Computationally Augmented Ethnography: Emotion Tracking and Learning in Museum Games

Martin, Kit; Wang, Emily; Bain, Connor; Worsley, Marcelo

doi:10.1007/978-3-030-33232-7_12

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…Nowadays, learning analytics research focused on supporting collaboration and group learning has diversified, and perhaps even fragmented, across various research communities, such as CSCL (e.g., Spikol, Ruffaldi, & Cukurova, 2017a), technologyenhanced learning (e.g., Praharaj, Scheffel, Drachsler, & Specht, 2018), team science (e.g., Kim, Sottilare, Brawner, & Flowers, 2018), and human-computer interaction (e.g., Chandrasegaran, Bryan, Shidara, Chuang, & Ma, 2019). Novel analytic approaches have emerged and are applied to study collaboration with a wide variety of purposes, such as assessment and measurement of collaborative learning (Khan, 2017), theory building (Malmberg et al, 2018), orchestration support (Olsen, 2017), dashboards design (van Leeuwen, Rummel, & van Gog, 2019), and user modelling (Worsley, 2019). The emergence of accurate and inexpensive sensors is also enabling the exploration of multimodal aspects of group interaction, such as gaze tracking for measuring joint attention of students (Schneider & Pea, 2017), physiological sensing for identifying group synchrony Schneider, Dich, & Radu, 2020), and gesture/motion tracking for inferring collaboration strategies in maker-spaces (Worsley & Blikstein, 2018).…”

Section: Introductionmentioning

confidence: 99%

What Do You Mean by Collaboration Analytics? A Conceptual Model

Martínez-Maldonado

Gašević

Echeverria

et al. 2021

JLA

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Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.

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Section: Introductionmentioning

confidence: 99%

What Do You Mean by Collaboration Analytics? A Conceptual Model

Martínez-Maldonado

Gašević

Echeverria

et al. 2021

JLA

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“…et al, 2020), dialogue's characteristics such as pitch(Olsen et al, 2020;Sharma, Leftheriotis, et al, 2020) and speech rate(Vrzakova et al, 2020). Another commonly used practice is employing the video modality to capture facial data; Lee-Cultura,Martin et al, 2019). Video techniques have also been used to capture students' motion activity by detecting body movements(Vrzakova et al, 2020;Vujovic et al, 2020) and the number of the facein-the-screen and distance metrics between bodies(Cukurova, Zhou, et al, 2020).Eye-tracking: Another widely employed and "insightful" modality is eyetracking.…”

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confidence: 99%

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Alwahaby¹,

Cukurova²,

Papamitsiou³

et al. 2021

Preprint

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There is a growing interest in the research and use of multimodal data in learning analytics. This paper presents a systematic literature review of multimodal learning analytics (MMLA) research to assess i) the available evidence of impact on learning outcomes in real-world contexts and ii) explore the extent to which ethical considerations are addressed. A few recent literature reviews argue for the promising value of multimodal data in learning analytics research. However, our understanding of the challenges associated with MMLA research from real-world teaching and learning environments is limited. To address this gap, this paper provides an overview of the evidence of impact and ethical considerations stemming from an analysis of the relevant MMLA research published in the last decade. The search of the literature resulted in 663 papers, of which 100 were included in the final synthesis. The results show that the evidence of real-world impact on learning outcomes is weak, and ethical aspects of MMLA work are rarely addressed. We discuss our results through the lenses of two theoretical frameworks for evidence of impact types and ethical dimensions of MMLA. We conclude that for MMLA to stay relevant and become part of mainstream education, future research should directly address the gaps identified in this review.

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“…On the other hand, the model representing connections in topic-based relational context did not exhibit connections between Physical abuse and Dissonance, which could have been an anticipated connection, especially since all our non-topic-based models exhibited a connection between these two codes. A qualitative examination is justified in this case as well, from which one could draw such conclusions as: 1) Kristina does not see any discrepancies in how others perceive her and how she feels, or between how she behaves and feels regarding physical abuse, but does with respect to emotional abuse and loss or 2) Kristina mostly mentions Physical abuse in relation to herself (lines [16][17][18][19][20][21][22][23] and Dissonance in relation to others (24)(25)(26).…”

Section: Perspective Topicmentioning

confidence: 86%

Parsing the Continuum: Manual Segmentation of Monologic Data

Zörgő¹,

Brohinsky²

2022

Preprint

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Segmentation is a crucial step in analyses of qualitative data where code or code co-occurrence frequencies are of interest. Decisions about how best to segment are inextricably connected to coding decisions, as well as wider analytical goals and research questions. These decisions directly affect resulting models and the interpretations derived from them. However, while there is a wealth of frameworks guiding code development and application, far fewer guidelines exist for segmentation. This paper reports on the development of an initial set of heuristics for the segmentation of monologic data. Using the framework of Epistemic Network Analysis, we demonstrate various approaches to segmentation and show how these segmentation decisions affect models and subsequent interpretations. We argue that segmentation should be aligned with research questions and developed in conjunction with coding, and we offer considerations and techniques for doing so.

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Computationally Augmented Ethnography: Emotion Tracking and Learning in Museum Games

Cited by 11 publications

References 16 publications

What Do You Mean by Collaboration Analytics? A Conceptual Model

What Do You Mean by Collaboration Analytics? A Conceptual Model

The evidence of impact and ethical considerations of Multimodal Learning Analytics: A Systematic Literature Review

Parsing the Continuum: Manual Segmentation of Monologic Data

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