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Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı

Year 2015, Volume: 5 Issue: 5 - Volume: 5 Issue: 5, 569 - 586, 14.07.2016

Abstract

Bu çalışmanın amacı, bilgisayar programlamanın önemli konuları: algoritma ve akış şeması kavramlarının öğretilmesi için geliştirilmiş olan, Yapay Zekâ destekli bir eğitsel yazılım sistemini tanıtmak ve sistemin başarımını değerlendirmek için elde edilen bulgulara değinmektir. Çalışma kapsamında tanıtılan yazılım sistemi, bilgisayar programlama temel kavramlarının öğretimi düsturuna sıkı bir şekilde bağlı kalmakta, ancak bunu Yapay Zekâ destekli, akıllı bir mekanizma çerçevesinde gerçekleştirmektedir. Yazılımın etkili bir öğretim aracı olup olmadığı konusunda fikir edinmek için genel bir değerlendirme süreci planlanmış; çalışmaya konu olan öğrenciler, bu süreçten geçirilmiştir. Değerlendirme süreciyle elde edilen bulgular, geliştirilen yazılım sisteminin, algoritma - akış şeması kavramlarını ve bilgisayar programlamanın temel odak noktası olan algoritmik düşünce mantığını etkili bir şekilde öğretilmesi noktasında başarılı olduğunu göstermiştir. Ek olarak; yazılım sisteminin, öğrencilerin bilgisayar programlama temellerine yönelik derslerdeki başarı oranlarını artırdığını ve gerek yazılımın, gerekse gerçekleşen eğitimsel süreçlerin, öğrenciler tarafından oldukça etkili bulunduğunu da ifade etmek mümkündür.

References

  • Anderson, D., & McNeill, G. (1992). Artificial neural networks technology. A DACS state-of-the-art report. Kaman Sciences Corporation, 258, 13502-462.
  • Armoni, M., Meerbaum-Salant, O., & Ben-Ari, M. (2015). From scratch to “real” programming. ACM Transactions on Computing Education (TOCE), 14(4), 25.
  • Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
  • Bau, D., Dawson, M., & Bau, A. (2015). Using Pencil Code to Bridge the Gap between Visual and Text- Based Coding. 46th ACM Technical Symposium on Computer Science Education (706-706). ACM.
  • Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. ACM SIGCSE Bulletin, 39(2), 32-36.
  • Cañas, J. J., Bajo, M. T., & Gonzalvo, P. (1994). Mental models and computer programming. International Journal of Human-Computer Studies, 40(5), 795-811.
  • Carlisle, M. C., Wilson, T. A., Humphries, J. W., & Hadfield, S. M. (2005). RAPTOR: a visual programming environment for teaching algorithmic problem solving. ACM SIGCSE Bulletin, 37(1), 176-180, ACM.
  • Chen, S., & Morris, S. (2005). Iconic programming for flowcharts, java, turing, etc. ACM SIGCSE Bulletin, 37(3), 104-107.
  • Cutts, Q., Connor, R., Michaelson, G., & Donaldson, P. (2014). Code or (not code): separating formal and natural language in CS education. 9th Workshop in Primary and Secondary Computing Education (20- 28). ACM.
  • Dorça, F. (2015). Implementation and use of Simulated Students for Test and Validation of new Adaptive Educational Systems: a Practical Insight. International Journal of Artificial Intelligence in Education, 1- 27.
  • Esteves, M., Fonseca, B., Morgado, L., & Martins, P. (2011). Improving teaching and learning of computer programming through the use of the Second Life virtual world. British Journal of Educational Technology, 42(4), 624-637.
  • Hassoun, M. H. (1995). Fundamentals of artificial neural networks. MIT Press.
  • Hooshyar, D., Ahmad, R. B., Yousefi, M., Yusop, F. D., & Horng, S. J. (2015). A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers. Journal of Computer Assisted Learning.
  • Jain, G. P., Gurupur, V. P., & Faulkenberry, E. D. (2013). Artificial intelligence based student learning evaluation tool. Global Engineering Education Conference (EDUCON), 2013 IEEE (751-756), IEEE.
  • Kafai, Y. B., & Burke, Q. (2015). Computer programming goes back to school. Education Week, 61-65.
  • Krpan, D., Mladenović, S., & Rosić, M. (2015). Undergraduate Programming Courses, Students’ Perception and Success. Procedia-Social and Behavioral Sciences, 174, 3868-3872.
  • Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 16.
  • Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2015). Teaching the teacher: tutoring SimStudent leads to more effective cognitive tutor authoring. International Journal of Artificial Intelligence in Education, 25(1), 1-34.
  • Michaelson, G. (2015). Teaching Programming with Computational and Informational Thinking. Journal of Pedagogic Development, 5(1).
  • Moser, R. (1997). A fantasy adventure game as a learning environment: why learning to program is so difficult and what can be done about it. ACM SIGCSE Bulletin, 29(3), 114-116, ACM.
  • Pillay, N. (2003). Developing intelligent programming tutors for novice programmers. ACM SIGCSE Bulletin, 35(2), 78-82.
  • Pillay, N. & Jugoo, V. (2005). An investigation into student characteristics affecting novice programming performance. ACM SIGCSE Bulletin 37: 107-110.
  • Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.
  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172.
  • Sterritt, R., Hanna, P., & Campbell, J. (2015). Reintroducing programming to the school environment. 9th International Technology, Education and Development Conference, (2), IATED.
  • Wenger, E. (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
  • Wiedenbeck, S., Labelle, D., & Kain, V. N. (2004). Factors affecting course outcomes in introductory programming. 16th Annual Workshop of the Psychology of Programming Interest Group (97-109).
  • Wiedenbeck, S., Ramalingam, V., Sarasamma, S., & Corritore, C. (1999). A comparison of the comprehension of object-oriented and procedural programs by novice programmers. Interacting with Computers, 11(3), 255-282.
  • Yadin, A. (2011). Reducing the dropout rate in an introductory programming course. ACM Inroads, 2(4), 71-76.
  • Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.

Usage of an Intelligent Software System in Teaching Algorithm and Flowchart Concepts

Year 2015, Volume: 5 Issue: 5 - Volume: 5 Issue: 5, 569 - 586, 14.07.2016

Abstract

Objective of this work is to introduce an Artificial Intelligence supported educational software system, which has been developed for teaching important subjects of computer programming: algorithm and flowchart concepts, and touch upon the findings, which were obtained for evaluating success of the system. The software system introduced in the work is tightly connected to the rule of teaching essential computer programming concepts, but ensures this task in the context of an Artificial Intelligence supported, intelligent mechanism. In order to have idea about whether the software is an effective teaching tool or not, a general evaluation process has been planned; students subjected to the work have been taken into this process. Findings obtained via evaluation process have shown that the developed software system is successful at effectively teaching the algorithmic thinking logic, which is the essential focus, and algorithm - flowchart concepts. Additionally, it is also possible to express that the software system has improved students’ success rates in the courses related to essentials of computer programming and students have found both software and the performed educational processes pretty effective.

References

  • Anderson, D., & McNeill, G. (1992). Artificial neural networks technology. A DACS state-of-the-art report. Kaman Sciences Corporation, 258, 13502-462.
  • Armoni, M., Meerbaum-Salant, O., & Ben-Ari, M. (2015). From scratch to “real” programming. ACM Transactions on Computing Education (TOCE), 14(4), 25.
  • Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3-31.
  • Bau, D., Dawson, M., & Bau, A. (2015). Using Pencil Code to Bridge the Gap between Visual and Text- Based Coding. 46th ACM Technical Symposium on Computer Science Education (706-706). ACM.
  • Bennedsen, J., & Caspersen, M. E. (2007). Failure rates in introductory programming. ACM SIGCSE Bulletin, 39(2), 32-36.
  • Cañas, J. J., Bajo, M. T., & Gonzalvo, P. (1994). Mental models and computer programming. International Journal of Human-Computer Studies, 40(5), 795-811.
  • Carlisle, M. C., Wilson, T. A., Humphries, J. W., & Hadfield, S. M. (2005). RAPTOR: a visual programming environment for teaching algorithmic problem solving. ACM SIGCSE Bulletin, 37(1), 176-180, ACM.
  • Chen, S., & Morris, S. (2005). Iconic programming for flowcharts, java, turing, etc. ACM SIGCSE Bulletin, 37(3), 104-107.
  • Cutts, Q., Connor, R., Michaelson, G., & Donaldson, P. (2014). Code or (not code): separating formal and natural language in CS education. 9th Workshop in Primary and Secondary Computing Education (20- 28). ACM.
  • Dorça, F. (2015). Implementation and use of Simulated Students for Test and Validation of new Adaptive Educational Systems: a Practical Insight. International Journal of Artificial Intelligence in Education, 1- 27.
  • Esteves, M., Fonseca, B., Morgado, L., & Martins, P. (2011). Improving teaching and learning of computer programming through the use of the Second Life virtual world. British Journal of Educational Technology, 42(4), 624-637.
  • Hassoun, M. H. (1995). Fundamentals of artificial neural networks. MIT Press.
  • Hooshyar, D., Ahmad, R. B., Yousefi, M., Yusop, F. D., & Horng, S. J. (2015). A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers. Journal of Computer Assisted Learning.
  • Jain, G. P., Gurupur, V. P., & Faulkenberry, E. D. (2013). Artificial intelligence based student learning evaluation tool. Global Engineering Education Conference (EDUCON), 2013 IEEE (751-756), IEEE.
  • Kafai, Y. B., & Burke, Q. (2015). Computer programming goes back to school. Education Week, 61-65.
  • Krpan, D., Mladenović, S., & Rosić, M. (2015). Undergraduate Programming Courses, Students’ Perception and Success. Procedia-Social and Behavioral Sciences, 174, 3868-3872.
  • Maloney, J., Resnick, M., Rusk, N., Silverman, B., & Eastmond, E. (2010). The scratch programming language and environment. ACM Transactions on Computing Education (TOCE), 10(4), 16.
  • Matsuda, N., Cohen, W. W., & Koedinger, K. R. (2015). Teaching the teacher: tutoring SimStudent leads to more effective cognitive tutor authoring. International Journal of Artificial Intelligence in Education, 25(1), 1-34.
  • Michaelson, G. (2015). Teaching Programming with Computational and Informational Thinking. Journal of Pedagogic Development, 5(1).
  • Moser, R. (1997). A fantasy adventure game as a learning environment: why learning to program is so difficult and what can be done about it. ACM SIGCSE Bulletin, 29(3), 114-116, ACM.
  • Pillay, N. (2003). Developing intelligent programming tutors for novice programmers. ACM SIGCSE Bulletin, 35(2), 78-82.
  • Pillay, N. & Jugoo, V. (2005). An investigation into student characteristics affecting novice programming performance. ACM SIGCSE Bulletin 37: 107-110.
  • Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.
  • Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172.
  • Sterritt, R., Hanna, P., & Campbell, J. (2015). Reintroducing programming to the school environment. 9th International Technology, Education and Development Conference, (2), IATED.
  • Wenger, E. (2014). Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge. Morgan Kaufmann.
  • Wiedenbeck, S., Labelle, D., & Kain, V. N. (2004). Factors affecting course outcomes in introductory programming. 16th Annual Workshop of the Psychology of Programming Interest Group (97-109).
  • Wiedenbeck, S., Ramalingam, V., Sarasamma, S., & Corritore, C. (1999). A comparison of the comprehension of object-oriented and procedural programs by novice programmers. Interacting with Computers, 11(3), 255-282.
  • Yadin, A. (2011). Reducing the dropout rate in an introductory programming course. ACM Inroads, 2(4), 71-76.
  • Yegnanarayana, B. (2009). Artificial neural networks. PHI Learning Pvt. Ltd.
There are 30 citations in total.

Details

Other ID JA36JZ46MV
Journal Section Articles
Authors

Utku Köse This is me

Aslıhan Tüfekçi This is me

Publication Date July 14, 2016
Published in Issue Year 2015 Volume: 5 Issue: 5 - Volume: 5 Issue: 5

Cite

APA Köse, U., & Tüfekçi, A. (2016). Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı. Pegem Eğitim Ve Öğretim Dergisi, 5(5), 569-586.
AMA Köse U, Tüfekçi A. Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı. Pegem Eğitim ve Öğretim Dergisi. July 2016;5(5):569-586.
Chicago Köse, Utku, and Aslıhan Tüfekçi. “Algoritma Ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı”. Pegem Eğitim Ve Öğretim Dergisi 5, no. 5 (July 2016): 569-86.
EndNote Köse U, Tüfekçi A (July 1, 2016) Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı. Pegem Eğitim ve Öğretim Dergisi 5 5 569–586.
IEEE U. Köse and A. Tüfekçi, “Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı”, Pegem Eğitim ve Öğretim Dergisi, vol. 5, no. 5, pp. 569–586, 2016.
ISNAD Köse, Utku - Tüfekçi, Aslıhan. “Algoritma Ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı”. Pegem Eğitim ve Öğretim Dergisi 5/5 (July 2016), 569-586.
JAMA Köse U, Tüfekçi A. Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı. Pegem Eğitim ve Öğretim Dergisi. 2016;5:569–586.
MLA Köse, Utku and Aslıhan Tüfekçi. “Algoritma Ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı”. Pegem Eğitim Ve Öğretim Dergisi, vol. 5, no. 5, 2016, pp. 569-86.
Vancouver Köse U, Tüfekçi A. Algoritma ve Akış Şeması Kavramlarının Öğretiminde Akıllı Bir Yazılım Sistemi Kullanımı. Pegem Eğitim ve Öğretim Dergisi. 2016;5(5):569-86.