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This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Team cognition research continues to evolve as the need for understanding and improving complex problem solving itself grows. Complex problem solving requires members to engage in a number of complicated collaborative processes to generate solutions. This chapter illustrates how the Macrocognition in Teams model, developed to guide research on these processes, can be utilized to propose how intelligent tutoring systems (ITSs) could be developed to train collaborative problem solving. Metacognitive prompting, based upon macrocognitive processes, was offered as an intervention to scaffold learning these complex processes. Our objective is to provide a theoretically grounded approach for linking intelligent tutoring research and development with team cognition. In this way, team members are more likely to learn how to identify and integrate relevant knowledge, as well as plan, monitor, and reflect on their problem-solving performance as it evolves. We argue that ITSs that utilize metacognitive prompting that promotes team planning during the preparation stage, team knowledge building during the execution stage, and team reflexivity and team knowledge sharing interventions during the reflection stage can improve collaborative problem solving.

In a variety of domains, teams represent the main mechanism for dealing with change, complexity, and uncertainty in organizations. Consequently, teams need to be able to adapt and effectively use shared and complementary cognitive processing while collaborating to deal with these challenges.

A conceptual review is provided that addresses this type of complex collaborative cognition via discussion of macrocognition and the processes contributing to effective team problem-solving.

Despite extensive research on problem-solving, research and theories regarding how problem-solving changes over time as teams develop is missing. With this review, we extend research on team problem-solving and team development through integration of existing theory and concepts from the team literature.

This review provides a theoretical foundation for understanding and studying the developmental dynamic of team problem-solving.

A team problem-solving development model is described which outlines the degree to which the primary elements of team development are likely to affect macrocognitive processes within problem-solving phases. A set of propositions is offered in order to guide research on team development in collaborative problem-solving.

Increased interest in team dynamics has resulted in new methods for measuring teamwork over time. The primary purpose of this chapter is to provide a survey of recent developments in teamwork/collaboration measurement in an educational context. Key topics include conceptual frameworks, large-scale assessments, and innovative measurement techniques.

A range of methods for collecting and analyzing teamwork data are discussed, and five frameworks for measuring collaborative problem solving (CPS) over time are compared. Frameworks from Programme for International Student Assessment (PISA), Assessment and Teaching of 21st Century Skills (ATC21S) project, Educational Testing Service (ETS), ACT, and von Davier and Halpin (2013) are discussed. Results of assessments developed from these frameworks are also considered.

New techniques for measuring team dynamics over time have great potential to improve education and work outcomes. Preliminary results of the assessments developed from these frameworks show that important advances in teamwork measurement have been enabled by innovative task designs, data-mining techniques, and novel applications of stochastic models.

This novel overview and comparison of interdisciplinary approaches will help to indicate where progress has been made and what challenges are ahead.

The purpose of this research study is to explore simple collaborative technique for teaching mathematics and thus improving the problem solving skills of the students. Better pedagogic activities are required to motivate the students to perceive mathematics as a subject that stimulates problem-solving skills required for engineers.

This paper presents a research study on the application of technology-supported collaborative learning in improving the problem-solving skills of first-year engineering students in a mathematics course. The experiment was conducted in a mathematics course “Engineering Calculus” with 286 first-year engineering students in two groups: experimental group (N = 60) and controlled group (N = 226). The academic performance of the students was measured and analyzed with statistical techniques.

From the results obtained, it was found that the academic performance of the experimental group was better than the controlled group. Also, interest shown by the students in the topic that dealt with collaborative learning was more than in other topics taught using conventional teaching methods.

The teachers are required to find effective pedagogical activities to improve the problem-solving skills in mathematics. The research work proposes a collaborative method in mathematics for attaining higher cognitive level in an entry level engineering course. The limitation of this study lies in group formation techniques and the grading policy which deals only with individual assessment scores.

Practice of collaborative learning is made easy with simple technology. A clear strategy for the conduct of collaborative learning sessions has been presented. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

Using technology in mathematics teaching may not be very easy for all teachers. Especially, for an undergraduate engineering fresher, mathematics may not be a very easy task. This work shall bridge the gap with simple technology-assisted collaborative learning. The teachers need not spend too much time and effort in learning technology for mathematics teaching. The effect of this learning is significant in terms of the performance and satisfaction evaluation.

This work presents a systematic implementation of collaborative learning that shall result in improved problem-solving skills and engaging learning sessions. The statistical analysis methods and the visualization of obtained results shall help in understanding the implications of the presented work. Practice of collaborative learning is made easy with simple technology. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

The purpose of this study is to identify and assess collaborative problem solving (CPS) behaviors in elementary students in science, technology, engineering, arts/humanities and mathematics (STEAM)-related making and to garner students perspectives. We offer a valid way for researchers to understand collaborative processes and for educators to create opportunities for collaboration. Additionally, the feedback from the assessment offers students a way to reflect on their CPS skills.

This qualitative study evaluated 52 elementary students’ CPS skills using co-measure, a validated rubric assessing students’ CPS when working in STEAM-related makerspace activities. Students worked in collaborative groups to “make” artifacts when solving a problem posed by their teacher. They were assessed using co-measure’s four dimensions: peer interactions, positive communication, inquiry rich/multiple paths and transdisciplinary approaches and scored via each dimension’s associated attributes. Student interviews provided their perspectives on CPS.

A majority of students scored in the acceptable or proficient range in the social dimensions of peer interactions and positive communication. Students scored slightly lower on the cognitive dimensions of inquiry rich/multiple paths and markedly lower on transdisciplinary approaches when collaborating. Findings suggest to increase CPS skills, teachers might develop “making” activities fostering greater inquiry and model ways to strategize and verify information, approach the problem drawing on student interest and prior knowledge and collaboratively use tools, materials and methods that mimic the real world when problem-solving.

Much of the current research on assessing CPS during making is in the early stages of considering appropriate assessment approaches, especially in schools. To expand this literature the study includes elementary students between the ages of 6-10, the focus is on assessing their collaboration using an observational rubric. The authors use preliminary findings from young children’s perspectives on making to position the future work.

This paper aims to present a model of collaborative inquiry play: rule-based imaginary situations that provide challenging problems and support agentic multiplayer interactions (c.f., Vygotsky, 1967; Salen and Zimmerman, 2003). Drawing on problem-based learning (PBL, Hmelo-Silver, 2004), this paper provides a design case to articulate the relationship between the design goals and the game-based learning environment.

Drawing on conjecture mapping (Sandoval, 2014), this paper presents an iterative development of the conjecture map for crystal island: ecojourneys and highlights the development of the story and tools in crystal island: ecojourneys, an immersive game based on PBL pedagogy. By articulating this development, the authors highlight the affordances and constraints of designing for collaborative inquiry play and address challenges in supporting learner agency.

The PBL inquiry process served as the foundation of collaborative inquiry play. Attending to the rules of inquiry fostered student agency, and in turn, playful engagement in the game-based learning environment. Agency however meant holding students accountable to actions undertaken, especially as it pertained to generating group-based explanations and reflecting on productive collaboration. Moreover, socially shared regulation of learning and systems thinking concepts (i.e. phenomenon, mechanisms, and components) must also be externalized in representations and interactions in the game such that students have the agency to decide on their learning paths.

This paper presents the model of collaborative inquiry play and highlights how to support player agency and design content-rich play environments which are not always completely open.

The purpose of this paper is to verify how distributed cognition enhances collaborative problem-solving in the context of projects.

Using qualitative research and in-depth interviews, a sample of 32 project managers with experience in traditional and agile methods acting in Brazil and internationally participated in the research process. The analysis process, utilising coding techniques, involved stages: open, axial, coding and selective coding. These stages encompassed the evaluation of categories based on a hierarchy, in order to determine an appropriate level of abstraction that properly explains theoretical findings.

The results indicate that distributed team cognition is significant for collaborative problem-solving. The data from the interviews allowed the proposal of a model of cognition, and the identification of the elements that support it.

Understand how aspects of distributed team cognition can impact the behaviours of the project professional and contribute to problem-solving in the project environment.

The elements observed affects the collaborative problem-solving by presenting a model of distributed cognition, which is composed by directed communication, collective interaction, trust building and collaborative behaviour.

The purpose of this paper is to identify the core dimensions of problem solving of experts in commercial valuation in order to provide a rich stimulus for managing current practice and enabling future development.

The study adopted a cognitive position but emphasised understanding the everyday commercial property valuation practice in a naturalistic context and from the participants’ perspectives. Given this, a grounded theory approach was employed as a research strategy to guide the data collection and surface theoretical interpretations. Data were obtained through in-depth interviews with practicing valuers working in private real estate firms within metropolitan Birmingham, UK.

The interviews uncover four dimensions of experts’ problem-solving practice in commercial valuation: multidimensional, domain-specific knowledge base; cognitive process that is centred on analysis and reflection; collaborative problem-solving venture with colleagues; and professional practice issues awareness. A conceptual model is proposed which integrates these dimensions enabling a clearer understanding of the nature of valuation work.

This study was designed to be descriptive and theory generating, thus, the findings cannot be generalised as the sample was confined to one city and consists of a small number of senior practicing valuers. Therefore, the findings may not be fully applicable to other practicing valuers, other geographical locations or more widely to other types of property valuation. Nevertheless, the findings provide an important cognitive framework which can be verified by other researchers seeking to examine the practice of expert valuers.

The identification of the core dimensions of expert problem solving in commercial property valuation is shown to have implications for valuation practice, education and continued research. The valuation practice environments need to develop mechanisms to provide time that would enable these multi-dimensions of professional competence to be developed. Further work is needed to expand and refine the model across expert practice in other specialty areas of valuation practice.

This study expands the current understanding of valuation process to areas of expertise that have received less coverage in behavioural valuation literature, that is, the central role of knowledge and cognition and how these are integrated for effective valuation problem solving and decision making.

Domain ontologies facilitate sharing and re‐use of data and knowledge between distributed collaborating systems. A major problem in the design and application of intelligent systems is to capture and understand: the data and information model that describes the domain; the various levels of knowledge associated with problem solving; and the patterns of interaction, information and data flow in the problem solving space. This paper reports the development of an ontology for agent‐based collaborative design of portal structures, using knowledge acquisition techniques and tools. It illustrates the application of the ontology in the development of a prototype multi‐agent systems. The study shows that a common ontology facilitates interaction and negotiation between agents and other distributed systems. The paper discusses the findings from the knowledge acquisition, their implications in the design and implementation of multi‐agent systems, and gives recommendations on developing agent‐based systems for collaborative design and decision‐support in the construction sector.