Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members?

119

Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

Section: For Learning Behavior and Performancementioning

confidence: 99%

Section: Importance Of Contextualization and Ecological Validitymentioning

confidence: 99%

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

119

“…The authors explain how the learning context refers to the interaction between learners and multiple people, artefacts and environments, and they draw attention to the importance of comprehensive studies on contextual factors in bridging the gap between research and practice. The emerging use of MMLA, which considers different sensors and computer systems for data collection during learning activities (Pijeira‐Diaz, Drachsler, Järvelä, & Kirschner, 2019), offers a methodological approach that can provide further insights into collaborative face‐to‐face spaces (Ricca, Bowers, & Jordan, 2019). The diversity of data (Spikol, Ruffaldi, Landolfi, & Cukurova, 2017), fusion and analysis methods (Shankar, Prieto, Rodríguez‐Triana, & Ruiz‐Calleja, 2018) and advanced developments in sensor technology (Schneider, Di Mitri, Limbu, & Drachsler, 2018) bring vast possibilities in terms of studying the effects of learning spaces.…”

Section: Introductionmentioning

confidence: 99%

Round or rectangular tables for collaborative problem solving? A multimodal learning analytics study

Vujović¹,

Hernández‐Leo²,

Tassani³

2020

The current knowledge of the effects of the physical environment on learners’ behaviour in collaborative problem‐solving tasks is underexplored. This paper aims to critically examine the potential of multimodal learning analytics, using new data sets, in studying how the shapes of shared tables affect the learners’ behaviour when collaborating in terms of patterns of participation and indicators related to physical social interactions. The research presented in this paper investigates this question considering the potential interplay with contextual aspects (level of education) and learning design decisions (group size). Three dependent variables (distance between students, range of movement and level of participation) are tested using quantitative and qualitative analyses of data collected using a motion capture system and video recordings. Results show that the use of round tables (vs rectangular tables) leads to higher levels of on‐task participation in the case of elementary school students. For university students, different table shapes seem to have a limited impact on their levels of participation in collaborative problem solving. The analysis shows significant differences regarding the relationship between group size and the distance between students, but there is no substantial evidence that group size affects the level of participation. The findings support previous research highlighting the importance of studying the role of the physical environment as an element of learning design and the potential of multimodal learning analytics in approaching these studies.

“…Schneider, Reilly, and Radu (2020) point out concerns related to the increasing amount of fine-grained data that are infiltrating educational environments. However, recent MMLA studies collecting physiological data (eg, Echeverria, Martinez-Maldonado, & Buckingham Shum, 2019;Pijeira-Díaz et al, 2019) may promote the use of responsible practices, but do not make explicit the importance of informed consent and ensuring adequate understanding of the study and its risks by participants. The results of our study point to deficiencies in our informed consent process for MMLA research.…”

Section: Discussionmentioning

confidence: 99%

“…Measures of electrodermal activity (Pijeira-Díaz, Drachsler, Järvelä, & Kirschner, 2019), heart rate (Larra et al, 2014) and neural oscillations via an electroencephalogram (EEG) (Sun & Yeh, 2017) are more frequently being used. This biometric data are considered sensitive health data (Chassang, 2017).…”

Section: Practitioner Notesmentioning

confidence: 99%

Enhancing consent forms to support participant decision making in multimodal learning data research

Beardsley¹,

Moreno²,

Vujović³

et al. 2020

Advances in the field of multimodal learning analytics (MMLA) research is often accomplished by actively exploring new technologies and techniques related to the collection and analysis of data. Exploration of ethical principles and procedures for governing the use of new technologies and techniques, however, is not as readily pursued. As collected data grow in complexity and invasiveness, potentially, a growing need is arising to scrutinize ethical aspects of MMLA research. In our study, we introduce an informed consent comprehension test for educational technology research and assess the effects of enhancing MMLA consent forms on comprehension of informed consent and on rates of enrollment in a MMLA study. One form is written from a researcher perspective and the other from a participant perspective. Results of the study involving first‐year undergraduate students suggest that the overall level of comprehension did not differ between conditions. Yet, the participant‐oriented consent form resulted in significantly lower rates of enrollment. Implications for MMLA researchers are discussed.