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Analytics for Collaboration and Creativity

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Learning Analytics for understanding small-group collaborative processes in multi-device spaces

Roberto Martinez-Maldonado (CIC), Simon BuckinghamShum (CIC)

Communicating and articulating thoughts and ideas effectively using oral, written and nonverbal communication channels, in a variety of forms and contexts, are critical 21st century skills for lifelong learning. Part of the work that some of the members of CIC have previously done consisted in supporting collaboration of groups using ecologies of devices, with an emphasis on automated data capture for learning analytics and data mining. The overarching goal has been to use different tools or sensors that could capture some traces of collaboration without interrupting the activity. So we have used, for example, kinect sensors to differentiate some learner’s actions, microphone arrays to capture traces of conversation, and multitouch technologies. This made it possible to generate live dashboards that could mirror information to teachers or students. We have recently started to explore how to integrate pen-based interaction in an ecology of tools to support collaborative and creative tasks. This has been as a consequence of multiple observations that identified that many key moments of group collaboration were mediated by hand-written artefacts and inscriptions.

This project aims to support face-to-face and blended small-group collaborative learning through the provision of formative feedback and cues about the collaborative process.

This research has three main goals:

1) making use of different Learning Analytics and Data Mining tools to generate understanding of small-group collaborative processes of users interacting at multi-device spaces;

2) exploring ways to provide immediate or delayed feedback to the group members in the form of visualisations and/or notifications automatically generated; and

3) finding patterns of collaborative interaction that can differentiate high from low performers.

We will conduct a series of face-to-face user trials in a set physical space, which will be iteratively enhanced with emerging technologies, namely, interactive surface devices (such as interactive tabletops and/or interactive whiteboards), large screens, microphone arrays, smart pens, paper and other non-digital writing materials. In each iteration, we expect to run no more than ten experiments with small groups of 2 to 6 participants in each.

Part of the work that some of the members of CIC have previously done consisted in supporting the collaboration of groups using ecologies of devices, with an emphasis on automated data capture for learning analytics and data mining. The overarching goal has been to use different tools or sensors that could capture some traces of collaboration without interrupting the activity. So we have used, for example, Kinect sensors to differentiate some learner’s actions, microphone arrays to capture traces of conversation, and multitouch technologies. This made it possible to generate live dashboards that could mirror information to teachers or students. We have recently started to explore how to integrate pen-based interaction in an ecology of tools to support collaborative and creative tasks. This has been as a consequence of multiple observations that identified that many key moments of group collaboration were mediated by hand-written artefacts and inscriptions.

The following short demo videos can provide a quick idea of the type of tools we have built in the past:

Learning analytics in the classroom

Multi-device ecologies

For more information about the project, please, contact: Dr. Roberto Martinez-Maldoando

Key publications:

Martinez-Maldonado, R., Goodyear, P., J.Kay, Thompson, K., and Carvalho (2016) An Actionable Approach to Understand Group Experience in Complex, Multi-surface SpacesSIGCHI Conference: Human Factors in Computing Systems, CHI 2016, (Acceptance Rate 23%).

Martinez-Maldonado, R., Yacef, K. and Kay, J. (2013) Data Mining in the Classroom: Discovering Groups’ Strategies at a Multi-tabletop Environment. International Conference on Educational Data mining, EDM 2013, pages 121-128.

Martinez-Maldonado, R., Wallace, J., Kay, J., and Yacef, K. (2011) Modelling and identifying collaborative situations in a collocated multi-display groupware setting.  International Conference on Artificial Intelligence in Education, AIED 2011, pages 196-204.

 

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