Exploratory versus Explanatory Visual Learning Analytics: Driving Teachers’ Attention through Educational Data Storytelling

Abstract

From a human-centred computing perspective, supporting the interpretation of educational dashboards and visualizations by the people intended to use them exposes critical design challenges that may often be trivialized. Empirical evidence already shows that "usable" visualizations are not necessarily effective from an educational perspective. Since an educator's interpretation of visualized data is essentially the construction of a narrative about student progress, in this paper, we propose the concept of "Educational Data Storytelling" as an approach for explaining student data by aligning educational visualizations with the intended learning design. We present a pilot study that explores the effectiveness of these data storytelling elements based on educator responses to prototypes by analyzing the kinds of stories they articulate, their eye-tracking behaviour, and their preferences after inspecting a series of student data visualizations. The dual purpose is to understand the contribution of each visual element for data storytelling, as well as the effectiveness of the enhancements when combined. 1 Notes for Practice  The learning analytics field, to effectively support teaching and learning visually, needs to move on from visual analytics that invite users to explore the data, to visualizations that explain insights.  In this paper, we propose the concept of "Educational Data Storytelling" as an approach to designing visual learning analytics interfaces that explain student data by aligning educational visualizations with the learning design intended by the teacher.  We see the potential of learning-design-driven data storytelling elements to support sensemaking by guiding students and teachers to "one learning story per visualization," given that learning is a complex task.

Deconstructing orchestration load: comparing teacher support through mirroring and guiding

“…ENA results and subjective responses of the teachers both confirmed that in this condition, they were out of control, and they could not make announcements to the class regarding time, phase transitions, and student participation during the activity. Given the small number of teachers participating in our study, we take these findings as early indications that having access to orchestration tools that visualize information about collaboration and provide a means for guiding collaboration become beneficial for orchestration (Wise & June 2019;Echeverria et al, 2018;Schwarz et al, 2021). The post-activity questionnaire responses from the teachers also confirmed that having access to the dashboard provided awareness and control regarding collaboration.…”

Designing Analytics for Collaboration Literacy and Student Empowerment

“…When students and practitioners engage with a dashboard, they typically do so with specific tasks in mind, and may only need certain information in specific contexts. These ideas generally align with a best practice around minimalism and supporting the ability for users to tell a story with the data (Williams, 2015;Alhadad, 2018;Echeverria et al, 2018). This idea of storytelling also speaks to the importance of supporting multiple interpretations of the data as well as avoiding generalizations and equating causation with correlation (Alhadad, 2018).…”

“…More recent work has explicitly broader consideration of the different stakeholders within learning analytics (Alhadad, 2018;Teasley, 2015;Echeverria et al, 2018). Researchers have drawn attention to bridging between the theoretical and the practical.…”

“…These are all important considerations when designing and implementing educational dashboards that inform this work. Echeverria et al (2018) also pushes into the domain of teacher analytics, a growing sub-area of learning analytics (LA) that aims to enable teachers to better understand and support their learners across a variety of learning configurations (Teasley, 2015). This work is dually motivated by the challenges posed by teaching large classes, and the availability of increasing amounts of data within learning management systems (Carbone, 1999;Dillenbourg et al, 2013;Gibbs, Jenkins, & Alan, 1992).…”

Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

“…Upcoming studies include investigations of off-task behavior (e.g., [ 67 ]) and self quantification of students in distance education at home. Finally, a data storytelling dashboard on the multimodal data should benefit students and instructors in the superficial analysis of behavior and context across learning (e.g., [ 68 ]).…”