Using multimodal learning analytics to study collaboration on discussion groups

Riquelme, Fabián; Castro-Muñoz, Roberto; Lean, Roberto Mac; Villarroel, Rodolfo; Barcelos, Thiago; Albuquerque, Victor Hugo C. de

doi:10.1007/s10209-019-00683-w

Cited by 37 publications

(30 citation statements)

References 30 publications

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“…These microphones were used to record and store the students’ speech interventions, identifying the opportunities in which they spoke, as well as the duration of each intervention carried out. For the preprocessing, analysis, and reporting of the results, software generated by the authors was used [5]. During steps 5, 7, and 9 of the activity, students were videotaped through a tablet that recorded the whole process of building artifacts with Lego ® bricks in each sprint.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Small and low-cost single-board computers (such as Raspberry Pi) and single-board microcontrollers (such as Arduino) provide a huge opportunity for the implementation of low-cost sensors [4]. These types of solutions would help the development of core skills through collaborative learning, because the teacher would be able to have a greater and better amount of data generated by the students [5]. The automation of data acquisition and analysis processes is another direct benefit in the incorporation of sensors into the classroom [6].…”

Section: Introductionmentioning

confidence: 99%

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

Cornide-Reyes

Noël

Riquelme

et al. 2019

Sensors

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Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article 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. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cornide-Reyes

Noël

Riquelme

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, learners working together in teams ultimately construct their understanding about different phenomena by discussing and negotiating the meanings they have gathered both from available learning resources and each other through different sensory and communicative modalities (Riquelme et al 2019;Anastopoulou et al 2011;Kress 2003). In this regard, multimodal research, which is studying individuals' learning endeavours by gathering and exploring different multimodal data, might help to explain the complex interplay of cognitive, motivational, and emotional processes during the learning process.…”

Section: Multimodality Of Learningmentioning

confidence: 99%

Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review

et al. 2020

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This systematic review on data modalities synthesises the research findings in terms of how to optimally use and combine such modalities when investigating cognitive, motivational, and emotional learning processes. ERIC, WoS, and ScienceDirect databases were searched with specific keywords and inclusion criteria for research on data modalities, resulting in 207 relevant publications. We provide findings in terms of target journal, country, subject, participant characteristics, educational level, foci, type of data modality, research method, type of learning, learning setting, and modalities used to study the different foci. In total, 18 data modalities were classified. For the 207 multimodal publications, 721 occurrences of modalities were observed. The most popular modality was interview followed by survey and observation. The least common modalities were heart rate variability, facial expression recognition, and screen recording. From the 207 publications, 98 focused exclusively on the cognitive aspects of learning, followed by 27 publications that only focused on motivation, while only five publications exclusively focused on emotional aspects. Only 10 publications focused on a combination of cognitive, motivational, and emotional aspects of learning. Our results plea for the increased use of objective measures, highlight the need for triangulation of objective and subjective data, and demand for more research on combining various aspects of learning. Further, rather than researching cognitive, motivational, and emotional aspects of learning separately, we encourage scholars to tap into multiple learning processes with multimodal data to derive a more comprehensive view on the phenomenon of learning.

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“…For the sake of addressing this problem, a framework is proposed for the collection, storage, processing and visualization of geolocation data in closed environments using beacons devices. This framework is related to the one proposed by Riquelme et al [47] for speech measurement using ReSpeaker devices. The system is supported by an application with two main components: a mobile application, that captures the data emitted by Estimote beacons 1 (via Bluetooth), and a web application, where the data captured are processed.…”

Section: Developed Solutionmentioning

confidence: 99%

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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Using multimodal learning analytics to study collaboration on discussion groups

Cited by 37 publications

References 30 publications

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

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

Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review

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

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