Systematic analysis of theses made using learning analytics in Türkiye

Year 2023, , 770 - 782, 07.07.2023

Caner Börekci Tuncay Sarıtaş

https://doi.org/10.25092/baunfbed.1189141

Abstract

This research aims to examine the theses made using learning analytics in Türkiye. For this purpose, the theses published in the Higher Education Council Thesis Center with the keyword "learning analytics" were searched, and 11 doctoral and 10 master's theses published between 2014 and June 2022 were examined. The preferred analysis method was content analysis. The aims, research method, data collection tools, data analysis approaches, participants/sample and keywords of the theses were examined. The study’s findings show that the theses made using learning analytics aim to (1) predict academic progress and determine the factors affecting success, (2) analyze student behaviours and (3) evaluate the usefulness of the developed monitoring systems. Quantitative (n=10) and mixed methods (n=11) were preferred as research methods in theses, but no study was found in which only qualitative methods were preferred. Although learning management systems represent the majority, it has been shown that questionnaires, scales, interviews, observations, and achievement assessments are used as data-gathering tools. It was determined that for the data analysis, hypothesis testing, machine learning techniques, content analyses, and descriptive statistical analyses were used. Higher education students make up the majority of the samples of the theses. A total of 72 different keywords were used 108 times in the theses examined. Apart from "learning analytics" used to identify theses, the most frequently used keywords are "educational data mining", "open and distance learning", "online learning environments", "learning management systems" and "self-regulated learning". It was given information on Turkish postgraduate theses in the field of learning analytics.

Keywords

Learning analytics, document analysis, postgraduate theses.

Türkiye'de öğrenme analitikleri kullanılarak yapılan tezlerin sistematik incelenmesi

Abstract

Bu araştırmanın amacı, Türkiye’de öğrenme analitiği kullanılarak yapılan tezleri incelemektir. Bu amaçla anahtar kelimeleri arasında “Öğrenme analitiği” veya “Öğrenme analitikleri” bulunan ve Yüksek Öğretim Kurulu Tez Merkezinde yayımlanan tezler araştırılmış, 2014 - Haziran 2022 tarihleri arasında yayımlanan 11 doktora ve 10 yüksek lisans tezi incelemeye tabi tutulmuştur. Analiz için içerik analizi yöntemi tercih edilmiştir. Tezlerin amaçları, araştırma yöntemi, veri toplama araçları, veri analiz yaklaşımları, katılımcıları/örneklemi ve anahtar kelimeleri incelenmiştir. Araştırma sonucunda öğrenme analitiği kullanılarak yapılan tezlerin (1) akademik ilerlemeyi tahmin etme ve başarıyı etkileyen unsurları belirleme, (2) öğrenci davranışlarını analiz etme ve (3) geliştirilen izleme sistemlerinin kullanışlılığını tespit etmeyi amaçladığı görülmüştür. Tezlerde araştırma yöntemi olarak nicel (n=10) ve karma yöntemler (n=11) tercih edilmiş, sadece nitel yöntemlerin tercih edildiği çalışmaya rastlanmamıştır. Çoğunlukla öğrenme yönetim sistemleri olmakla beraber anketlerin, ölçeklerin, görüşmelerin, gözlemlerin ve başarı testlerinin veri toplama aracı olarak kullanıldığı tespit edilmiştir. Verilerin analizi için hipotez testlerinin, makine öğrenmesi algoritmalarının, içerik analizlerinin ve betimsel istatistiki analizlerin yapıldığı belirlenmiştir. Tezlerin örneklemleri büyük çoğunlukla yüksek öğrenim öğrencilerinden oluşmaktadır. İncelenen tezlerde toplam 72 farklı anahtar kelime 108 defa kullanılmıştır. Tezlerin belirlenmesi için kullanılan “öğrenme analitiği / öğrenme analitikleri” dışında en sık kullanılan anahtar kelimeler “eğitsel veri madenciliği”, “açık ve uzaktan öğrenme”, “çevrimiçi öğrenme ortamları”, “öğrenme yönetim sistemleri” ve “öz düzenlemeli öğrenme” olarak belirlenmiştir. Türkiye’de öğrenme analitiği alanında yapılan lisansüstü tezlere dair bilgi sahibi olunması sağlanmıştır.

Keywords

Öğrenme analitikleri, doküman incelemesi, lisansüstü tezler.

References

• Avella, J. T., Kebritchi, M., Nunn, S. G., ve Kanai, T., Learning analytics methods, benefits, and challenges in higher education: A systematic literature review, Online Learning, 20(2), 13-29, (2016). https://doi.org/10.24059/olj.v20i2.790
• El Alfy, S., Marx Gómez, J. ve Dani, A., Exploring the benefits and challenges of learning analytics in higher education institutions: a systematic literature review, Information Discovery and Delivery, 47(1), 25-34, (2019). https://doi.org/10.1108/IDD-06-2018-0018
• Kaliisa, R., Rienties, B., Mørch, A. I., ve Kluge, A., Social learning analytics in computer-supported collaborative learning environments: A systematic review of empirical studies, Computers and Education Open, 100073, (2022).
• Joksimović, S., Kovanović, V., ve Dawson, S., The journey of learning analytics. HERDSA Review of Higher Education, 6, 27-63, (2019).
• Ifenthaler, D., Learning analytics. In J. M. Spector (Hrsg.), The SAGE encyclopedia of educational technology, 2, 447–451. Thousand Oaks: Sage, (2015).
• Ji, H., Park, K., Jo, J., ve Lim, H., Mining students’ activities from a computer supported collaborative learning system based on peer to peer network. Peer-to-Peer Networking and Applications, 9(3), 465-476, (2016).
• Cooper, A., A Brief History of Analytics A Briefing Paper. JISC CETIS Analytics Series, 1, 1-21, (2012).
• Kew, S.N., ve Tasir, Z., Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data. Tech Know Learn, 27, 405 – 427, (2022). https://doi.org/10.1007/s10758-021-09541-2
• Park, Y., ve Jo, I. H. Development of the learning analytics dashboard to support students' learning performance. Journal of Universal Computer Science, 21(1), 110-133, (2015).
• Wong, B.Tm., ve Li, K.C. A, Review of learning analytics intervention in higher education (2011–2018). Journal of Computer Education, 7, 7–28. https://doi.org/10.1007/s40692-019-00143-7
• Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education,18(6), 683–695. https://doi.org/10.1080/13562517.2013.827653
• Hendricks, M., Plantz, M.C., ve Pritchard, K.J. (2008). Measuring outcomes of United Wayfunded programs: Expectations and reality. In J.G. Carman ve K.A. Fredricks (Eds.), Nonprofits and evaluation. New Directions for Evaluation, 119, 13-35. https://doi.org/10.1007/s12083-015-0397-0
• Campbell, J. P., ve Oblinger, D. G., Academic analytics. Educause Quarterly, 24. (2007).
• Drachsler, H., ve Greller, W., The pulse of learning analytics understandings and expectations from the stakeholders. Proceedings of the 2nd international conference on learning analytics and knowledge,120-129, (2012).
• Gülcüoğlu, E. , Karaoğlan Yılmaz, F. G. ve Gökkaya, G., Öğrenme Analitikleri Kapsamında 2016-2019 Yıllar Arasında Web of Science Veritabanında Yayınlanan Makalelerin Betimsel Analizi. Bilgi ve İletişim Teknolojileri Dergisi, 3 (1) , 42-76 , (2021).
• Banihashem, S. K., Aliabadi, K., Pourroostaei Ardakani, S., Delaver, A., ve Nili Ahmadabadi, M., Learning analytics: A systematic literature review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 9(2), 1-10, (2018).
• Kıcıman, A. H. , Altun Tot, E. , Eren, E. , Çetintav, G. , Karakaş, G. & Guler, T., 2016-2020 yılları arasında Öğrenme Analitiği ile ilgili Yapılmış SSCI İndeksli Makalelerin Sistematik Olarak İncelenmesi. Öğretim Teknolojisi ve Hayat Boyu Öğrenme Dergisi, 2 (1), 135-152, (2021). https://doi.org/10.52911/itall.875685
• Leitner, P., Khalil, M., ve Ebner, M., Learning Analytics in Higher Education—A Literature Review. In: Peña-Ayala, A. (eds) Learning Analytics: Fundaments, Applications, and Trends. Studies in Systems, Decision and Control, vol 94. Springer, Cham. (2017). https://doi.org/10.1007/978-3-319-52977-6_1
• Mangaroska, K., ve Giannakos, M., Learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning. IEEE Transactions on Learning Technologies, 12(4), 516-534, (2018). https://doi.org/10.1109/TLT.2018.2868673
• Larrabee Sønderlund, A., Hughes, E., & Smith, J., The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594-2618, (2019). https://doi.org/10.1111/bjet.12720
• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M., Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49, (2019). https://doi.org/10.1016/j.tele.2019.01.007
• Li, K.C., Lam, H.K., ve Lam, S.S., A Review of Learning Analytics in Educational Research. Lam, J., Ng, K., Cheung, S., Wong, T., Li, K., Wang, F. (eds) Communications in Computer and Information Science, vol 559. Springer, Berlin, Heidelberg. (2015). https://doi.org/10.1007/978-3-662-48978-9_17
• Adeniji, B. A., A bibliometric study on learning analytics, Doktora Tezi, Long Island University, Long Island, (2019).
• Düzenli Çil, B. , Akgün, E. ve Karaoğlan Yılmaz, G., A Content Analysis Study on Learning Analytics and Motivation. The Journal of Buca Faculty of Education, (53) , 409-426, (2022). https://doi.org/ 10.53444/deubefd.1063342
• Somyürek, S. , Güyer, T. , Atasoy, B. ve Ünal, M., E-öğrenme ortamları ve öğrenme analitikleri. Bilişim Teknolojileri Dergisi, 14 (3), 327-336, (2021). https://doi.org/10.17671/gazibtd.709798
• Viberg, O., Hatakka, M., Bälter, O., ve Mavroudi, A., The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110, (2018). https://doi.org/10.1016/j.chb.2018.07.027
• Peña-Ayala, A., Learning Analytics: fundaments, applications, and trends. A view of the current state of the art to enhance e-learning. NY: Springer. (2017). https://doi.org/10.1007/978-3-319-52977-6
• Koç, M., Research trends in learning analytics from 2010 to 2015. Proceedings of International Conference on Research in Education & Science (ICRES), 203–209, (2016).
• Matcha, W., Gašević, D., ve Pardo, A., A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226-245, (2019). https://doi.org/10.1109/TLT.2019.2916802
• Romero, C., ve Ventura, S., Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355, (2020). https://doi.org/10.1002/widm.1355
• Bowen, G.A., Document Analysis as a Qualitative Research Method. Qualitative Research Journal, (9),2, 27-40, (2009). https://doi.org/10.3316/QRJ0902027
• Snyder, H., Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333-339, (2019). https://doi.org/10.1016/j.jbusres.2019.07.039
• Baek, C., & Doleck, T., Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019, Interactive Learning Environments, 1-23, (2021). https://doi.org/10.1080/10494820.2021.1943689
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46.