JUCS - Journal of Universal Computer Science 24(2): 149-170, doi: 10.3217/jucs-024-02-0149
Development of a Software that Supports Multimodal Learning Analytics: A Case Study on Oral Presentations
expand article infoRoberto Munoz‡, Rodolfo Villarroel§, Thiago S. Barcelos|, Alexandra Souza|, Erick Merino‡, Rodolfo Guiñez‡, Leandro A. Silva
‡ Universidad de Valparaíso, Valparaíso, Chile§ Pontificia Universidad Catolica de Valparaíso, Valparaíso, Chile| Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, São Paulo, Brazil¶ Universidade Presbiteriana Mackenzie, São Paulo, Brazil

Corresponding author: Roberto Munoz ( roberto.munoz@uv.cl )

© Roberto Munoz, Rodolfo Villarroel, Thiago Barcelos, Alexandra Souza, Erick Merino, Rodolfo Guiñez, Leandro Silva.
This article is freely available under the J.UCS Open Content License.
Citation: Munoz R, Villarroel R, Barcelos TS, Souza A, Merino E, Guiñez R, Silva LA (2018) Development of a Software that Supports Multimodal Learning Analytics: A Case Study on Oral Presentations. JUCS - Journal of Universal Computer Science 24(2): 149-170. https://doi.org/10.3217/jucs-024-02-0149
Open Access
Abstract
Learning Analytics is the intelligent use of data generated from students with the objective of understanding and improving the teaching and learning process. Currently, there is a lack of tools to measure the development of complex skills in real classroom environments that are flexible enough to add and process data from different sensors and oriented towards a massive public. Based on this finding, we developed a free software system that permits to capture and to visualize a set of 10 body postures using the Microsoft Kinect sensor, along with the ability to track custom body postures and data from other sensors. The developed tool was validated by means of precision and usability tests. Furthermore, with the goal of demonstrating the potential of incorporating this type of software into the classroom, the software was used as a tool to give feedback to the teacher and to the students at the moment of giving and evaluating oral presentations. Also, a clustering analysis of data gathered from 45 student presentations indicate that presentations on similar topics with also similar complexity levels can be successfully discriminated.
Keywords
multimodal learning analytics, microsoft kinect, oral presentations, self-organizing maps