Deeva, Galina

Islind, Anna Sigridur

Willermark, Sara

Óskarsdóttir, Maria

English

ScholarSpace at University of Hawai'i at Manoa

Introduction to the Minitrack on Learning Analytics 2023    Sara Willermark University West,  School of Business, Economics and IT,  Sweden  sara.willermark@hv.se Anna Sigridur Islind Reykjavik University, Department of Computer Science, Iceland islind@ru.se Galina Deeva KU Leuven,  Research Centre for Information Systems Engineering, Belgium galina.deeva@kuleuven.be María Óskarsdóttir Reykjavik University, Department of Computer Science, Iceland mariaoskars@ru.is   The digitalization of society cuts through the entire education system (e.g. Deeva, Bogdanova, et al., 2021; Flores et al., 2022; Josefsson & Willermark, 2022; Olofsson et al., 2021; Pareto & Willermark, 2014, 2022; Willermark & Islind, 2022a, 2022b; Winman et al., 2018). It is therefore not surprising that learning analytics and educational data mining have arisen as important research fields in the last decades to enhance education at all levels (Deeva, Willermark, et al., 2021; Willermark et al., 2022).  Learning analytics (LA) centers around aspects of personalization, adaptive learning, predictive analysis, and user behavior profiling. Often, learning analytics is presented as offering limitless possibilities. Because of the vast amounts of data that are generated and gathered within educational systems, there is an opportunity to develop a data-driven understanding of learning and teaching processes (Deeva, Willermark, et al., 2021; Nguyen et al., 2020; Siemens & Baker, 2012; Willermark et al., 2022). Lately, the need for direct involvement of educational research in learning analytics has been acknowledged as the first fundamental pillar of learning analytics. This is to frame the research, i.e., to determine which data to collect and analyze. The second pillar, “capturing”, refers to finding evidence of learning, by identifying and explaining useful data for analyzing and understanding teaching, learning, and developing methods that capture and model learning. Third, “understanding” is associated with how learning theory is informed by large-scale data analysis, as well as the use of data science techniques to understand specific aspects of teaching and learning. The fourth pillar is the “impact” on learning and teaching by providing decision support and feedback based on LA. For example, through dashboards and early alert systems, and personalized and adaptive learning.  However, for LA to reach an impact in practice there is a need to enhance the understanding of educators' overall practice, their needs, and how to support them in making use of a data-informed process (Viberg & Gronlund, 2021). Furthermore, LA raises important questions about students' autonomy and integrity (Pargman & McGrath, 2019) which has often been overlooked as a result of wanting to explore possibilities with the approach.  For this minitrack, we welcomed papers that address, reflect on, and relate to, the four pillars of learning analytics and datafication in educational settings alongside papers that reflect on changes in learning platforms, teaching and learning practices, and student profiling. We especially welcomed papers that take a critical and ethical perspective on learning analytics.   In total, three papers were accepted for the minitrack this year. In the paper written by Edmilson Cosme et al. (2023) “A systematic review of the factors that impact the prediction of retention and dropout in higher education,” the authors present state-of-the-art knowledge about factors related to student dropout and retention. Such knowledge can, for example, be used as a basis to construct models using data mining techniques such as regression, decision trees, and neural networks. The paper written by Kenzo Muramatsu et al. (2023), ”Does Mouse Click Frequency Predict Students' Flow Experience?” explores students' flow experience during the use of educational systems via mouse click frequency. The study contributes to the field of learning analytics by confirming that it is not possible to predict students' flow experience only with mouse click frequency and paving the way for new studies that use different behavior data to predict students' flow experience. Lastly, the paper by López Flores et al. (2023) “Making the Most of Slides and Lecture Captures for Better Performance: A Learning Analytics Case Study in Higher Education” explores how high achiever students make use of the educational material’. The Proceedings of the 56th Hawaii International Conference on System Sciences | 2023Page 1269URI: https://hdl.handle.net/10125/102786978-0-9981331-6-4(CC BY-NC-ND 4.0)study includes data from two educational platforms and highlights the value of multiple data sources.  Overall, the contributions demonstrate a breadth of perspectives and approaches to learning analytics.  Acknowledgments. The minitrack chairs would like to thank the authors of the paper for contributing to the body of knowledge on learning analytics. We would also like to thank the reviewers of this minitrack for their time and engagement to provide insightful comments to further enhance the authors' works.  References   Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & De Weerdt, J. (2021). A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education, 162, 104094.  Deeva, G., Willermark, S., Islind, A. S., & Oskarsdottir, M. (2021). Introduction to the Minitrack on Learning Analytics. Proceedings of the 54th Hawaii International Conference on System Sciences. Edmilson Cosme, S., Freitas, S., Soares Ramos, C., Muniz de Menezes, A. E., & Rodrigues de Araujo, L. K. S. (2023). A systematic review of the factors that impact the prediction of retention and dropout in higher education. The Hawaii International Conference on System Sciences (HICSS).   Flores, N. L., Óskarsdóttir, M., & Islind, A. (2022). Asynchronous and synchronous teaching and learning: Designing university-wide instructional material to support teachers in the post-pandemic normality of higher education. INTED2022 Proceedings. Josefsson, P., & Willermark, S. (2022). What does teachers' digital vulnerability look like? INTED2022 Proceedings.  Kenzo Muramatsu, P., Oliverira, W., Hamari, J., & Oyibo, K. (2023). Does Mouse Click Frequency Predict Students' Flow Experience? The Hawaii International Conference on System Sciences (HICSS).  López Flores, N., Islind, A. S. & Óskarsdóttir, M. (2023). Making the Most of Slides and Lecture Captures for Better Performance: A Learning Analytics Case Study in Higher Education. The Hawaii International Conference on System Sciences (HICSS).  Nguyen, A., Gardner, L., & Sheridan, D. (2020). A design methodology for learning analytics information systems: Informing learning analytics development with learning design.  Olofsson, A. D., Lindberg, J. O., Young Pedersen, A., Arstorp, A.-T., Dalsgaard, C., Einum, E., Caviglia, F., Ilomäki, L., Veermans, M., Häkkinen, P., & Willermark, S. (2021). Digital competence across boundaries-beyond a common Nordic model of the digitalisation of K-12 schools? Education Inquiry, 1-12.  Pareto, L., & Willermark, S. (2014). Can teachers’ fragmented work situation jeopardize professional development of future teaching practices? European Conference on Technology Enhanced Learning. Pareto, L., & Willermark, S. (2022). Tracing expansive learning in computer-supported collaborative teaching. Learning, Culture and Social Interaction, 33, 100617. https://doi.org/https://doi.org/10.1016/j.lcsi.2022.100617  Pargman, T. C., & McGrath, C. (2019). Be Careful What You Wish For! Learning Analytics and the Emergence of Data-Driven Practices in Higher Education. Technology, 50(6), 2839-2854.  Siemens, G., & Baker, R. S. d. (2012). Learning analytics and educational data mining: towards communication and collaboration. Proceedings of the 2nd international conference on learning analytics and knowledge. Viberg, O., & Gronlund, A. (2021). Desperately seeking the impact of learning analytics in education at scale: Marrying data analysis with teaching and learning. arXiv preprint arXiv:2105.06680.  Willermark, S., & Islind, A. S. (2022a). Seven educational affordances of virtual classrooms. Computers and Education Open, 3, 100078.  Willermark, S., & Islind, A. S. (2022b). Adopting to the virtual workplace: identifying leadership affordances in virtual schools. Journal of Workplace Learning. Willermark, S., Óskarsdottir, M., Islind, A. S., & Deeva, G. (2022). Introduction to the Minitrack on Learning Analytics. The Hawaii International Conference on System Sciences (HICSS). Winman, T., Spante, M., Willermark, S., Svensson, L., & Lundh Snis, U. (2018). Digitaliseringen i skolan: möjligheter och utmaningar.   Page 1270

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