Ortaokul Beşinci Sınıf Öğrencileri ile ChatGPT Yapay Zeka Aracının Örüntü Görevleri Deneyimlerinin Karşılaştırmalı İncelenmesi

Yıl 2024, Cilt: 9 Sayı: 4, 294 - 320

Sude Ay , Menekşe Seden Tapan Broutın

Öz

ChatGPT yapay zeka aracına öğrencilerin sorduğu soruların cevaplarının öğrenci çözüm süreçleriyle ne derecede benzeyip farklılaştığının ortaya koyulması önemlidir. Bu doğrultuda çalışmanın amacı, ortaokul beşinci sınıf öğrencileri ile ChatGPT yapay zeka aracının örüntü görevlerine verdikleri cevaplar arasındaki farklılıkları incelemektir. Nitel yaklaşımla yürütülen çalışmada durum çalışması deseni kullanılmıştır. Çalışmanın katılımcıları İstanbul’da bir ortaokulda öğrenim gören 100 beşinci sınıf öğrencisi ve ChatGPT yapay zeka aracıdır. Çalışmada veri toplama aracı olarak örüntü çalışma kağıtları ve ChatGPT yapay zeka aracından alınan ekran görüntüleri kullanılmıştır. Çalışma kâğıdı dört tane sabit artan ve azalan sayı örüntüsü görevinden oluşmaktadır. Veri toplama yöntemi olarak klinik görüşme kullanılmıştır. Veri analizi içerik analizi ile gerçekleştirilmiştir. Çalışmanın bulgularına göre, öğrenciler örüntü görevlerinin yakın mesafedeki terimine ulaşmak için en çok örüntüyü devam ettirmeden ve rastgele dört işlem yapmadan yararlanmışlardır. ChatGPT yapay zeka aracı ise örüntü görevinin yakın mesafedeki terimine ulaşmak için örüntüyü devam ettirmeden ve formül yazmadan işlem yapmadan yararlanmıştır. Öğrenciler örüntü görevlerinin orta mesafedeki terimine ulaşmak için en çok örüntüyü devam ettirmeden ve rastgele dört işlem yapmadan yararlanmışlardır. ChatGPT yapay zeka aracı ise örüntü görevinin orta mesafedeki terimine ulaşmak için örüntüyü devam ettirmeden, formül yazmadan işlem yapmadan ve formül kullanmadan yararlanmıştır. Öğrenciler örüntü görevlerinin uzak mesafedeki terimine ulaşmak için en çok rastgele dört işlem yapmadan yararlanmışlardır. ChatGPT yapay zeka aracı ise örüntü görevinin uzak mesafedeki terimine ulaşmak için için örüntüyü devam ettirmeden, formül yazmadan işlem yapmadan ve formül kullanmadan yararlanmıştır. Öğrenciler örüntü görevlerinin tersine çevirme problemlerinde en çok rastgele dört işlem yapmadan yararlanmışlardır. ChatGPT yapay zeka aracı ise örüntü görevlerinin tersine çevirme problemlerinde formül kullanmadan ve örüntüyü devam ettirmeden yararlanmıştır. Öğrenciler örüntü görevlerinin uzak mesafedeki terimine ulaşmada ve tersine çevirme problemlerinde zorlanmışlardır. Bunlara ek olarak öğrencilerin en çok zorlandığı ve boş bıraktığı örüntü görevinin sabit azalan örüntü görevi olduğu görülmüştür.

Anahtar Kelimeler

ChatGPT, Örüntü, Ortaokul Öğrencileri, Yapay Zeka

Etik Beyan

Çalışmayla ilgili sosyal ve beşerî bilimler araştırma ve yayın etik kurul kararı ile IJHAR etik kurul onay belgesi sisteme yüklenmiştir. Makalenin herhangi bir aşamasında yazarlar arasında veya üçüncü kişilerle maddi veya manevi çatışma bulunmamaktadır.

Comparative Analysis of Pattern Tasks Experiences Between Fifth Grade Secondary School Students and ChatGPT Artificial Intelligence Tool

Öz

The technology that rapidly changes and advances the world necessitates its interaction in all areas of life. Considering the role of technology in education today, the depth of this interaction offers various opportunities for education. One of these opportunities is the technological tools used in educational settings. Technological tools, which add different dimensions to education, are quite significant. In this context, the use of artificial intelligence tools in educational environments in recent years is noteworthy. AI chatbots are particularly prevalent in education. Students and teachers can interact with these chatbots in written or spoken form. ChatGPT, developed by OpenAI, is one of the frequently used chatbots recently. It can generate texts similar to those constructed by humans, understand users' language, answer their questions, and perform certain tasks. In addition to being a large language model, ChatGPT is also used in various disciplines, including mathematics. Given the importance of patterns in algebra teaching, it is inevitable that children encounter pattern tasks at an early age. It is important to reveal the similarities and differences between the answers provided by students and those generated by the ChatGPT AI tool to pattern tasks. Accordingly, the aim of this study is to examine the differences between the answers given to pattern tasks by fifth grade students and the ChatGPT AI tool. In line with this aim, a qualitative approach using a case study design was employed. The study was limited to fifth grade students and the ChatGPT AI tool, with their responses to pattern tasks examined in detail. Purposeful sampling was used to select 8 fifth grade students and the ChatGPT AI tool as the units of analysis. Criterion sampling, a type of purposeful sampling, was utilized, with voluntariness and being in the fifth grade considered as criteria for participant selection. The participants comprised 100 fifth grade students studying at a middle school in Istanbul and the ChatGPT AI tool. Pattern worksheets and screenshots from the ChatGPT AI tool were used as data collection tools. The pattern worksheet consisted of four number pattern tasks with constant increasing and decreasing patterns, and a total of 16 sub-problems. The pattern tasks were developed by the researcher using achievements from the mathematics curriculum and literature to ensure content validity. The pattern worksheet was later administered to students participating in clinical interviews with additional questions for in-depth examination. Ethical committee approval and necessary permissions from the provincial directorate of national education were obtained prior to data collection. Clinical interviews were used as the data collection method. Initially, the pattern worksheets were administered to the study group, and students were provided with necessary information about the study before completing the worksheets. Students were given 80 minutes to answer the pattern tasks. Based on their responses to the pattern worksheet, students were categorized. To obtain rich data for comparison with the ChatGPT AI tool, clinical interviews were conducted with students who were considered to best represent each category. Data analysis was performed using content analysis. According to the study findings, students most frequently used random arithmetic operations without continuing the pattern to reach the close terms of the pattern tasks. The ChatGPT AI tool, however, utilized operations without continuing the pattern or writing formulas to reach the close terms of the pattern tasks. For the middle terms of the pattern tasks, students again predominantly used random arithmetic operations without continuing the pattern, whereas the ChatGPT AI tool used operations without continuing the pattern, writing formulas, or using formulas. To reach the distant terms of the pattern tasks, students mostly relied on random arithmetic operations, while the ChatGPT AI tool used operations without continuing the pattern, writing formulas, or using formulas. In inverse problems of the pattern tasks, students primarily used random arithmetic operations, while the ChatGPT AI tool employed formulas without continuing the pattern. Students faced difficulties and left blank responses particularly for the distant terms and inverse problems of the pattern tasks. Additionally, the most challenging and frequently left blank pattern task for students was the constant decreasing pattern task. This study is limited to fifth grade middle school students and the topic of patterns. In future studies, similar research can be repeated with different grade levels or subject areas to determine the extent to which ChatGPT can be utilized in students' instructional processes.

Anahtar Kelimeler

Artificial Intelligence, ChatGPT, Middle School Students, Pattern

Kaynakça

• Abdul-Kader, S. A., & Woods, J. C. (2015). Survey on chatbot design techniques in speech conversation systems. International Journal of Advanced Computer Science and Applications, 6(7).
• Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. Independently Published.
• Aygün, E. S. (2019). Problem çözme öğretimine yönelik oyunlaştırılmış uyarlanabilir bir zeki öğretim sisteminin tasarlanması [Yayımlanmamış yüksek lisans tezi]. Trabzon Üniversitesi.
• Azwary, F., Indriani, F., & Nugrahadi, D. T. (2016). Question answering system berbasis artificial intelligence markup language sebagai media informasi. Klik-Kumpulan Jurnal Ilmu Komputer, 3(1), 48-60.
• Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT. arXiv preprint.. https://doi.org/10.48550/arXiv.2303.04226
• Chomsky, N., Roberts, I., & Watumull, J. (2023). The false promise of ChatGPT. New York Times. https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html
• Deveci Topal, A., Dilek Eren, C., & Kolburan Geçer, A. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241-6265.
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., ... Wright, R. (2023). "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
• Fox, J. (2005). Child-initiated mathematical patterning in the pre-compulsory years. International for the Psychology of Mathematics Education, 2, 313-320.
• Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089
• Herbert, K., & Brown, R. H. (1997). Patterns as tools for algebraic reasoning. Teaching Children Mathematics, 3, 123- 128.
• Jordan, M. I., & Mitchell, T. M (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245)., 255-260.
• Kabiljagić, M., Wachtler, J., Ebner, M., & Ebner, M. (2022). Math trainer as a chatbot via system (push) messages for android. International Journal of Interactive Mobile Technologies (iJIM), 16(17), 75-87. https://doi.org/10.3991/ijim.v16i17.33351
• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
• Kim, K. J., & Han, H. J. (2021). A design and effect of maker education using educational artificial intelligence tools in elementary online environment. Journal of Digital Convergence, 19(6), 61- 71. https://doi.org/10.14400/J
• Kim, K., & Park, Y. (2017). A development and application of the teaching and learning model of artificial intelligence education for elementary students. Journal of The Korean Association of Information Education, 21(1), 139-149. https://doi.org/10.14352/jkaie.2017.21.1.139
• Megahed, F. M., Chen, Y. J., Ferris, J. A., Knoth, S., & Jones-Farmer, L. A. (2023). How generative AI models such as ChatGPT can be (mis) used in SPC practice, education, and research? An exploratory study. Quality Engineering, 36(2), 287-315.
• Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning, Available at SSRN: https://ssrn.com/abstract=4354422 or http://dx.doi.org/10.2139/ssrn.4354422
• Millî Eğitim Bakanlığı. (2018). Matematik dersi öğretim programı (İlkokul ve ortaokul 1,2,3,4.5,6,7 ve 8. sınıflar). Ankara: MEB Talim Terbiye Başkanlığı Yayınları.
• Olkun, S., & Toluk-Uçar, Z. (2007). İlköğretimde etkinlik temelli matematik öğretimi. Ankara: Maya Akademi. OpenAI. (2021). What is artificial intelligence (AI)? OpenAI. https://openai.com/learn/what-is-ai
• Ottenbreit-Leftwich, A., Glazewski, K., Jeon, M., Hmelo-Silver, C., Mott, B., Lee, S., & Lester, J. (2021, March). How do elementary students conceptualize artificial intelligence? In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 1261-1261).
• Russell, S., & Norvig, P. (2021). Artificial intelligence: a modern approach (3rd Ed). Prentice Hall.
• Ryu, M., & Han, S. (2017). Image of artificial intelligence of elementary students by using semantic differential scale. Journal of The Korean Association of Information Education, 21(5), 527-535. https://doi.org/10.14352/jkaie.2017.21.5.527
• Shen-Berro, J. (2023). New York City Schools blocked ChatGPT. Here’s what other large districts are doing. Chalkbeat. https://www.chalkbeat.org/2023/1/6/23543039/chatgpt-school- districtsban-block-artiicial-intelligence-open-ai
• Shin, W. S. (2020). A case study on application of artificial intelligence convergence education in elementary biological classification learning. Journal of Korean Elementary Science Education, 39(2), 284-295. http://dx.doi.org/10.15267/keses.2020.39.2.284 Shin, W. S., & Shin, D. H. (2020). A study on the application of artificial intelligence in elementary science education. Journal of Korean elementary science education, 39(1), 117-132.
• Son, W. S. (2020). Development of SW education class plan using artificial intelligence education platform: Focusing on upper grade of elementary school. Journal of The Korean Association of Information Education, 24(5), 453-462. https://doi.org/10.14352/jkaie.2020.24.5.453
• Tanışlı, D., & Özdaş, A. (2009). İlköğretim beşinci sınıf öğrencilerinin örüntüleri genellemede kullandıkları stratejiler. Educational Sciences: Theory & Practice, 9(3), 1453-1497.
• Threlfall, J. (1999). Repeating patterns in the primary years. In A. Orton (Ed.), Pattern in the Teaching and Learning of Mathematics (pp. 18–30). London: Cassell.
• Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
• Waters, J. (2004). Mathematical patterning in early childhood settings. In I. Putt & M. McLean (Eds.), Mathematics education for the third millennium (pp. 565-572). Mathematics Education Research Group of Australia.
• Yin, R. K. (2009). Case study research design and methods (4th ed.). Sage.
• Zhai, X. (2023). Chatgpt for next generation science learning. XRDS: Crossroads, The ACM Magazine for Students, 29(3), 42-46.