Large high resolution displays for co-located collaborative sensemaking: Display usage and territoriality

Bradel, Lauren; Endert, Alex; Koch, Kristen; Andrews, Christopher; North, Chris

doi:10.1016/j.ijhcs.2013.07.004

Cited by 38 publications

(27 citation statements)

References 28 publications

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“…In particular, individual pickand-drops are twice as fast with CloseTech than with Di-vide&Conquer. But this advantage is offset by the fact that the 16 pick-and-drops have to be performed sequentially vs. 3 Although CloseComm is slower than the other styles for Distant layouts (p's < 0.001), it is 1.3 times faster than twice the TCT of Divide&Conquer which, according to our pilot studies, is a close approximation of the time it would take a single user to perform the task. Thus, help from the partner did improve performance.…”

Section: Individual Pick-and-dropsmentioning

confidence: 86%

“…http://dx.doi.org/10.1145/2858036.2858039 required users to manipulate data, not just observe or navigate it. Indeed, in many cognitive tasks such as sense-making [3] or problem-solving [11], users need to reorganize data items to solve the task. However, little research has studied the effects of shared interaction on co-located collaborative work that involves data manipulation in such settings.…”

Section: Introductionmentioning

confidence: 99%

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Shared Interaction on a Wall-Sized Display in a Data Manipulation Task

Liu

Chapuis

Beaudouin-Lafon

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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International audienceWall-sized displays support small groups of users working together on large amounts of data. Observational studies of such settings have shown that users adopt a range of collaboration styles, from loosely to closely coupled. Shared interaction techniques, in which multiple users perform a command collaboratively, have also been introduced to support co-located collaborative work. In this paper, we operational-ize five collaborative situations with increasing levels of coupling , and test the effects of providing shared interaction support for a data manipulation task in each situation. The results show the benefits of shared interaction for close collaboration: it encourages collaborative manipulation, it is more efficient and preferred by users, and it reduces physical navigation and fatigue. We also identify the time costs caused by disruption and communication in loose collaboration and analyze the trade-offs between parallelization and close collaboration. These findings inform the design of shared interaction techniques to support collaboration on wall-sized displays

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Section: Individual Pick-and-dropsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Shared Interaction on a Wall-Sized Display in a Data Manipulation Task

Liu

Chapuis

Beaudouin-Lafon

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

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“…Co-located collaboration has been shown to be beneficial for complex visual analytics tasks [39][40][41][42][43]. In an exploratory study of supporting group collaboration around a shared tabletop display [42], researchers found that task success was highly correlated with time spent working collaboratively.…”

Section: Co-located Collaborative Visual Analyticsmentioning

confidence: 99%

Be the Data: Embodied Visual Analytics

Chen¹,

Self

House

et al. 2018

IEEE Trans. Learning Technol.

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(ABSTRACT)With the rise of big data, it is becoming increasingly important to educate students about data analytics. In particular, students without a strong mathematical background usually have an unenthusiastic attitude towards high-dimensional data and find it challenging to understand relevant complex analytical methods, such as dimension reduction. In this thesis, we present an embodied approach for visual analytics designed to teach students exploring alternative 2D projections of high dimensional data points using weighted multidimensional scaling. We proposed a novel concept, Be the Data, and its application to explore the possibilities of using human's embodied resources to learn from high dimensional data. In our system, each student embodies a data point and the position of students in a physical space represents a 2D projection of the high-dimensional data.Students physically moves in a room with respect to others to interact with alternative projections and receive visual feedback. We conducted educational workshops with students inexperienced in relevant data analytical methods. Our findings indicate that the students were able to learn about high-dimensional data and data analysis process despite their low level of knowledge about the complex analytical methods. Similarly, we applied Be the Data to social meetings. We used the same techniques to analyze and display social-cluster related information to facilitate social interactions in real time. Participants information 323.2 A quantitative summary of students' understanding about the key concepts (i.e., variable, relative distance, dimension reduction, data exploration), interests, and confidence towards learning high-dimensional data before and after the workshop. DR stands for dimension reduction. Column 3 and 4 are observed proportion of correct answers for the pre and post surveys. Column 5 and 6 are the expected difference and the credible interval for the difference in proportions. Column 7 are the p-values from a two-tailed two sample t-test. The * in column 6 and 7 flags questions when there is important difference in pre and post. 373.3 A summary of students' embodied interaction during the activity 48 4.1 A portion of the high-dimensional dataset that describes participants in one social meeting.

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“…For example, large displays in working spaces [10]; large displays in public sites [24]; and large displays in collaborative work [6].…”

Section: Introductionmentioning

confidence: 99%

Promoted Motion-Sensing Gestural Interaction by Adapting to Group Users' Spatial Movements

Lou¹,

Li²,

Peng³

et al. 2017

Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

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Large displays are weak in providing sufficient access efficiency and input precision in gestural interaction. In particular, in multi-user situations when equal access efficiency and high precision are required, the weak of large displays becomes more obstructive. In order to address access efficiency and precision issues in both single-and multi-user situations, a walk-to-point technique is proposed. To evaluate the effectiveness, the new technique is compared to the traditional laser pointing method in terms of efficiency, precision and usability. The results showed that the new pointing technique outperformed the traditional laser pointing in both efficiency and precision, the smaller target size the more obvious superiority of the new technique. The new technique is also verified to be effective in providing equal access efficiency and precision to group users. Furthermore, the new technique achieves good result in usability evaluation.

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Large high resolution displays for co-located collaborative sensemaking: Display usage and territoriality

Cited by 38 publications

References 28 publications

Shared Interaction on a Wall-Sized Display in a Data Manipulation Task

Shared Interaction on a Wall-Sized Display in a Data Manipulation Task

Be the Data: Embodied Visual Analytics

Promoted Motion-Sensing Gestural Interaction by Adapting to Group Users' Spatial Movements

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