Tracing physical movement during practice-based learning through multimodal learning analytics

Healion, Donal; Russell, Sam; Cukurova, Mutlu

doi:10.1145/3027385.3029474

Cited by 14 publications

(5 citation statements)

References 37 publications

(82 reference statements)

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“…e) Indicator type-spatial: This indicator type is a mix of the proximity indicator (i.e., the distance between the group members) [38], [79], [82], [89] and the positioning of the members (i.e., their mobility) [88], [89]. Some collaboration scenarios like medical simulations need the collaborating members to move around the room (or occupy particular positions) while performing the operation or other medical tasks.…”

Section: Indicator Types Sensors Referencesmentioning

confidence: 99%

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

Praharaj

Scheffel

Drachsler

et al. 2021

IEEE Trans. Learning Technol.

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Collaboration is one of the important 21st-century skills. It can take place in remote or co-located settings. Colocated collaboration (CC) is a very complex process that involves subtle human interactions that can be described with indicators like eye gaze, speaking time, pitch, and social skills from different modalities. With the advent of sensors, multimodal learning analytics has gained momentum to detect CC quality. Indicators (or low-level events) can be used to detect CC quality with the help of measurable markers (i.e., indexes composed of one or more indicators) which give the high-level collaboration process definition. However, this understanding is incomplete without considering the scenarios (such as problem solving or meetings) of CC. The scenario of CC affects the set of indicators considered: for instance, in collaborative programming, grabbing the mouse from the partner is an indicator of collaboration; whereas in collaborative meetings, eye gaze, and audio level are indicators of collaboration. This can be a result of the differing goals and fundamental parameters (such as group behavior, interaction, or composition) in each scenario. In this review, we present our work on profiles of indicators on the basis of a scenariodriven prioritization, the parameters in different CC scenarios are mapped onto the indicators and the available indexes. This defines the conceptual model to support the design of a CC quality detection and prediction system.

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Section: Indicator Types Sensors Referencesmentioning

confidence: 99%

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

Praharaj

Scheffel

Drachsler

et al. 2021

IEEE Trans. Learning Technol.

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“…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. Furthermore, a study examining the movement of students and teachers around furniture, using MMLA, provided a better understanding of collaborative learning processes (Healion, Russell, Cukurova, & Spikol, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Vujović¹,

Hernández‐Leo²,

Tassani³

2020

Brit J Educational Tech

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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.

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“…Collecting, processing, storing and analyzing data is different and more complex in an embedded systems context as compared to SaaS deployments [20]. By engaging in an action research study, and based on interventions such as interviews, workshops and active participation in the development team at the case company, we identified challenges encountered when developing the Data Factory system.…”

Section: Resultsmentioning

confidence: 99%

“…The Institute of Electrical and Electronics Engineers (IEEE) is studying and defining EC [20] as a new way to analyze and use IoT data. Instead of sending vast quantities of IoT data to the cloud for analysis and longer-term storage, this model involves:…”

Section: The Internet Of Things and Multi-tier Architecturementioning

confidence: 99%

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Towards designing a flexible multimodal learning analytics system

Ouhaichi¹

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The goal of learning analytics (LA) is to better understand and improve education. There are indications that LA would benefit from using multiple data sources instead of relying only on log data, which is usually collected when learning occurs in front of a computer screen. Learning is not always facilitated by a digital system that can capture digital traces. Therefore, learning analytics and learning science are increasingly focusing on multimodality, or the combination of multiple sensor data streams where each data type is a modality or mode. During the learning process, sensors can capture observable events, such as learners' behavior and interactions, as well as the learning context. However, to improve and evaluate the effectiveness of analytical systems, it is necessary to evaluate how research is conducted in the emerging area of multimodal learning analytics (MMLA). Multimodal sensory data consist of continuously evolving sets of sequences that are recorded over time. Such data are essential to MMLA applications, but it is difficult to prepare and process these datasets. Moreover, MMLA provides opportunities for understanding and supporting collaborative problem solving. Implementing MMLA systems during face-to-face work can be challenging.This thesis has three objectives: 1. To examine current research methodologies and assess current interest and research themes in MMLA, 2. To investigate the data management aspects of developing MMLA IoT-based systems, and 3. To develop an initial sample design for MMLA systems. We use three main approaches to achieve these objectives. First, we review existing literature and identify existing research methodologies and trending research themes in MMLA. Second, to analyze how multimodal sensor data can be managed, we use an AR case study to identify the main challenges in managing multimodal data.

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Tracing physical movement during practice-based learning through multimodal learning analytics

Cited by 14 publications

References 37 publications

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

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

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

Towards designing a flexible multimodal learning analytics system

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