Sensor-based analytics in education

The use of sensors to support learning technology research and practice is not new, whether this was in the context of wearable technology, context-aware technology, or ubiquitous systems. Nevertheless, the proliferation of sensing technology has driven the field of learning technology in the development of tools and methods that can benefit from the produced sensor-based analytics (SBA). SBA fulfills the vision of integrating many sources of information, coming from different channels to both strengthen learning systems’  capacity  (e.g., adaptation, affect detection, embodied interaction), but also improve the research practices of the field. Sensing technologies like, eye-tracking, motion cameras, and wearables, combined with powerful AI and ML algorithms, have the capacity to empower teachers, learners, and researchers with (near) real-time insight. In this talk I will describe how collecting and combining SBA can provide valuable information in designing meaningful learning experiences.

Davinia Hernández-Leo is Full Professor at the Department of Information and Communications Technologies Department (DTIC) at UPF, the head of the Interactive and Distributed Technologies for Education group (TIDE, and Vice-Dean of the UPF Engineering School. She obtained a degree and a Ph.D, in Telecommunication Engineering at University of Valladolid, Spain, and has been visiting researcher at Open University of the Netherlands, Fulbright Scholar at Virginia Tech and visiting academic at the University of Sydney.

She has published extensively and received several awards, including best paper awards and the European award for excellence in the field of CSCL technology. She has also received Teaching Awards (Vicens Vives) and the ICREA Academia Award from the Catalan Government in 2019. She is currently the Vice-President of the European Association for Technology-Enhanced Learning and a member of the Steering Committee of the European Conference on Technology-Enhanced Learning.

Productive collaborative learning and technology support  

In this talk I will summarize research results leading to practical implications in the achievement of productive collaborative learning. In particular, I will focus on how technology can support both the design and orchestration of collaborative learning scenarios. The technology presented will include authoring tools, teaching community platforms, enactment systems and orchestration dashboards based on learning analytics. I will also discuss synergies between technological solutions emphasizing human-in-control and machine-in-control perspectives.

Margarida Romero

Research director of the Learning, INnovation and Learning, Laboratoire d’Innovation et Numérique pour l’Éducation (LINE), a research lab in the field of Technology Enhanced Learning (TEL). Full professor at Université Côte d’Azur (France) and associate professor at Université Laval (Canada). 

Robots, learners and creative problem solving In creative problem solving tasks with unfamiliar technologies there is not the possibility to plan ahead and execute an algorithm solution. The gulf of execution of the creative problem solving task proposed, and the unfamiliarity of the technologies proposed to the participant requires to explore these technologies to discover their potentials and actualize both the knowledge of the participant and the state of the technological system. 

Within the process of interpreting the goals but also the technological system, the knowledge of the user is of a key importance both in perceiving, interpreting and evaluating the technological system. The lack of prior knowledge related to unfamiliar technologies requires the participant to interact with the objects to create, in an emergent way,  the technological knowledge required to solve the task. The manipulative exploration of the objects changes the problem space and contributes to construct the technological knowledge which contributes to move towards the solution space.

The actualisation process of the technological system and the participants’ knowledge is developed through the creative problem solving process interactions. While the user’s actions change the state of the technological system, the knowledge of the user in relation to his interpretation of the feedback of the technological system allows to actualize the user’s knowledge.