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Using technological functions on a multi-touch table and their affordances to counteract biases and foster collaborative problem solving

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Abstract

Touch technologies have become ubiquitous, motivating researchers to explore their potential - especially in collaborative scenarios. Studies on collaboration using joint visual spaces like multi-touch tables have demonstrated positive effects on performance. Yet, factors like prior knowledge and preferences, resulting in cognitive biases, were neglected although they are likely to put additional demands on collaboration. Whether touch technology can support its users in mastering the resulting challenges remains an open issue. To address this issue, we employed a hidden-profile paradigm (e.g., Schulz-Hardt and Mojzisch 2012) to investigate whether the affordances of specific support functions realized in a collaboration support kit on a multi-touch table help to overcome established pitfalls of collaboration (prior preferences and discussion biases). The collaboration support kit comprised a joint space and private spaces. It allowed participants to push information from the private into the joint space, to jointly sort information within the joint space, and it provided automatic functions like merging information. To replicate traditional hidden-profile studies, triads in a standard hidden-profile condition (n = 25) exchanged information in a discussion; triads in the condition with collaboration support kit (n = 29) were additionally provided with the aforementioned functions. Our results revealed that groups with collaboration support kit available showed greater discussion intensity, more balanced discussions, more indicators of mutual understanding, and better decision performance than standard hidden-profile groups. This is original evidence that affordances of a multi-touch table with interactive support functions can be used to overcome biases from prior preferences and to enhance collaboration.

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Notes

  1. The Organization for Economic Cooperation and Development (OECD) recognized the need to understand collaboration skills by including CPS in their latest Program for International Student Assessment (PISA) in 2015 (OECD 2017). In PISA 2015, CPS was assessed by implementing a standardized computer supported human-agent interaction, where a student was to solve the curriculum-independent tasks in collaboration with one or more computer-simulated agents using pre-defined messages (OECD 2017). In general, at each step, the student could choose the most adequate messages out of two to seven alternatives. When a message was sent, the computerized agent replied accordingly and a new set of messages to choose from was presented to the student. This circle repeated itself until the student came to the solution. It is important to note that students received guidance from one of the agents if their choice was not conducive to reach the solution. Therefore, every student ended up solving the task, only the paths to the solution differed. Summing up, in order to measure collaborative skills, PISA developed a scripted, highly standardized paradigm, yielding a reliable assessment approach. Still, this procedure comes with issues of reduced external validity because this kind of collaboration with computer agents does not directly compare to collaboration with actual persons (Greiff et al. 2013).

  2. For the discussion bias measure according to Stasser, Vaughan, and Stewart (2000) the introduction/repetition rate for shared information was divided by the sum of the rate of introduced/repeated shared and unshared information. For this measure a value of .5 (range 0 to 1) indicates an unbiased discussion, larger values indicate a stronger bias towards shared information.

  3. When comparing the decision quality in the CSK condition to that of the standard HP condition after participants saw the merged information, adding the condition predictor (AIC: 70.83) to the intercept model (AIC: 69.27) did not improve goodness-of-fit, χ2(1) = 0.44, p = .507. That is, having been presented with the merged information, the groups in the standard HP condition were as likely to decide for the best candidate as groups in the CSK condition. Specifically, the chance to make the right decision was nearly identical (OR = 1.48) in this analysis.

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  1. Leibniz-Institut für Wissensmedien, Schleichstraße 6, 72076, Tübingen, Germany

    Inga M. Bause, Irina R. Brich & Friedrich W. Hesse

  2. University of Tuebingen, Tuebingen, Germany

    Ann-Katrin Wesslein

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Bause, I.M., Brich, I.R., Wesslein, AK. et al. Using technological functions on a multi-touch table and their affordances to counteract biases and foster collaborative problem solving. Intern. J. Comput.-Support. Collab. Learn 13, 7–33 (2018). https://doi.org/10.1007/s11412-018-9271-4

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