Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups

Schneider, Bertrand; Sharma, Kshitij; Cuendet, Sébastien; Zufferey, Guillaume; Dillenbourg, Pierre; Pea, Roy

doi:10.1007/s11412-018-9281-2

Cited by 76 publications

(75 citation statements)

References 18 publications

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“…First, rather than utilizing the full RQA matrix, which compares each point to all other points and is therefore less theoretically interesting, we restricted our analyses to recurrence points across short time lags. This is consistent with findings that tighter coupling occurs during real‐time interaction, as well as associated research that analyzes time lags up to 2 s in order to examine local coupling patterns (Coco & Dale, ; Richardson & Dale, ; Schneider et al, ). We extend this approach to aRQA and MdRQA by focusing on local dynamics, or time lags occurring between 1 and 2 s apart.…”

Section: Methodssupporting

confidence: 89%

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

2019

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We hypothesize that effective collaboration is facilitated when individuals and environmental components form a synergy where they work together and regulate one another to produce stable patterns of behavior, or regularity, as well as adaptively reorganize to form new behaviors, or irregularity. We tested this hypothesis in a study with 32 triads who collaboratively solved a challenging visual computer programming task for 20 min following an introductory warm‐up phase. Multidimensional recurrence quantification analysis was used to examine fine‐grained (i.e., every 10 s) collective patterns of regularity across team members' speech rate, body movement, and team interaction with the shared user interface. We found that teams exhibited significant patterns of regularity as compared to shuffled baselines, but there were no systematic trends in regularity across time. We also found that periods of regularity were associated with a reduction in overall behavior. Notably, the production of irregular behavior predicted expert‐coded metrics of collaborative activity, such as teams' ability to construct shared knowledge and effectively negotiate and coordinate execution of solutions, net of overall behavioral production and behavioral self‐similarity. Our findings support the theory that groups can interact to form interpersonal synergies and indicate that information about system‐level dynamics is a viable way to understand and predict effective collaborative processes.

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Section: Methodssupporting

confidence: 89%

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

2019

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“…In the context of collaborative learning, the dynamic and reciprocal adaptation of shared interaction emerges: that is, when individuals in a group are not only working on the same activity at the same time, but are also all "in tune" mentally (Baker 2002;Popov et al 2017). Therefore, synchronicity between individuals in collaborative learning can also be seen in gazes (Schneider and Pea 2013), joint visual attention (Schneider et al 2018), and physiology (Ahonen et al 2018;Gillies et al 2016).…”

Section: Collaborative Learning and Physiological Synchronymentioning

confidence: 99%

Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam

Malmberg

Haataja

Seppänen

et al. 2019

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

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The coordination of cognitive and non-cognitive interactive processes contributes to successful collaboration in groups, but it is hard to evidence in computer-supported collaborative learning (CSCL). Monitoring is a metacognitive process that can be an indicator of a student's ability to recognize success or failure in collaboration. This study focuses on how monitoring occurs in CSCL during a collaborative exam situation by examining how individual student contributions to monitoring processes are related to physiological synchrony and physiological arousal in groups. The participants were organized in four groups of three members each, and they wore sensors that measured their physiological activity. The data consist of video recordings from collaborative exam sessions lasting 90 minutes and physiological data captured from each student with Empatica 4.0 sensors. The video data were analyzed using qualitative content analysis to identify monitoring events. Students' physiological arousal was determined through peak detection, and physiological concordance was used as an index for the students' physiological synchrony. The individual and group level analysis investigated arousal and physiological synchrony in concordance with monitoring during the collaborative exam. The results showed that, in each group, each student contributed to joint monitoring. In addition, the monitoring activities exhibited a significant correlation with the arousal, indicating that monitoring events are reflected in physiological arousal. Physiological synchrony occurred within two groups, which experienced difficulties during the collaborative exam, whereas the two groups who had no physiological synchrony did not experience difficulties. It is concluded that physiological synchrony may be a new indicator for recognizing meaningful events in CSCL

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“…Another approach, multimodal learning analytics (MMLA), seeks to capture and synthesize some combination of all three types of learning data: behavioral, physiological and representational, towards the goal of developing comprehensive models of student learning (Worsely et al, 2016). Recent studies related to colocated, computer-based, collaborative problem solving (CPS) using the MMLA approach, focus on understanding how gaze, gesture and physical actions in the computer environment predict group success (Cukurova, Luckin, Millán, & Mavrikis, 2018;Schneider & Blikstein, 2015;Schneider, Sharma, Cuendet, Aufferey, Dillenbourg, & Pea, 2018;Spikol, Ruffaldi, Landolfi, & Cukurova, 2017). These studies have demonstrated the utility of these types of data as a means of convergent triangulation in correlating non-verbal elements with learning gains.…”

Section: Learning Analyticsmentioning

confidence: 99%

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

Sullivan¹,

Keith²

2019

Brit J Educational Tech

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In this study, we explore the potential of a natural language processing (NLP) approach to support discourse analysis of in‐situ, small group learning conversations. The theoretical basis of this work derives from Bakhtin’s notion of speech genres as bounded by educational robotics activity. Our goal is to leverage computational linguistics methods to advance and improve educational research methods. We used a parts‐of‐speech (POS) tagging program to automatically parse a transcript of spoken dialogue collected from a small group of middle school students involved in solving a robotics challenge. We grammatically parsed the dialogue at the level of the trigram. Then, through a deliberative process, we mapped the POS trigrams to our theoretically derived problem solving in computational environments coding system. Next, we developed a stacked histogram visualization to identify rich interactional segments in the data. Seven segments of the transcript were thus identified for closer analysis. Our NLP‐based approach partially replicated prior findings. Here, we present the theoretical basis for the work, our analytical approach in exploring this NLP‐based method, and our research findings. What is already known about this topic Over the last 10 years, several educational research papers indicate that natural language processing (NLP) techniques can be used to help interpret well‐structured, written dialogue, eg, conversations in online class discussions. Two recent papers indicate that NLP techniques can also be used to help interpret well‐structured, spoken dialogue, eg, replies to interview questions and/or comments made during think aloud protocols. Multimodal learning analytic techniques are being used to investigate collaborative learning. These studies use non‐verbal features of data (gaze, gesture, physical actions), prosodic features of verbal data (pitch and tone) and/or turn‐taking and duration of talk per speaker data, as means of predicting group success. None of the MMLA studies attempt semantic analysis of student talk in collaborative settings. What this paper adds A theoretical framework for why and how an automated NLP approach can support discourse analysis research on co‐located, computer‐based, collaborative problem solving interactions. This framework, entitled the Problem Solving in Computational Environment Speech Genre, links children’s physical interactions with computational devices to their verbal exchanges and presents a theoretical rationale for the use of NLP methods in educational research. Description of an interdisciplinary method that combines NLP techniques with qualitative coding approaches to support analysis of student collaborative learning with educational robotics. Identification of student learning outcomes derived from the semantic, PSCE Speech Genre and NLP approach. Implications for practice and/or policy Educational researchers will be able to expand upon our findings towards the goal of using computation and automation to support microgenetic analysis of la...

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Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups

Cited by 76 publications

References 18 publications

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

Beyond Dyadic Coordination: Multimodal Behavioral Irregularity in Triads Predicts Facets of Collaborative Problem Solving

Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam

Exploring the potential of natural language processing to support microgenetic analysis of collaborative learning discussions

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