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DS-LAK24 - Data Storytelling Narratives and Learning Analytics Dashboards
Where: LAK24 Conference, Kyoto, Japan
When: Tuesday, March 19, 2024, from 1:30 to 5:00 pm
Data Storytelling (DS) in Learning Analytics (LA) has proven as an effective approach to communicating insights to non-data experts (e.g., students and teachers). DS brings the promise to incorporate narratives into LA interfaces (e.g., dashboards) to facilitate the provision of direct feedback and pedagogical explanations. The LA community has researched Data Storytelling principles and techniques to support educational stakeholders in interpreting their teaching and learning progress. However, given the relevance of the story narrative, challenges arise to provide unbiased, fair, and meaningful stories without misleading the communication of insights. This workshop aims to explore the formal and practical challenges and opportunities of DS by engaging in discussions with the LA community. In this workshop, we expect to spark discussion on these main topics: What methods and methodologies of DS from other domains are suitable for LA? How to evaluate the impact of DS in LA? How can we automate the process of generating fair and unbiased data stories to facilitate sense-making and effectively communicate insights? This workshop will bring together storytelling researchers and practitioners, whose data storytelling in LA is a special case, to clarify and converge on the future of DS in LA related to their challenges and opportunities.
There is a growing interest in creating Learning Analytics (LA) interfaces (e.g., dashboards, visualizations, or reports) to support educational stakeholders in monitoring learning tasks (Salas-Pilco et al., 2022). However, recent literature reviews and empirical studies report that most of these LA interfaces have serious limitations, such as showing visualizations that are difficult to understand by non data experts (Corrin & de Barba, 2015; Herodotou et al., 2019), lack of effectiveness in communicating insights (Bodily et al., 2017), and failing to align with educators’ pedagogical needs (Kaliisa et al., 2022; Sergis et al., 2017).
Commonly current research and design approaches adopted to create LA interfaces are generating interfaces that are often hard to interpret in a timely manner (Duval, 2011). With the increasing amount of complex data traces captured (in online and physical spaces), there is a need for compelling ways to distill information into meaningful, memorable, and engaging insights (Dominyk, 2022). One of the strategies to address these challenges is the improvement of the explanatory design features of current LA interfaces. Data storytelling (DS) techniques and principles provide a way to include narrative and elements to explain and connect the learning design goals with visual elements aiming at guiding the user’s attention to relevant insights. For instance, Echeverria et al. (2018) demonstrated the potential of enhancing visualisations with DS visual elements (e.g., title, highlights, shaded areas) in helping teachers explore visualisations with less effort. Similarly, Martinez-Maldonado et al. (2020) demonstrated the promise of using a layered storytelling approach to communicate insights on team performance. Following a similar layered approach, Fernandez-Nieto et al. (2021) crafted data stories to promote students’ reflections. Their work demonstrated that learner data stories were useful for students to identify potential improvements and errors they performed while enacting clinical simulations. One of the most recent works on DS is presented by Pozdniakov et al. (2023). The authors evaluated the impact of teachers’ visualisation literacy on their interactions with LA dashboards, and found that teachers with low visualisation literacy especially benefited from DS-based visual guidance. While these prior works have demonstrated how DS can benefit teachers and students when interpreting data from LA interfaces, there are still challenges and opportunities to explore in terms of DS automation, ethics, fairness, scalability, and impact (Fernandez-Nieto et al., 2021; Martinez-Maldonado et al., 2020; Zdanovic et al., 2022).
Salas-Pilco, S. Z., Xiao, K., & Hu, X. (2022). Artificial intelligence and learning analytics in teacher education: A systematic review. Education Sciences, 12(8), 569.
Corrin, L. & De Barba, P. Exploring students’ interpretation of feed- back delivered through learning analytics dashboards, in Proc. 31st Annu. Conf. Australas. Soc. Comput. Learn. Tertiary Educ., 2014, pp. 629–633. [Online]. Available: http://www.ascilite.org/conferences/ dunedin2014/proceedings/
Kaliisa, R., Mørch, A.I. & Kluge, A. My Point of Departure for Analytics is Extreme Skepticism’: Implications Derived from An Investigation of University Teachers. Learning Analytics Perspectives and Design Practices. Tech Know Learn 27, 505–527 (2022). https://doi.org/10.1007/s10758-020-09488-w
Sergis, S., Sampson, D.G. (2017). Teaching and Learning Analytics to Support Teacher Inquiry: A Systematic Literature Review. In: Peña-Ayala, A. (eds) Learning Analytics: Fundaments, Applications, and Trends. Studies in Systems, Decision and Control, vol 94. Springer, Cham. https://doi.org/10.1007/978-3-319-52977-6_2
Herodotou, Christothea & Rienties, Bart & Boroowa, Avinash & Zdráhal, Zdenek & Hlosta, Martin. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational Technology Research and Development. 67. 10.1007/s11423-019-09685-0.
Bodily, R., & Verbert, K. (2017, March). Trends and issues in student-facing learning analytics reporting systems research. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 309-318).
Dominyk Zdanovic, Tanja Julie Lembcke, and Toine Bogers. 2022. The Influence of Data Storytelling on the Ability to Recall Information. In Proceedings of the 2022 Conference on Human Information Interaction and Retrieval (CHIIR ‘22). Association for Computing Machinery, New York, NY, USA, 67–77. https://doi.org/10.1145/3498366.3505755
Echeverria, V., Martinez-Maldonado, R., Buckingham Shum, S., Chiluiza, K., Granda, R., & Conati, C. (2018). Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling. Journal of Learning Analytics, 5(3), 73—97. https://doi.org/10.18608/jla.2018.53.6
Martinez-Maldonado, R., Echeverria V., Fernandez Nieto G., & Buckingham Shum S. 2020. From Data to Insights: A Layered Storytelling Approach for Multimodal Learning Analytics. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ‘20). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3313831.3376148
Pozdniakov S., Martinez-Maldonado R., Tsai Yi-Shan, Echeverria Vanessa, Namrata Srivastava, & Gasevic Dragan. 2023. How Do Teachers Use Dashboards Enhanced with Data Storytelling Elements According to their Data Visualisation Literacy Skills? In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK2023). Association for Computing Machinery, New York, NY, USA, 89–99. https://doi.org/10.1145/3576050.3576063
Fernandez-Nieto, G. M., Echeverria, V., Shum, S. B., Mangaroska, K., Kitto, K., Palominos, E., … & Martinez-Maldonado, R. (2021). Storytelling with learner data: Guiding student reflection on multimodal team data. IEEE Transactions on Learning Technologies, 14(5), 695-708. 10.1109/TLT.2021.3131842.