Legends of the dashboard: an empirical evaluation of split and joint layout designs for geovisual analytics interfaces

Gołębiowska, Izabela; Opach, Tomasz; Çöltekin, Arzu; Korycka-Skorupa, Jolanta; Rød, Jan Ketil

doi:10.1080/17538947.2023.2197262

Cited by 2 publications

(1 citation statement)

References 57 publications

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“…Existing research often utilizes feature data from the three dimensions of cognition, emotion, and behavior. For example, the pupil [9], eye gaze [10], and other features are analyzed to evaluate concentration from a cognitive perspective. Concentration is identified from the emotional dimension by extracting features such as facial expressions [11], text [12], and posture [13].…”

Section: Introductionmentioning

confidence: 99%

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Zhong

et al. 2023

Sustainability

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Learning concentration, as a crucial factor influencing learning outcomes, provides the basis for learners’ self-regulation and teachers’ instructional adjustments and intervention decisions. However, the current research on learning concentration recognition lacks the integration of cognitive, emotional, and behavioral features, and the integration of interaction and vision data for recognition requires further exploration. The way data are collected in a head-mounted display differs from that in a traditional classroom or online learning. Therefore, it is vital to explore a recognition method for learning concentration based on multi-modal features in VR environments. This study proposes a multi-modal feature integration-based learning concentration recognition method in VR environments. It combines interaction and vision data, including measurements of interactive tests, text, clickstream, pupil facial expressions, and eye gaze data, to measure learners’ concentration in VR environments in terms of cognitive, emotional, and behavioral representation. The experimental results demonstrate that the proposed method, which integrates interaction and vision data to comprehensively represent the cognitive, emotional, and behavioral dimensions of learning concentration, outperforms single-dimensional and single-type recognition results in terms of accuracy. Additionally, it was found that learners with higher concentration levels achieve better learning outcomes, and learners’ perceived sense of immersion is an important factor influencing their concentration.

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Section: Introductionmentioning

confidence: 99%

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Zhong

et al. 2023

Sustainability

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User Performance Modelling for Spatial Entities Comparison with Geodashboards: Using View Quality and Distractor as Concepts

Meißner,

Degbelo

2024

Companion of The16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems

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Legends of the dashboard: an empirical evaluation of split and joint layout designs for geovisual analytics interfaces

Cited by 2 publications

References 57 publications

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments

User Performance Modelling for Spatial Entities Comparison with Geodashboards: Using View Quality and Distractor as Concepts

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