Team Interaction Dynamics During Collaborative Problem Solving

This dissertation contributes an enhanced understanding of team cognition, in general, and collaborative problem solving (CPS), specifically, through an integration of methods that measure team interaction dynamics and knowledge building as it occurs during a complex CPS task. The need for better understanding CPS has risen in prominence as many organizations have increasingly worked to address complex problems requiring the combination of diverse sets of individual expertise to achieve solutions for novel problems. Towards this end, the present research drew from theoretical and empirical work on Macrocognition in Teams that describes the knowledge coordination arising from team communications during CPS. It built from this by incorporating the study of team interaction during complex collaborative cognition. Interaction between team members in such contexts has proven to be inherently dynamic and exhibiting nonlinear patterns not accounted for by extant research methods. To redress this gap, the present research drew from work in cognitive science designed to study social and team interaction as a nonlinear dynamical system. CPS was examined by studying knowledge building and interaction processes of 43 dyads working on NASA\u27s Moonbase Alpha simulation, a CPS task. Both non-verbal and verbal interaction dynamics were examined. Specifically, frame-differencing, an automated video analysis technique, was used to capture the bodily movements of participants and content coding was applied to the teams\u27 communications to characterize their CPS processes. A combination of linear (i.e., multiple regression, t-test, and time-lagged cross-correlation analysis), as well as nonlinear analytic techniques (i.e., recurrence quantification analysis; RQA) were applied. In terms of the predicted interaction dynamics, it was hypothesized that teams would exhibit synchronization in their bodily movements and complementarity in their communications and further, that teams more strongly exhibiting these forms of coordination will produce better problem solving outcomes. Results showed that teams did exhibit a pattern of bodily movements that could be characterized as synchronized, but higher synchronization was not systematically related to performance. Further, results showed that teams did exhibit communicative interaction that was complementary, but this was not predictive of better problem solving performance. Several exploratory research questions were proposed as a way of refining the application of these techniques to the investigation of CPS. Results showed that semantic code-based communications time-series and %REC and ENTROPY recurrence-based measures were most sensitive to differences in performance. Overall, this dissertation adds to the scientific body of knowledge by advancing theory and empirical knowledge on the forms of verbal and non-verbal team interaction during CPS, but future work remains to be conducted to identify the relationship between interaction dynamics and CPS performance

Exploration of Sensemaking in the Education of Novices to the Complex Cognitive Work Domain of Air Traffic Control

Many current complex business and industry jobs consist primarily of cognitive work; however, current approaches to training may be inadequate for this type of work (Hoffman, Feltovich, Fiore, Klein, & Ziebell, 2009). To try and improve training and education for cognitive work, Klein and Baxter (2006) have proposed cognitive transformation theory (CTT), a learning theory that claims that sensemaking activities are essential for acquiring expertise that is adaptive and thus well suited for cognitive work domains. In the present research, cognitive task analysis methods were used to identify and assess sensemaking support in the instruction and learning of complex concepts by two experienced air traffic control professors and seven of their students. The goal of this research was to compare instructional strategies used in an academic setting with the predictions of CTT to gain insight into strategies for the application of CTT. Cognitive task analysis methods employed included course observation, artifact examination, and knowledge elicitation sessions with two professors and seven of their students. Knowledge elicitation transcriptions were coded using categories derived from CTT and the data/frame theory of sensemaking (e.g. Klein, Moon, & Hoffman, 2006; Sieck, Klein, Peluso, Smith, & Harris-Thompson, 2007) to assess theoretical and applied implications for learning and instruction in a complex domain. Findings are represented by synthesizing theory driven predictions with grounded training strategies and technologies. In addition, recommendations are advanced for applying CTT to training and educational systems in order to provide sensemaking support during early phases of learning from which expertise may be developed

Team Interaction Dynamics during Collaborative Problem Solving

The need for better understanding collaborative problem solving (CPS) is rising in prominence as many organizations are increasingly addressing complex problems requiring the combination of diverse sets of individual expertise to address novel situations. This research draws from theoretical and empirical work that describes the knowledge coordination arising from team communications during CPS and builds from this by incorporating methods to study interaction dynamics. Interaction between team members in such contexts is inherently dynamic and exhibits nonlinear patterns not accounted for by extant research methods. To redress this gap, the present study draws from methods designed to study social and team interaction as a nonlinear dynamical system. CPS was examined by studying knowledge building and interaction processes of 43 dyads working to solve NASA’s Moonbase Alpha simulation. Specifically, frame-differencing, an automated video analysis technique, was used to capture the bodily movements of participants and content coding was applied to the teams’ communications to characterize their CPS processes. A combination of linear and nonlinear analytic and modeling techniques were applied to quantify and predict CPS performance based on the observed interaction dynamics and other individual differences. We hypothesized that teams exhibiting synchronization in their bodily movements and complementarity in their communications would produce better problem solving outcomes. The present research advances theory and empirical knowledge on effective team interaction during CPS and provides practical guidance on methods that can be used to observe and quantify interaction dynamics during CPS in complex work domains

Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model

This study discusses the effect of semi-supervised learning in combination with pretrained language models for data-to-text generation. It is not known whether semi-supervised learning is still helpful when a large-scale language model is also supplemented. This study aims to answer this question by comparing a data-to-text system only supplemented with a language model, to two data-to-text systems that are additionally enriched by a data augmentation or a pseudo-labeling semi-supervised learning approach. Results show that semi-supervised learning results in higher scores on diversity metrics. In terms of output quality, extending the training set of a data-to-text system with a language model using the pseudo-labeling approach did increase text quality scores, but the data augmentation approach yielded similar scores to the system without training set extension. These results indicate that semi-supervised learning approaches can bolster output quality and diversity, even when a language model is also present.Comment: 22 pages (excluding bibliography and appendix

LoCoMoTe – a framework for classification of natural locomotion in VR by task, technique and modality

Virtual reality (VR) research has provided overviews of locomotion techniques, how they work, their strengths and overall user experience. Considerable research has investigated new methodologies, particularly machine learning to develop redirection algorithms. To best support the development of redirection algorithms through machine learning, we must understand how best to replicate human navigation and behaviour in VR, which can be supported by the accumulation of results produced through live-user experiments. However, it can be difficult to identify, select and compare relevant research without a pre-existing framework in an ever-growing research field. Therefore, this work aimed to facilitate the ongoing structuring and comparison of the VR-based natural walking literature by providing a standardised framework for researchers to utilise. We applied thematic analysis to study methodology descriptions from 140 VR-based papers that contained live-user experiments. From this analysis, we developed the LoCoMoTe framework with three themes: navigational decisions, technique implementation, and modalities. The LoCoMoTe framework provides a standardised approach to structuring and comparing experimental conditions. The framework should be continually updated to categorise and systematise knowledge and aid in identifying research gaps and discussions

Challenges for using coordination-based measures to augment collaborative social interactions

Relationships are pervasive in human life, both personal and professional, and they are formed, maintained, and strengthened or weakened through human interactions. However, human interactions can be complex and multi-scale, meaning they occur at different time scales (e.g., seconds to lifetimes), across multiple modalities (e.g., movements and speech), and at different levels of organization (e.g., genetic, neural, behavioral; Abney et al. 2014; Dumas et al. 2014). Effective interactions with others seem to be facilitated in contexts such as conversations, teamwork, romantic partnerships, and psychotherapy by the degree to which we engage in multi-modal coordination with others (Dale et al. 2013; Gorman et al. 2016; Imel et al. 2014; Louwerse et al. 2012; Timmons et al. 2015)

Investigating coregulation of emotional arousal during exposure-based CBT using vocal encoding and actor–partner interdependence models

Objective. High patient emotional arousal during rationale development for in-vivo exposure in CBT for panic disorder with agoraphobia might endanger comprehension of the exposure rationale. Since therapists are supposed to coregulate patients’ emotions, this study investigated whether there was evidence of coregulation of vocally encoded emotional arousal, assessed by fundamental frequency (f0), during rationale development. Further, the association of patient f0 stability and therapist coregulation with patients’ perceived rationale plausibility was analyzed. Method. N = 197 therapy videos - used to deduct f0 - from a multicenter randomized controlled trial evaluating therapist-guided exposure on CBT outcome were analyzed posthoc. Plausibility of the exposure rationale was assessed by patients after its development. This trial-specific rating aggregates plausibility ratings for every manual component in the development of the exposure rationale and showed good internal consistency (Cronbach’s α = 0.85). Stability in f0 and its coregulation were calculated using Cross-lagged Actor-Partner Interdependence Models (APIM) and APIM dyad estimates were associated with plausibility using linear regression analyses. Results. Analyses indicated a relative stability in emotional arousal within both patients and therapists. Therapists’ f0 had a significant effect on patients in that with therapist covariation, patients’ f0 departed from their equilibrium level, while patients’ f0 had no effect on therapists. Therapists’ f0 covariation was positively associated with rationale plausibility. Conclusions. This study sheds light on interpersonal regulation mechanisms of patients’ and therapists’ emotional arousal during development of the exposure rationale. It suggests that coregulation of patients’ emotional arousal supports patients’ perceived rationale plausibility