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Article

A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

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Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil
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Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile
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Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
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Departamento de Ingeniería Informática y Ciencias de la Computación, Universidad de Atacama, Copiapó 1531772, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Peham
Sensors 2021, 21(4), 1525; https://doi.org/10.3390/s21041525
Received: 17 December 2020 / Revised: 5 January 2021 / Accepted: 25 January 2021 / Published: 22 February 2021
Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses. View Full-Text
Keywords: multimodal learning analytics; oral presentations; educational data mining; sequential pattern mining multimodal learning analytics; oral presentations; educational data mining; sequential pattern mining
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MDPI and ACS Style

Vieira, F.; Cechinel, C.; Ramos, V.; Riquelme, F.; Noel, R.; Villarroel, R.; Cornide-Reyes, H.; Munoz, R. A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations. Sensors 2021, 21, 1525. https://doi.org/10.3390/s21041525

AMA Style

Vieira F, Cechinel C, Ramos V, Riquelme F, Noel R, Villarroel R, Cornide-Reyes H, Munoz R. A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations. Sensors. 2021; 21(4):1525. https://doi.org/10.3390/s21041525

Chicago/Turabian Style

Vieira, Felipe, Cristian Cechinel, Vinicius Ramos, Fabián Riquelme, Rene Noel, Rodolfo Villarroel, Hector Cornide-Reyes, and Roberto Munoz. 2021. "A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations" Sensors 21, no. 4: 1525. https://doi.org/10.3390/s21041525

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