Introducing Low-Cost Sensors into the Classroom Settings: Improving the Assessment in Agile Practices with Multimodal Learning Analytics

Cornide-Reyes, Hector; Noël, René; Riquelme, Fabián; Gajardo, Matías; Cechinel, Cristian; Lean, Roberto Mac; Becerra, Carlos; Villarroel, Rodolfo; Castro-Muñoz, Roberto

doi:10.3390/s19153291

Cited by 29 publications

(19 citation statements)

References 47 publications

(71 reference statements)

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“…For [ 28 ], MMLA is a way to improve the core skills of students. The authors present an exploratory study that analyzes the collaboration and communication of students in a Software Engineering class of agile practices using Lego4scrum.…”

Section: Related Workmentioning

confidence: 99%

A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Vieira

Cechinel

Ramos

et al. 2021

Sensors

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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.

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Section: Related Workmentioning

confidence: 99%

A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Vieira

Cechinel

Ramos

et al. 2021

Sensors

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“…The application associates a unique identifier to each user. Through the application, each student's cell phone can capture the data 1 https://estimote.com/ transmitted by nearby beacons via Bluetooth. In this way, the time of entry and exit of the area covered by each beacon can be recorded.…”

Section: A Technical Environmentmentioning

confidence: 99%

“…In education, integrating technologies to improve the quality of learning represents a growing challenge. In fact, different technological tools are being developed for this purpose [1], [2]. The use of devices in the classroom provides quantitative information to teachers about how the teaching-learning process is being developed [3].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, MMLA seeks to quantify what was not quantifiable before and understand people's behavior from the measurement of specific skills. MMLA techniques have recently been used to evaluate the collaboration of individuals in learning environments [3], [7], in programming tasks [8] and in the training of agile practices [1], [9]. According to Worsley [3], one of the MMLA techniques that has been less explored and developed is micro-location.…”

Section: Introductionmentioning

confidence: 99%

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Where are You? Exploring Micro-Location in Indoor Learning Environments

et al. 2020

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Classroom teaching methodologies are gradually changing from masterclasses to active learning practices, and peer collaboration emerges as an essential skill to be developed. However, there are several challenges in evaluating collaborative activities more objectively, as well as to generate valuable information to teachers and appropriate feedback to students about their learning processes. In this context, multimodal learning analytics facilitate the evaluation of complex skills using data from multiple data sources. In this work, we propose the use of beacons to collect geolocation data from students who carry out collaborative tasks that involve movement and interactions through space. Furthermore, we suggest new ways to analyze, visualize, and interpret the data obtained. As a first practical approach, we carried out an exploratory, collaborative activity with sixteen undergraduate students working in a library, with bookshelves and work tables monitored by beacons. From the analysis of student movement dynamics, three types of well-differentiated student roles were identified: the collectors, those who go out to collect data from the bookshelves, ambassadors, those who communicate with other groups, and the secretaries, those who stay at their work desk to shape the requested essay. We believe these findings are valuable feedback for the enhancement of the learning activity and the first step towards MMLA-driven Teaching Process Improvement method.

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“…Cornide-Reyes et al presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course who perform a learning activity simulating Scrum with Lego® bricks [9]. Data from the Scrum process is captured and multidirectional microphones are used in the retrospective ceremonies.…”

Section: Contributionsmentioning

confidence: 99%

Advanced Sensors Technology in Education

Crespo

Burgos

2019

Sensors

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The topic presented will show how different kinds of sensors can help to improve our skills in learning environments. When we open the mind and let it take the control to be creative, we can think how a martial art would be improved with registered sensors, or how a person may dance with machines to improve their technique, or how you may improve your soccer kick for a penalties round. The use of sensors seems easy to imagine in these examples, but their use is not limited to these types of learning environments. Using depth cameras to detect patterns in oral presentations, or improving the assessment of agility through low cost-sensors with multimodal learning analytics, or using computing devices as sensors to measure their impact on primary and secondary students’ performances are the focus of this study as well. We hope readers will find original ideas that allow them to improve and advance in their own researches.

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Introducing Low-Cost Sensors into the Classroom Settings: Improving the Assessment in Agile Practices with Multimodal Learning Analytics

Cited by 29 publications

References 47 publications

A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations

Where are You? Exploring Micro-Location in Indoor Learning Environments

Advanced Sensors Technology in Education

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