An overview of the models respecting the learning analytics process: a conceptual framework proposal

Year 2023, Volume: 9 Issue: 1, 92 - 117, 31.01.2023

Asuman Önder , Gisu Sanem Öztaş , Gökhan Akçapınar

https://doi.org/10.51948/auad.1163540

Abstract

Recent technologies have made it possible to collect miscellaneous and detailed digital data related to learning processes. Although the majority of these data consists of logs obtained from online learning environments, a diverse range of data such as image, sound or data from different sensors is also collected in this context. By virtue of the ease of collecting, storing and analyzing aforementioned data, it is observed that a paradigm shift has been experienced in the research questions, the data sources and the analysis methods of the educational research. As a concomitant of this shift, the studies largely based on self-report data are replaced by the studies depending on digital data collected from different sources. Methods from different disciplines such as data mining, artificial intelligence, natural language processing are pressed into service, when the data analysis is concerned. In the literature, these studies are gathered under the concept of learning analytics. There are several reference and process models in the literature to guide the studies in the field of learning analytics. The aim of this study is to review different well approved models in the literature and in accordance with the components in these models to propose a conceptual framework for the use of researchers.

Keywords

learning analytics, educational data mining, learning analytics process, learning analytics models

Öğrenme analitiği sürecine yönelik modellere genel bir bakış: Kavramsal bir çerçeve önerisi

Abstract

Günümüz teknolojileriyle birlikte öğrencilerin öğrenme süreçlerine ilişkin çok yönlü ve detaylı dijital verilerin toplanması olanaklı hale gelmiştir. Her ne kadar bu verilerin büyük çoğunluğunu çevrimiçi öğrenme ortamlarından elde edilen log veriler oluştursa da görüntü, ses veya sensör verileri gibi çok çeşitli veriler de bu bağlamda toplanmaktadır. Bu tür verilerin toplanmasının, saklanmasının ve analiz edilmesinin kolaylaşması ile birlikte eğitim araştırmalarında cevap aranan araştırma sorularında, kullanılan veri kaynaklarında, analiz yöntemlerinde bir paradigma değişimi yaşandığı gözlenmektedir. Bu değişimle birlikte veri kaynağı olarak büyük oranda öğrencilerin öz bildirimine dayalı olan çalışmalar yerini farklı kaynaklardan toplanan dijital verilerin kullanıldığı çalışmalara bırakmaktadır. Veri analizi noktasında da veri madenciliği, yapay zekâ, doğal dil işleme gibi farklı disiplinlerden yöntemler işe koşulmaktadır. Bu tür çalışmalar alanyazında öğrenme analitiği çatı kavramı altında toplanmaktadır. Öğrenme analitiği alanındaki çalışmalar için yol gösterici olacak birçok referans ve süreç modeli alanyazında yer almaktadır. Bu çalışmanın amacı, alanyazında kabul gören farklı modelleri incelemek ve bu modellerde yer alan bileşenler doğrultusunda araştırmacıların kullanımına yönelik kavramsal bir çerçeve önerisinde bulunmaktır.

Keywords

öğrenme analitiği, eğitsel veri madenciliği, öğrenme analitiği süreci, öğrenme analitiği modelleri

References

• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49.
• Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers & Education, 141, 103612.
• Ang, K. L.-M., Ge, F. L., & Seng, K. P. (2020). Big educational data & analytics: Survey, architecture and challenges. IEEE Access, 8, 116392-116414.
• Arnold, K. E. (2010). Signals: Applying academic analytics. Educause Quarterly, 33(1), n1.
• Baker, R., & Inventado, P. S. (2014). Chapter X: Educational Data Mining and Learning Analytics. Computer Science, 1-16.
• Baker, R., & Siemens, G. (2014). Learning analytics and educational data mining. Cambridge Handbook of the Leaning Sciences, 253-272.
• Bakharia, A., & Dawson, S. (2011). SNAPP: a bird's-eye view of temporal participant interaction. Proceedings of the 1st international conference on learning analytics and knowledge,
• Bodily, R., Ikahihifo, T. K., Mackley, B., & Graham, C. R. (2018). The design, development, and implementation of student-facing learning analytics dashboards. Journal of Computing in Higher Education, 30(3), 572-598.
• Boticki, I., Akçapınar, G., & Ogata, H. (2019). E-book user modelling through learning analytics: the case of learner engagement and reading styles. Interactive Learning Environments, 27(5-6), 754-765. https://doi.org/10.1080/10494820.2019.1610459
• Brooks, C., & Thompson, C. (2017). Predictive modelling in teaching and learning. Handbook of learning analytics, 61-68.
• Broos, T., Pinxten, M., Delporte, M., Verbert, K., & De Laet, T. (2020). Learning dashboards at scale: early warning and overall first year experience. Assessment & Evaluation in Higher Education, 45(6), 855-874. https://doi.org/10.1080/02602938.2019.1689546
• Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40.
• Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331.
• Clow, D. (2012). The learning analytics cycle: closing the loop effectively Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, British Columbia, Canada. https://doi.org/10.1145/2330601.2330636
• Coelho, O. B., & Silveira, I. (2017). Deep learning applied to learning analytics and educational data mining: A systematic literature review. Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE),
• Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338-349.
• dos Santos Garcia, C., Meincheim, A., Junior, E. R. F., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., Santos, E. A. P., & Scalabrin, E. E. (2019). Process mining techniques and applications–A systematic mapping study. Expert Systems with Applications, 133, 260-295.
• Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. Proceedings of the 2nd international conference on learning analytics and knowledge,
• Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. https://doi.org/https://doi.org/10.1016/j.iheduc.2015.10.002
• Gašević, D., Tsai, Y.-S., & Drachsler, H. (2022). Learning analytics in higher education – Stakeholders, strategy and scale. The internet and higher education, 52, 100833. https://doi.org/https://doi.org/10.1016/j.iheduc.2021.100833
• Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42-57.
• Han, J., & Kamber, M. (2006). Classification and prediction. Data mining: Concepts and techniques, 2006, 347-350.
• Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366-371.
• Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1), 221-240.
• Khalil, M., & Ebner, M. (2015). Learning analytics: principles and constraints. EdMedia+ Innovate Learning,
• Kim, J., Jo, I.-H., & Park, Y. (2016). Effects of learning analytics dashboard: analyzing the relations among dashboard utilization, satisfaction, and learning achievement. Asia Pacific Education Review, 17(1), 13-24. https://doi.org/10.1007/s12564-015-9403-8
• Lang, C., Siemens, G., Wise, A., & Gasevic, D. (2017). Handbook of learning analytics. SOLAR, Society for Learning Analytics and Research New York.
• Liu, B. (2011). Web data mining: exploring hyperlinks, contents, and usage data (Vol. 1). Springer.
• Long, P., & Siemens, G. (2011). What is Learning Analytics? ACM,
• Mangaroska, K., & Giannakos, M. (2018). 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.
• Mangaroska, K., Martinez‐Maldonado, R., Vesin, B., & Gašević, D. (2021). Challenges and opportunities of multimodal data in human learning: The computer science students' perspective. Journal of Computer Assisted Learning, 37(4), 1030-1047.
• Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., Ram, I., Woloshen, S., Winne, P. H., & Nesbit, J. C. (2016). What if learning analytics were based on learning science? Australasian Journal of Educational Technology, 32(6).
• Matcha, W., Gašević, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226-245.
• Mazza, R. (2010). Visualization in educational environments. C. Romero, S. Ventura, M. Pechenizkiy, & RSJ d. Baker (Eds.), Handbook of educational data mining, 9-26.
• McNamara, D. S., Allen, L., Crossley, S., Dascalu, M., & Perret, C. A. (2017). Natural language processing and learning analytics. Handbook of learning analytics, 93.
• Na, K. S., & Tasir, Z. (2017). A systematic review of learning analytics intervention contributing to student success in online learning. 2017 International conference on learning and teaching in computing and engineering (LaTICE),
• Ok, G. (2022). Bibliometric evaluation based on Web of Science database: nature and environmental education. Journal for the Education of Gifted Young Scientists, 10(3), 435-451.
• Pardo, A. (2014). Designing learning analytics experiences. In Learning analytics (pp. 15-38). Springer.
• Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. https://doi.org/https://doi.org/10.1016/j.eswa.2006.04.005
• Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
• Saqr, M., & Alamro, A. (2019). The role of social network analysis as a learning analytics tool in online problem based learning. BMC medical education, 19(1), 1-11.
• Saqr, M., Fors, U., & Tedre, M. (2018). How the study of online collaborative learning can guide teachers and predict students’ performance in a medical course. BMC medical education, 18(1), 1-14.
• Scheuer, O., & McLaren, B. M. (2012). Educational data mining. Encyclopedia of the Sciences of Learning, 1075, 1079.
• Selwyn, N., & Gašević, D. (2020). The datafication of higher education: Discussing the promises and problems. Teaching in Higher Education, 25(4), 527-540.
• Serrat, O. (2017). Social network analysis. In Knowledge solutions (pp. 39-43). Springer. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040-53065.
• Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400.
• SOLAR. (2011). What is Learning Analytics? Erişim adresi: https://www.solaresearch.org/about/what-is-learning-analytics/.
• Suthers, D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. Proceedings of the 1st international conference on learning analytics and knowledge,
• Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Kloos, C. D., & Gašević, D. (2020). Learning analytics in European higher education—Trends and barriers. Computers & Education, 155, 103933.
• Veletsianos, G., Collier, A., & Schneider, E. (2015). Digging deeper into learners' experiences in MOOC s: Participation in social networks outside of MOOC s, notetaking and contexts surrounding content consumption. British Journal of Educational Technology, 46(3), 570-587.
• Veluri, R. K., Patra, I., Naved, M., Prasad, V. V., Arcinas, M. M., Beram, S. M., & Raghuvanshi, A. (2021). Learning analytics using deep learning techniques for efficiently managing educational institutes. Materials Today: Proceedings. https://doi.org/https://doi.org/10.1016/j.matpr.2021.11.416
• Ye, D., & Pennisi, S. (2022). Analysing interactions in online discussions through social network analysis. Journal of Computer Assisted Learning, 38(3), 784-796.
• Yoon, M., Lee, J., & Jo, I.-H. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The internet and higher education, 50, 100806. https://doi.org/https://doi.org/10.1016/j.iheduc.2021.100806