Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards?

Praharaj, Sambit; Scheffel, Maren; Drachsler, Hendrik; Specht, Marcus

doi:10.1109/tlt.2021.3097766

Cited by 27 publications

(15 citation statements)

References 78 publications

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“…The same is true for other speech behaviors including speech overlap between team members, the overlap to speech activity ratio, and short and long pauses over the entire speech activity. Overall, we find that there is a lot more verbal interaction within the teams that complete the activity with higher learning gains, as observed in previous research on collaborative learning (Bassiou et al, 2016;Praharaj et al, 2021;Weinberger & Fischer, 2006). This is not surprising since the nature of the collaborative activity requires the learners to communicate, share information, and build common ground to enable construction of a solution (Barron, 2003;Roschelle & Teasley, 1995).…”

Section: Speech Activitysupporting

confidence: 85%

“…In the other two cases, we observe proposal-negation-agreement cycles (Barron, 2003;Roschelle, 1992) in the team members' dialogue during these periods of high speech overlap, indicating that the process of proposal discussion and uptake was happening, which is also indicative of good collaboration (Barron, 2003). Hence, contrary to the literature that suggests that the frequency of overlaps is negatively correlated with collaboration in children (Kim et al, 2015), speech overlap seems to be an indicator of the negotiation that is inherent in the collaborative learning process as also found by Bassiou et al (2016) and Praharaj et al (2021). The difference in the learning of the Silent Wanderers could be because of fewer such productive collaborative episodes within this group.…”

Section: Speech Activitymentioning

confidence: 79%

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Many are the ways to learn identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

Nasir

Kothiyal

Bruno

et al. 2021

Intern. J. Comput.-Support. Collab. Learn

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Understanding the way learners engage with learning technologies, and its relation with their learning, is crucial for motivating design of effective learning interventions. Assessing the learners’ state of engagement, however, is non-trivial. Research suggests that performance is not always a good indicator of learning, especially with open-ended constructivist activities. In this paper, we describe a combined multi-modal learning analytics and interaction analysis method that uses video, audio and log data to identify multi-modal collaborative learning behavioral profiles of 32 dyads as they work on an open-ended task around interactive tabletops with a robot mediator. These profiles, which we name Expressive Explorers, Calm Tinkerers, and Silent Wanderers, confirm previous collaborative learning findings. In particular, the amount of speech interaction and the overlap of speech between a pair of learners are behavior patterns that strongly distinguish between learning and non-learning pairs. Delving deeper, findings suggest that overlapping speech between learners can indicate engagement that is conducive to learning. When we more broadly consider learner affect and actions during the task, we are better able to characterize the range of behavioral profiles exhibited among those who learn. Specifically, we discover two behavioral dimensions along which those who learn vary, namely, problem solving strategy (actions) and emotional expressivity (affect). This finding suggests a relation between problem solving strategy and emotional behavior; one strategy leads to more frustration compared to another. These findings have implications for the design of real-time learning interventions that support productive collaborative learning in open-ended tasks.

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Section: Speech Activitysupporting

confidence: 85%

Section: Speech Activitymentioning

confidence: 79%

Many are the ways to learn identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

Nasir

Kothiyal

Bruno

et al. 2021

Intern. J. Comput.-Support. Collab. Learn

View full text Add to dashboard Cite

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“…Regarding the scope of MMLA data collection, Figure 6 provides an overview of the data collection in this review using the data classification schemes employed by Crescenzi‐Lanna (2020) and Praharaj et al (2021). Of interest is the different classification schemes of, for example, Ochoa (2022) and Mu et al (2020) discussed earlier.…”

Section: Methodsmentioning

confidence: 99%

Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

Prinsloo,

Slade,

Khalil

2023

Brit J Educational Tech

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Since the emergence of learning analytics (LA) in 2011 as a distinct field of research and practice, multimodal learning analytics (MMLA), shares an interdisciplinary approach to research and practice with LA in its use of technology (eg, low cost sensors, wearable technologies), the use of artificial intelligence (AI) and machine learning (ML), and the provision of automated, mostly real‐time feedback to students and instructors. Much of MMLA takes place in experimental and laboratory settings, researching students' learning in in‐between spaces—between research and classroom application, in‐between students' learning in private and public spaces as researchers track students' learning both in their use of social media and connected devices, and through the use of context‐aware and adaptive devices; and lastly, in‐between respecting students' privacy while increasingly using intrusive technologies. This study seeks to establish what is known about MMLA in terms of rationale for applications, the nature and scope of data collected, the study contexts, evidence of commercial interests and/or downstream uses of students' data, and consideration of ethics, privacy, and the protection of student data. This systematic review analysed 192 articles using a search string consisting of various combinations of multimodal (data) and learning analytics. The main findings include, inter alia, that though MMLA provide insights into learning and teaching, there is little evidence of MMLA findings successfully being applied to classroom settings, at scale. Given that the nature of MMLA research often necessitates the use of a range of (intrusive) sensors and recording technologies and can include children in its samples; the encroachment of students' right to privacy is a huge concern that is not addressed. There is also a need to reconsider the rationale for collecting multimodal data, the conditions under which such data collection will be ethical and in service of students' wellness, and the boundaries that should protect their (multimodal) data. Practitioner notesWhat is already known about this topic Experimental educational research predates both multimodal learning analytics (MMLA) and educational data mining (EDM). MMLA has been an integral part of learning analytics as research focus and practice since its inception. The increased digitisation and datafication, advances in technology (eg, sensor technology, geo‐tracking, etc.) and a growing normalisation of wearable technologies provides greater scope for collecting multimodal data. There is a vast body of published MMLA research providing a range of insight into students' and educators' behaviours aiming to increase the effectiveness of teaching. What this paper adds While there are several studies providing insights into the state of MMLA research, this study provides insight to a selected range of factors in MMLA research such as the type of research (empirical/conceptual); the nature and scope of data collected; the sample populations (pre‐higher education, higher education, etc.); evidence of commercial interests or consideration of downstream uses; issues pertaining to consent, privacy, data protection and ethics; and evidence of how findings were used to improve teaching and learning. The vast majority of MMLA research targets higher education, is empirical in nature and is based on relatively small samples of participation in experimental settings. Confirms previous research that found the predominance of small samples, and a lack of replicability and, as a result, lacking scalability. That there is very little explicit discussion of the ethical and privacy implications and data protection, either at design stage or for future implementation. Similarly, that there is little consideration of potential commercial interests or downstream uses of data. Implications for practice and/or policy MMLA in its essence requires interdisciplinary approaches and teams. For MMLA to move beyond small‐scale, experimental settings to application in real (classroom) settings, larger, replicable studies should be conducted together with ways to make study findings actionable for teachers and students. Ethical issues, commercial interests and downstream uses of collected data must be considered within the design and approval of MMLA research.

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“…In this sense, a closed and inflexible posture is less attractive than an open and relaxed posture. Identifying postures during collaboration may be important complementary information about the participants and may help to better understand the entire learning process [24].…”

Section: Related Workmentioning

confidence: 99%

Visualizing Collaboration in Teamwork: A Multimodal Learning Analytics Platform for Non-Verbal Communication

et al. 2022

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Developing communication skills in collaborative contexts is of special interest for educational institutions, since these skills are crucial to forming competent professionals for today’s world. New and accessible technologies open a way to analyze collaborative activities in face-to-face and non-face-to-face situations, where collaboration and student attitudes are difficult to measure using traditional methods. In this context, Multimodal Learning Analytics (MMLA) appear as an alternative to complement the evaluation and feedback of core skills. We present a MMLA platform to support collaboration assessment based on the capture and classification of non-verbal communication interactions. The developed platform integrates hardware and software, including machine learning techniques, to detect spoken interactions and body postures from video and audio recordings. The captured data is presented in a set of visualizations, designed to help teachers to obtain insights about the collaboration of a team. We performed a case study to explore if the visualizations were useful to represent different behavioral indicators of collaboration in different teamwork situations: a collaborative situation and a competitive situation. We discussed the results of the case study in a focus group with three teachers, to get insights in the usefulness of our proposal. The results show that the measurements and visualizations are helpful to understand differences in collaboration, confirming the feasibility the MMLA approach for assessing and providing collaboration insights based on non-verbal communication.

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Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards?

Cited by 27 publications

References 78 publications

Many are the ways to learn identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

Many are the ways to learn identifying multi-modal behavioral profiles of collaborative learning in constructivist activities

Multimodal learning analytics—In‐between student privacy and encroachment: A systematic review

Visualizing Collaboration in Teamwork: A Multimodal Learning Analytics Platform for Non-Verbal Communication

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