and sex and drugs and rock ‘n’ roll). London: Sage Publications.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
• Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236.
• Greiff, S., Holt, D., & Funke, J. (2013). Perspectives on problem solving in cognitive research and educational assessment: analytical, interactive, and collaborative problem solving. Journal of Problem Solving (The), 5, 71-91.
• Hackling, M. (1984). Expert and novice performance in solving genetic pedigree problems. In Proceedings of the annual conference of the Australian Association for Research in Education. pp. 304-309.
• Hooper, D., Coughlan, J., Mullen, M. Structural Equation Modelling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
• Jiang, Z., & Benbasat, I. (2007). The effects of presentation formats and task complexity on online consumers' product understanding. MIS Quarterly, 475-500.
• Kirschner, P. A., & Van Merriënboer, J. (2008). Ten steps to complex learning a new approach to instruction and instructional design. T.L. Good (Ed.), 21st century education: A reference handbook, (pp. 244-253). Sage, Thousand Oaks, CA .
• Kline, R.B. (2010). Principles and practice of structural equation modeling, 2nd ed. New York, NY: Guilford Press.
• Kuhn, D., & Pease, M. (2008). What needs to develop in the development of inquiry skills? Cognition and Instruction, 26(4), 512–559.
• Mair, C., M. Martincova ve Shepperd, M. (2009). A Literature Review of Expert Problem Solving using Analogy. 13th International Conference on Evaluation & Assessment in Software Engineering (EASE 2009), Durham, UK, BCS.
• Mayer, R.E. and Wittrock, M.C. (2006) Problem solving. In: Alexander, P.A. and Winne, P.H., Eds., Handbook of Educational Psychology, Macmillian, New York.
• Mazman, S.G. & Altun, A. (2012). Modeling cognitive strategies during complex task performing process. Turkish Online Journal of Qualitative Inquiry, 3(4), 1-27.
• Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling, 10(4), 622-647.
• Organisation for Economic Co-operation and Development. (2010). PISA 2012 problem solving framework. Paris, France: OECD.
• Peckham, T. (2012). Detection of cognitive strategies in reading comprehension tasks. In International Conference on Intelligent Tutoring Systems (pp. 585-587). Springer, Berlin, Heidelberg.
• Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed?. Educational psychologist, 40(1), 1-12.
• Raptis, G. E., Katsini, C., Belk, M., Fidas, C., Samaras, G., & Avouris, N. (2017). Using eye gaze data and visual activities to infer human cognitive styles: method and feasibility studies. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 164-173). ACM.
• Scherer, R., Greiff, S., & Hautamäki, J. (2015). Exploring the relation between time on task and ability in complex problem solving. Intelligence, 48, 37-50.
• Wüstenberg, S., Stadler, M., Hautamäki, J., & Greiff, S. (2014). The role of strategy knowledge for the application of strategies in complex problem solving tasks. Technology, Knowledge and Learning, 19(1-2), 127-146.