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The pedagogical modelling of everyday classroom practice is an interesting kind of evidence, both for educational research and teachers' own professional development. This paper explores the usage of wearable sensors and machine learning techniques to automatically extract orchestration graphs (teaching activities and their social plane over time), on a dataset of 12 classroom sessions enacted by two different teachers in different classroom settings. The dataset included mobile eye-tracking as well as audiovisual and accelerometry data from sensors worn by the teacher. We evaluated both time-independent and time-aware models, achieving median F1 scores of about 0.7-0.8 on leave-one-session-out k-fold cross-validation. Although these results show the feasibility of this approach, they also highlight the need for larger datasets, recorded in a wider variety of classroom settings, to provide automated tagging of classroom practice that can be used in everyday practice across multiple teachers.

Abstract:From the conceptualization to the evaluation of Computer-Supported Collaborative Learning (CSCL) scenarios, teachers address multiple tasks, sometimes being overwhelmed on account of the required time and associated burden. To support teachers in this endeavour, we propose to connect the pedagogical decisions made at design time with the analysis of the participants' interactions. Thus, teachers would be provided with relevant and coarse-grained information that could help them manage their CSCL scenarios. This paper synthesizes the main contributions obtained from a 3-year Design-Based Research process, and presents the findings obtained from the evaluation of the current proposal in 2 authentic CSCL scenarios. The participant teachers valued the proposal positively and stated that it was helpful for their orchestration of CSCL scenarios.

The field of learning design studies how to support teachers in devising suitable activities for their students to learn. The field of learning analytics explores how data about students' interactions can be used to increase the understanding of learning experiences. Despite its clear synergy, there is only limited and fragmented work exploring the active role that data analytics can play in supporting design for learning. This paper builds on previous research to propose a framework (analytics layers for learning design) that articulates three layers of data analytics-learning analytics, design analytics and community analytics-to support informed decision-making in learning design. Additionally, a set of tools and experiences are described to illustrate how the different data analytics perspectives proposed by the framework can support learning design processes.