Computational Thinking and Participatory Teaching as Pathways to Personalized Learning

This chapter discusses personalized learning by briefly outlining historical trends and deficiencies associated with what can be referred to as production style or assembly line approaches to education before contrasting personalized learning definitions. The chapter extends those definitions. It discusses participatory teaching as a personalized learning strategy by which students take on roles of co-teaching, co-designing lessons, or co-designing curriculum with adult teachers. One participatory teaching example involves an international group of students who help one another learn science and mathematics through shared video production. This example involves a US school involved in a larger districtwide effort comprehensively designed to involve each student. Organized around computational thinking, multidisciplinary innovation, arts integration, and collaborative problem-solving, the district may be viewed as a case study in implementing personalized learning. The chapter furnishes several examples that blend participatory teaching and computational thinking.

Trajectories of Idea Emergence in Dialogic Collaborative Problem Solving: Toward a Complex Dynamic Systems Perspective

According to the complex dynamic systems (CDS) perspective, learning emerges at various system levels. This study built a coherent theoretical framework based on CDS and Bakhtinian dialogic theory and further employed the concept of attractor (i.e., certain stable states that recur over time) in CDS theory to investigate the trajectories of idea emergence and how they diversified group outcomes in dialogic collaborative problem solving (D-CPS). Two contrasting groups were compared using visual and qualitative analysis approaches. The analysis based on idea tree diagrams showed that new ideas emergent in group discussion tended to attract local utterances and performed features of attractors in CDS in both high-performing and low-performing groups. The analysis based on idea hierarchy diagrams revealed how ideas emerged at various system levels. It was also found that status problems were likely to affect the functioning of regulative feedback loops, which might give rise to different structures of idea evolution. This study proposed CDS theory as an alternative perspective, augmented by the ethical considerations of Bakhtinian dialogism, for examining the dynamics of D-CPS.

Collaborative programming problem-solving in augmented reality: Multimodal analysis of effectiveness and group collaboration

Although numerous studies have demonstrated different ways that augmented reality (AR) can assist students to understand the learning content via contextualised visualisation, less explored is its effect on collaborative problem-solving (CPS) in computer programming. This study aims to investigate how AR affects a CPS in a programming task. We designed a mobile app that could visualise computer programming in AR and non-AR 3D images. The app could involve two participants working together on a programming problem face to face in the same workspace. We conducted a within-subjects experiment to compare their AR experience to the non-AR experience and collected multimodal usage data about the task performance, verbal communication, and user experience. The analysis showed that the participants in the AR experience had higher task performance and more insightful communication than the non-AR. The participants also had positive attitudes toward the use of AR in classroom instructions. In a semi-structured interview, the participants reflected that AR helped them engage in the content and analyse the task easier. Based on this study, we discuss several challenges and implications for future instruction designers. Implications for practice or policy: AR can improve student engagement in a collaborative problem-solving task. AR has the potential to promote and improve group communication in collaborative work. Instruction designers may need to carefully align the characteristics of AR with the task content especially when physical models are rarely used in the learning content.

“Anything taking shape?” Capturing various layers of small group collaborative problem solving in an experiential geometry course in initial teacher education

Collaborative problem solving (CPS) is widely recognized as a prominent 21st-century skill to be mastered. Until recently, research on CPS has often focused on problem solution by the individual; the interest in investigating how the theorized problem-solving constructs function as broader social units, such as pairs or small groups, is relatively recent. Capturing the complexity of CPS processes in group-level interaction is challenging. Therefore, a method of analysis capturing various layers of CPS was developed that aimed for a deeper understanding of CPS as a small-group enactment. In the study, small groups of teacher education students worked on two variations of open-ended CPS tasks—a technology-enhanced task and a task using physical objects. The method, relying on video data, encompassed triangulation of analysis methods and combined the following: (a) directed content analysis of the actualized CPS in groups, (b) process analysis and visualizations, and (c) qualitative cases. Content analysis did not show a large variation in how CPS was actualized in the groups or tasks for either case, whereas process analysis revealed both group- and task-related differences in accordance with the interchange of CPS elements. The qualitative cases exemplified the interaction diversity in the quality of coordination and students’ equal participation in groups. It was concluded that combining different methods gives access to various layers of CPS; moreover, it can contribute to a deeper articulation of the CPS as a group-level construct, providing divergent ways to understand CPS in this context.

Task dynamics define the contextual emergence of human corralling behaviors

Social animals have the remarkable ability to organize into collectives to achieve goals unobtainable to individual members. Equally striking is the observation that despite differences in perceptual-motor capabilities, different animals often exhibit qualitatively similar collective states of organization and coordination. Such qualitative similarities can be seen in corralling behaviors involving the encirclement of prey that are observed, for example, during collaborative hunting amongst several apex predator species living in disparate environments. Similar encirclement behaviors are also displayed by human participants in a collaborative problem-solving task involving the herding and containment of evasive artificial agents. Inspired by the functional similarities in this behavior across humans and non-human systems, this paper investigated whether the containment strategies displayed by humans emerge as a function of the task’s underlying dynamics, which shape patterns of goal-directed corralling more generally. This hypothesis was tested by comparing the strategies naïve human dyads adopt during the containment of a set of evasive artificial agents across two disparate task contexts. Despite the different movement types (manual manipulation or locomotion) required in the different task contexts, the behaviors that humans display can be predicted as emergent properties of the same underlying task-dynamic model.