THE INVESTIGATION OF TREND IN POSTGRADUATE DISSERTATIONS ON INTELLIGENT TUTORING SYSTEMS: THE CASE OF TURKEY

Year 2021, Volume: 11 Issue: 2, 421 - 444, 25.07.2021

Furkan Aydın , Halil Yurdugül

https://doi.org/10.17943/etku.892680

Abstract

This study aims to examine the postgraduate theses on intelligent tutoring systems in higher education in Turkey and provide a perspective on the subject to researchers. In accordance with the aim of the study, the research topic was determined as “The postgraduate research theses on intelligent tutoring systems in Turkey” and the search was conducted on the website “https://tez.yok.gov.tr/”, which is a database where the master’s theses and doctoral dissertations published by the Council of Higher Education (CoHE) are found. A total of 25 theses published by the year of 2020 (incl.) and matching the selection criteria were examined. The theses were analyzed according to 10 pre-determined criteria: year, education level, key word, content, study groups, intelligent tutoring types, student characteristics, algorithms used in decision making, feedback presented to the learner, and design effectiveness. As a result of the analysis, that 2013 was the year most theses were written, and four theses have been written in the last five years (2016-2020). Of the theses, 9 were doctoral dissertations and 16 were master’s theses. The examination showed that ITS was developed mostly for Mathematics lesson, and undergraduate students were the most preferred study group in the studies. That the intelligent tutoring systems are mostly approached within the concept of e-learning followed by learning domain, student model, artificial intelligence, expert systems and adaptive learning association in the completed theses was identified during the review. Moreover, the overlay model was used the most as the student model, and students’ level of knowledge was the most commonly mentioned student characteristic within the studies. The rule-based approach was the most commonly used method in decision-making, and hints were used the most as a form of feedback. Finally, it was determined that most of the theses were not evaluated or reported on design effectiveness. As a result, the current situation of the theses on intelligent tutoring systems in Turkey was revealed in line with the pre-determined criteria.

Keywords

intelligent tutoring systems, student model, tutoring model, student characteristics

ZEKİ ÖĞRETİM SİSTEMLERİ ÜZERİNE YAPILAN LİSANSÜSTÜ TEZLERDEKİ EĞİLİMİN İNCELENMESİ: TÜRKİYE ÖRNEĞİ

Abstract

Bu çalışmanın amacı Türkiye’deki yükseköğretimde zeki öğretim sistemleri üzerine bitirilen tezlerin incelenmesi ve araştırmacılara konu ile ilgili bir bakış açısı sağlanmaya çalışılmasıdır. Bu amaç doğrultusunda araştırma konusu “Türkiye’deki zeki öğretim sistemleri üzerine yazılan tezler” olarak belirlenmiş ve Yükseköğretim Kurulu (YÖK) tarafından doktora ve yüksek lisans tezlerinin yayınlandığı “https://tez.yok.gov.tr/” adresinden tarama gerçekleştirilmiştir. Çalışmada 2020 (dahil) yılına kadar yayınlanan ve belirlenen ölçütlere uyan 25 tez incelenmiştir. Tezler ise önceden belirlenmiş 10 ölçüte göre analiz edilmiştir. Bu ölçütler ise yıllara, eğitim seviyesi, içerik, çalışma grupları, öğrenci modelleri, öğrenci karakteristikleri, karar vermede kullanılan algoritmalar, öğrenene sunulan geri bildirimler ve tasarım etkililiğidir. Analiz sonucunda bir yılda en çok tez 2013 yılında yazıldığı, son 5 yılda (2016-2020) ise dört tez yazıldığı tespit edilmiştir. Tezlerin 9’u doktora, 16’sı ise yüksek lisans düzeyindedir. En çok Matematik dersine yönelik ZÖS geliştirilmiştir. Çalışma grubu olarak en çok lisans öğrencileri tercih edilmiştir. Bitirilen tezlerde zeki öğretim sistemleri en çok e-öğrenme kavramıyla birlikte ele alındığı; daha sonra sırasıyla öğrenme alanı, öğrenci modeli, yapay zekâ, uzman sistemler ve uyarlanabilir öğrenme kavramlarıyla birlikteliği tespit edilmiştir. Öğrenci modeli olarak en çok katman modeli kullanılmıştır. Öğrenci karakteristiklerinden en çok bilgi düzeyinin kullanıldığı görülmüştür. Karar vermede en çok kural tabanlı yaklaşımın kullandığı belirlenmiştir. Geri bildirim olarak en çok ipucunun kullanıldığı tespit edilmiştir. Son olarak tezlerin büyük bir çoğunluğunun, tasarım etkililiğinin değerlendirilmediği ya da raporlanmadığı belirlenmiştir. Sonuç olarak ise Türkiye’de zeki öğretim sistemleri üzerine tamamlanan tezlerin belirlenen ölçütler doğrultusunda var olan durumu ortaya konulmuştur.

Keywords

zeki öğretim sistemleri, öğrenci modeli, öğretici modeli, öğrenci karakteristikleri

References

• AbuEloun, N. N., & Abu-Naser, S. S. (2017). Mathematics intelligent tutoring system. International Journal of Advanced Scientific Research, 2(1), 11-16.
• Abyaa, A., Idrissi, M. K., & Bennani, S. (2019). Learner modelling: systematic review of the literature from the last 5 years. Educational Technology Research and Development, 67(5), 1105-1143.
• Agrawal, R., & Srikant, R. (1995, March). Mining sequential patterns. In Proceedings of the eleventh international conference on data engineering (pp. 3-14). IEEE.
• Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).
• Akkila, A. N., Almasri, A., Ahmed, A., Al-Masri, N., Abu Sultan, Y. S., Mahmoud, A. Y., ... & Abu-Naser, S. S. (2019). Survey of Intelligent Tutoring Systems up to the end of 2017. IJARW.
• Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., ... & Weber, N. (2019). EDUCAUSE Horizon Report: 2019 Higher Education Edition. EDUCAUSE.
• Aydın, M., Aydın, F. & Yurdugül, H. (2020). Eğitsel veri madenciliği bağlamında ikili benzerlik ve farklılık hesaplamaları. H. Yurdugül, S. Yıldırım, T. Güyer (Ed.), Eğitsel Veri Madenciliği ve Öğrenme Analitikleri (s. 56-73). Anı Yayıncılık, Ankara.
• Bates, A. T. (2005). Technology, e-learning and distance education. Routledge.
• Becker, S. A., Brown, M., Dahlstrom, E., Davis, A., DePaul, K., Diaz, V., & Pomerantz, J. (2018). NMC horizon report: 2018 higher education edition. Louisville, CO: Educause.
• Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition (pp. 1-60). The New Media Consortium.
• Becker, S. A., Freeman, A., Hall, C. G., Cummins, M., & Yuhnke, B. (2016). NMC/CoSN horizon report: 2016 K (pp. 1-52). The New Media Consortium.
• Brill, E. & Resnik, P., (1994). A rule-based approach to prepositional phrase attachment disambiguation. In Proceedings of the 15th International Conference on Computational Linguistics, Volume 2, 1198–1204.
• Brusilovskiy, P. L. (1994). The construction and application of student models in intelligent tutoring systems. Journal of computer and systems sciences international, 32(1), 70-89.
• Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-based expert systems: the MYCIN experiments of the Stanford Heuristic Programming Project.
• Butz, C. J., Hua, S., & Maguire, R. B. (2006). A web-based bayesian intelligent tutoring system for computer programming. Web Intelligence and Agent Systems: An International Journal, 4(1), 77-97.
• Carbonell, J. R. (1970). AI in CAI: An artificial-intelligence approach to computer-assisted instruction. IEEE transactions on man-machine systems, 11(4), 190-202.
• Cheung, B., Hui, L., Zhang, J., & Yiu, S. M. (2003). SmartTutor: An intelligent tutoring system in web-based adult education. Journal of Systems and Software, 68(1), 11-25.
• Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715-4729.
• Conati, C. (2009, June). Intelligent tutoring systems: new challenges and directions. In Twenty-First International Joint Conference on Artificial Intelligence.
• Creswell, J. W. (2012).Educational research: planning, conducting, and evaluating quantitative and qualitativeresearch (4th ed.). Boston, MA: Pearson Publication.
• Fraenkel, J., Wallen, N., & Hyun, H. (2011). How to design and evaluate research in education. 8th Edition. Columbus, OH: McGraw-Hill. Gutierrez, F., & Atkinson, J. (2011). Adaptive feedback selection for intelligent tutoring systems. Expert Systems with Applications, 38(5), 6146-6152.
• Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2015). NMC Horizon Report: 2015 Higher Education Edition. Austin, Texas: The New Media Consortium.
• Käser, T., Klingler, S., Schwing, A. G., & Gross, M. (2017). Dynamic Bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 10(4), 450-462.
• Kay, J. (2000, June). Stereotypes, student models and scrutability. In International Conference on Intelligent Tutoring Systems (pp. 19-30). Springer, Berlin, Heidelberg.
• Kaya, S. (2005). Microsoft Excel öğretimi için zeki öğretim sistemi. Yayınlanmamış yüksek lisans tezi. Gazi Üniversitesi, Ankara. Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of intelligent tutoring systems: a meta-analytic review. Review of educational research, 86(1), 42-78.
• Kumar, A., Singh, N., & Ahuja, N. J. (2017). Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016. International Journal of Cognitive Research in Science, Engineering and Education, 5(2), 83-98.
• Lesgold, A. M., & Mandl, H. (1988). Learning issues for intelligent tutoring systems. New York, NY: Springer-Verlag.
• Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & Education, 49(4), 1066-1080.
• Martin, F., Chen, Y., Moore, R. L., & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Educational Technology Research and Development, 68(4), 1903-1929.
• Melis, E., & Siekmann, J. (2004, June). Activemath: An intelligent tutoring system for mathematics. In International Conference on Artificial Intelligence and Soft Computing (pp. 91-101). Springer, Berlin, Heidelberg.
• Moreno-Marcos, P. M., de la Torre, D. M., Castro, G. G., Muñoz-Merino, P. J., & Kloos, C. D. (2020, June). Should We Consider Efficiency and Constancy for Adaptation in Intelligent Tutoring Systems?. In International Conference on Intelligent Tutoring Systems (pp. 237-247). Springer, Cham.
• Moundridou, M., & Virvou, M. (2000). A web-based authoring tool for algebra-related intelligent tutoring systems. Educational Technology & Sociey, 3(2), 61-70.
• Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2018). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 1-22.
• Naghizadeh, M., & Moradi, H. (2015, May). A model for motivation assessment in intelligent tutoring systems. In 2015 7th Conference on Information and Knowledge Technology (IKT) (pp. 1-6). IEEE.
• Narciss, S. (2013). Designing and evaluating tutoring feedback strategies for digital learning. Digital Education Review, (23), 7-26.
• Özek, M. B., Akpolat, Z. H., & Orhan, A. (2010). Web tabanlıakıllıöğretim sistemlerindetip-2 bulanık mantık kullanarak öğrenci öğrenme stili modelleme. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 22(1), 37–44.
• Paviotti, G., Rossi, P. G., & Zarka, D. (2012). Intelligent tutoring systems: an overview. Pensa Multimedia.
• Prentzas, J., & Hatzilygeroudis, I. (2007). Categorizing approaches combining rule‐based and case‐based reasoning. Expert Systems, 24(2), 97-122.
• Ramesh, V. M., & Rao, N. J. (2012, July). Tutoring and expert modules of intelligent tutoring systems. In 2012 IEEE Fourth International Conference on Technology for Education (pp. 251-252). IEEE.
• Rich, E. (1979). User modeling via stereotypes. Cognitive science, 3(4), 329-354.
• Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295).
• Sayad, S. (2020). An Introduction to Data Science. https://www.saedsayad.com/data_mining_map.htm adresinden 23.02.2020 tarihinde alınmıştır.
• Sleeman, D., & Brown, J. S. (1982). Intelligent tutoring systems. London, UK: Academic Press.
• Şahin, M. (2018). E-öğrenme ortamlarına yönelik öğrenme analitiklerine dayalı müdahale motoru tasarımı ve geliştirilmesi. Yayınlanmamış doktora tezi. Hacettepe Üniversitesi, Ankara.
• Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27(1), 118-130.
• Vanlehn, K. (2006). The behavior of tutoring systems. International journal of artificial intelligence in education, 16(3), 227-265.
• Yang, G., & Graf, S. (2010, July). A practical student model for a location-aware and context-sensitive personalized adaptive learning system. In 2010 International Conference on Technology for Education (pp. 130-133). IEEE.
• Yıldırım, A., & Şimşek, H. (2008). Sosyal bilimlerde nitel araştırma yöntemleri. Ankara: Seçkin Yayıncılık.