PRE-SERVICE TEACHERS’ FLOW, ANXIETY AND COGNITIVE LOAD LEVELS IN ROBOTICS PROGRAMMING

Year 2018, Volume: 8 Issue: 2, 125 - 156, 15.07.2018

Burak Şişman , Sevda Küçük

https://doi.org/10.17943/etku.366193

Cited By: 4

Abstract

The aim of this study was to compare pre-service teachers’ flow experience, anxiety and cognitive loads in the process of robotics programming, based on whether they were experienced or inexperienced. The sample of the study consisted of 19 pre-service teachers (16 females, 3 males) from different specialties. Within the scope of the study, pre-service teachers carried out five robotics programming activities. Two of these activities were grouped as beginner level and three as experience-requiring activities. The Flow Experience Scale and the Cognitive Load Scale were used as data collection tools in the study. Wilcoxon signed rank test was used in the analysis of the data. It was found that, in general, the flow experience and cognitive load levels of the pre-service teachers were high, and their anxiety levels were low in the process of robotics programming. It was also found that the pre-service teachers’ level of flow experience was significantly higher, and their cognitive loads were lower if they were experienced. Anxiety levels were not significantly different in the either case. The results were discussed, and suggestions were presented in order to guide future studies.

Keywords

pre-service teachers, robotics, programming, flow, anxiety, cognitive load

ÖĞRETMEN ADAYLARININ ROBOTİK PROGRAMLAMADA AKIŞ, KAYGI ve BİLİŞSEL YÜK SEVİYELERİ

Abstract

Bu çalışmanın amacı öğretmen adaylarının robotik programlama sürecindeki akış, kaygı ve bilişsel yük seviyelerinin deneyimsiz-deneyimli olma durumlarına göre karşılaştırılmasıdır. Çalışmanın örneklemini farklı branşlardan 19 öğretmen adayı (16 kadın, 3 erkek) oluşturmaktadır. Çalışma kapsamında öğretmen adayları beş robotik programlama etkinliği gerçekleştirmişlerdir. Bu etkinliklerden ikisi başlangıç düzeyi, üçü de deneyim gerektiren olarak gruplandırılmıştır. Çalışmada veri toplama aracı olarak akış yaşantısı ölçeği ve bilişsel yük ölçeği kullanılmıştır. Verilerin analizinde Wilcoxon işaretli sıralar testi kullanılmıştır. Elde edilen sonuçlara göre öğretmen adaylarının genel olarak robotik programlama sürecinde akış ve bilişsel yük seviyelerinin yüksek, kaygılarının ise düşük olduğu ortaya çıkmıştır. Deneyimsiz-deneyimli olma durumlarına göre yapılan karşılaştırmada; öğretmen adaylarının deneyimli oldukları durumlarda akış düzeylerinin anlamlı olarak yüksek, bilişsel yük seviyelerinin de düşük olduğu ortaya çıkmıştır. Kaygı düzeylerinin ise her iki durumda anlamlı düzeyde farklılaşmadığı belirlenmiştir. Elde edilen sonuçlar tartışılarak gelecekteki çalışmalara yön verici nitelikte öneriler sunulmuştur.

Keywords

öğretmen adayları, robotik, programlama, akış, kaygı, bilişsel yük

References

• Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63-71.
• Arlegui, J., Pina, A., ve Moro, M. (2013). A PBL approach using virtual and real robots (with BYOB and LEGO NXT) to teaching learning key competences and standard curricula in primary level. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (pp.323-328). New York, NY, USA: ACM.
• Bruder, S., ve Wedeward, K. (2003). Robotics in the classroom. IEEE Robotics & Automation Magazine, 10(3), 25-29.
• Brünken, R, Plass, J. L., ve Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38(1), 53-61.
• Benitti, F.B.V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978-988.
• Çakmak, E. K. (2007). Çoklu ortamlarda dar boğaz: Aşırı bilişsel yüklenme. Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi, 27(2).
• Clark, R. C., Nguyen, F., & Sweller, J. (2011). Efficiency in learning: Evidence-based guidelines to manage cognitive load. John Wiley & Sons.Csikszentmihalyi, M. (1975). Beyond boredom and anxiety: Experiencing flow in work and play. San Fransisco: Josey-Bass Inc. Publishers.
• Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row.
• Csikszentmihalyi, M. (2003). Good business. Published by the Penguin Group. Penguin Putnam Inc., 375 Hudson Street, New York, New York 100014, U.S.A.
• Csikszentmihalyi, M., Latter, P., ve Weinkauff Duranso, C. (2017). Running Flow. Champaign: Human Kinetics.
• Eguchi, A. (2010). What is educational robotics? Theories behind it and practical implementation. Editör D. Gibson ve B. Dodge. Proceedings of Society for Information Technology & Teacher Education International Conference 2010 (pp. 4006-4014). Chesapeake, VA: AACE.
• Engeser, S., Rheinberg, F., Vollmeyer, R., ve Bischoff, J. (2005). Motivation, Flow-Erleben und Lernleistung in universitären Lernsettings 1 Dieser Beitrag wurde unter der geschäftsführenden Herausgeberschaft von Joachim C. Brunstein akzeptiert. Zeitschrift für pädagogische Psychologie, 19(3), 159-172.
• Ersoy, H., Madran, R. O., ve Gülbahar, Y. (2011). Programlama dilleri öğretimine bir model önerisi: robot programlama. XIII. Akademik Bilişim Konferansı, Malatya, Türkiye.
• Feldgen, M., ve Clúa, O. (2004). Games as a motivation for freshman students learn programming. Frontiers in Education, 2004. FIE 2004. 34th Annual (pp S1H/11–S1H/16 Vol. 3). IEEE.
• Fraenkel, J. R., Wallen, N. E., ve Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). New York: McGraw-Hill.
• Gerecke, U., ve Wagner, B. (2007). The challenges and benefits of using robots in higher education. Intelligent Automation and Soft Computing, 13(1), 29–43.
• Gura, M. (2011). Getting started with Lego robotics: A guide for K-12 educators. http://www.iste.org/images/excerpts/ROBOTS-excerpt.pdf adresinden 15 Ekim 2017 tarihinde alınmıştır.
• Hadjiachilleos, S., Avraamidou, L., ve Papastavrou, S. (2013). The use of lego technologies in elementary teacher preparation. Journal of Science Education and Technology, 22(5), 614-629.
• Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., Asbell-Clarke, J., ve Edwards, T. (2016). Challenging games help students learn: An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior, 54, 170-179.
• İşigüzel, B., ve Çam, S. (2014). The adaptation of Flow Short Scale to Turkish: A validity and reliability study. Journal of Human Sciences, 11(2), 788-801.
• Kay, J. S., Moss, J. G., Engelman, S., ve McKlin, T. (2014). Sneaking in through the back door: Introducing K-12 teachers to robot programming. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education (pp. 499-504). New York, NY, USA: ACM.
• Kılıç, E., ve Karadeniz, Ş. (2004). Hiper ortamlarda öğrencilerin bilişsel yüklenme ve kaybolma düzeylerinin belirlenmesi. Kuram ve Uygulamada Egitim Yönetimi Dergisi, 10(4), 562-579.
• Kiili, K. (2005). Digital game-based learning: Towards an experiential gaming model. The Internet and higher education, 8(1), 13-24.
• Kim, C., Kim, D., Yuan, J., Hill RB., Doshi, P., ve Thai, CN. (2015). Robotics to promote elementary education preservice teachers' STEM engagement, learning, and teaching. Computers & Education, 91, 14-31.
• Lahtinen, E., Ala-Mutka, K., ve Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin 37(3), pp 14–18.
• Lin, C., Liu, E.Z., Kou, C., Virnes, M., Sutinen, E., ve Cheng, S-S. (2009). A case analysis of creative spiral instruction model and students’ creative problem solving performance in a Lego® robotics course. Editör Chang, M., Kuo, R., Kinshuk, Chen, G.-D., Hirose, M.. Edutainment 2009. LNCS, vol. 5670, pp. 501-505. Heidelberg: Springer.
• Lin, C. H., Liu, E. Z. F., ve Huang, Y. Y. (2012). Exploring parents’ perceptions toward educational robots: Gender and socioeconomic difference. British Journal of Educational Technology, 43(1), E31-E34.
• Liu, E. Z-H., Lin, C-H., Feng, H-C., ve Hou, H-T. (2013). An analysis of teacher-student interaction patterns in a robotics course for kindergarten children: A pilot study. The Turkish Online Journal of Educational Technology, 12(1), 9-18.
• Liu, E. Z. F., Lin, C. H., ve Chang, C. S. (2010). Student satisfaction and self-efficacy in a cooperative robotics course. Social Behavior and Personality, 38(8), 1135-1146.
• Mayer, R. E. (2005). The Cambridge handbook of multimedia learning. Cambridge university press.
• Mayer, R. E., & Moreno, R. (2010). Techniques that reduce extraneous cognitive load and manage intrinsic cognitive load during multimedia learning. Editör J. L. Plass, R. Moreno ve R. Brünken, Cognitive load theory, (pp. 131-152) New York, NY: Cambridge University Press.
• Moneta, G. B., ve Csikszentmihalyi, M. (1996). The effect of perceived challenges and skills on the quality of subjective experience. Journal of personality, 64(2), 275-310.
• Munusturlar, S., Kurnaz, B., Yavuz, G., Özcan, Ö. ve Karaş, B. (2017). Boş Zaman Davranışını Açıklamaya Işık Tutan Kuramsal Yaklaşımlar. Ulusal Spor Bilimleri Dergisi 1(1), 1-19.
• Paas, F. G., ve Van Merriënboer, J. J. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human factors, 35(4), 737-743.
• Paas, F., ve Van Merrienboer, J. J. G. (1994). Instructional control of cognitive load in the training of complex cognitive tasks. Educational Psychology Review, 6, 351-372.
• Paas, F., Renkl, A., ve Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.
• Paas, F., Tuovinen, J. E., Tabbers, H., ve Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational psychologist, 38(1), 63-71.
• Papert, S. (1971). Teaching Children Thinking. Artifical Intelligence. Cambridge : Massachusetts Institute of Technology.
• Perritt, D. C. (2010). Including professional practice in professional development while improving middle school teaching in math. National Teacher Education Journal, 3(3), 73-76.
• Pittí, K., Curto, B., Moreno, V., & Rodríguez, M. J. (2013). Resources and features of robotics learning environments (RLEs) in Spain and Latin America. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (pp. 315-322). New York, NY, USA: ACM.
• Reigeluth, C. M. (1987). Lesson blueprints based on the elaboration theory of instruction. Editör C.M. Reigeluth. Instructional theories in action: Lessons illustrating selected theories and models. Hillsdale, NJ: Erlbaum.
• Rheinberg, F., Vollmeyer, R., & Rollett, W. (2000). Motivation and action in self-regulated learning. Editör M. Boekaerts, P. R. Pintrich, & M. Zeidner, Handbook of self-regulation (pp. 503-529).
• Rheinberg, F., Vollmeyer, R., & Engeser, S. (2003). Die Erfassung des Flow-Erlebens [The assessment of flow experience]. Editör J. Stiensmeier-Pelster ve F. Rheinberg. Diagnostik von Motivation und Selbstkonzept (pp. 261–279). Göttingen: Hogrefe.
• Sözbilir, M. (2014). Nedensel karşılaştırmalı araştırma yöntemi, Editör Mustafa Metin, Kuramdan uygulamaya eğitimde bilimsel araştırma yöntemleri, Pegem Akademi: Ankara.
• Sullivan, F. R., Moriarty, M. A. (2009). Robotics and discovery learning: Pedagogical beliefs, teacher practice, and technology integration. Journal of Technology and Teacher Education, 17(1), 109-142.
• Wang, L., ve Chen, M. (2010). The effects of game strategy and preference‐matching on flow experience and programming performance in game‐based learning. Innovations in Education and Teaching International, 47(1), 39-52.
• Yıldırım, Z. (2016). Öğretim Teknolojileri ve İleti Tasarımı. Editör Çağıltay K. ve Göktaş Y. Öğretim Teknolojilerinin Temelleri: Teoriler, Araştırmalar, Eğilimler. Ankara: PEGEM. pp. 279-296.