Community Learning Analytics with Industry 4.0 and Wearable Sensor Data
Learning analytics in formal learning contexts is often restricted to collect and analyze data from students following curricula through a learning management system. In informal learning, however, a deep understanding of learners and entities interacting with each other is needed. The practice of exploring these interactions is known as community learning analytics. Mobile devices, wearables and interconnected Industry 4.0 production machines equipped with a multitude of sensors collecting vast amounts of data are ideal candidates to capture the goals and activities of informal learning settings. What is missing is a methodological approach to collect, manage, analyze and exploit data coming from such an interconnected network of artifacts. In this paper, we present a concept and prototypical implementation of a framework that is able to gather, transform and visualize data coming from Industry 4.0 and wearable sensors and actuators. Our collaborative Web-based visual analytics platform is highly embeddable and extensible on various levels. Its open source availability fosters research on community learning analytics on a broad level.
KeywordsCommunity learning analytics Visual analytics Industry 4.0 Internet of Things Wearables
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme project “Learning Layers” (grant no. 318209) and the European Union’s Horizon 2020 Programme through the project “WEKIT” (grant no. 687669).
- 1.Bosch: Bosch’s IoT platform (2017). https://www.bosch-si.com/de/iot-plattform/bosch-iot-suite/homepage-bosch-iot-suite.html
- 3.IBM: Watson IoT Platform (2017). https://www.ibm.com/internet-of-things/platform/watson-iot-platform/
- 4.Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual analytics: definition, process, and challenges. In: Kerren, A., Stasko, J.T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). doi:10.1007/978-3-540-70956-5_7 CrossRefGoogle Scholar
- 6.Klamma, R., Renzel, D., de Lange, P., Janßen, H.: Las2peer - A Primer (2016)Google Scholar
- 9.Patni, H., Henson, C., Sheth, A.: Linked sensor data. In: 2010 International Symposium on Collaborative Technologies and Systems, pp. 362–370Google Scholar
- 10.Ruiz-Calleja, A., Dennerlein, S.M., Ley, T., Lex, E.: Visualizing workplace learning data with the SSS Dashboard. In: Learning Analytics Across Physical and Digital Spaces 2016, CEUR Workshop Proceedings, CrossLAK 2016, vol. 1601. CEUR (2016)Google Scholar
- 12.Worsley, M., Blikstein, P.: What’s an Expert? Using learning analytics to identify emergent markers of expertise through automated speech, sentiment and sketch analysis. In: Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero, C., Stamper, J. (eds.) 4th International Conference on Educational Data Mining, pp. 235–240 (2011)Google Scholar