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Arthur C. Graesser, Nia Dowell, Andrew J. Hampton, Anne M. Lippert, Haiying Li and David Williamson Shaffer
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…
Abstract
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.
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Olivia B. Newton, Travis J. Wiltshire and Stephen M. Fiore
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…
Abstract
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.
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Dennis M. McInerney and Ronnel B. King
The aims of this study were (1) to examine the relationships among achievement goals, self-concept, learning strategies and self-regulation for post-secondary Indigenous…
Abstract
Purpose
The aims of this study were (1) to examine the relationships among achievement goals, self-concept, learning strategies and self-regulation for post-secondary Indigenous Australian and Native American students and (2) to investigate whether the relationships among these key variables were similar or different for the two groups.
Methodology
Students from the two Indigenous groups answered questionnaires assessing the relevant variables. Structural equation modelling (SEM) was used to analyse the data. Structure-oriented analysis was used to compare the two groups in terms of the strengths of the pathways, while level-oriented analysis was used to compare mean level differences.
Findings
Self-concept was found to positively predict deep learning and self-regulated learning, and these effects were mediated by achievement goals. Students who pursued mastery and social goals had more positive educational outcomes. Both structure and level-oriented differences were found.
Research implications
Drawing on two distinct research traditions – self-concept and achievement goals – this study explored the synergies between these two perspectives and showed how the key constructs drawn from each framework were associated with successful learning.
Practical implications
To improve learning outcomes, interventions may need to target students’ self-concept, mastery-oriented and socially oriented motivations.
Social implications
Supporting Indigenous students in their post-secondary education is an imperative. Psychologists have important insights to offer that can help achieve this noble aim.
Originality/value of the chapter
Research on Indigenous students has mostly adopted a deficiency model. In contrast, this study takes an explicitly positive perspective on Indigenous student success by focusing on the active psychological ingredients that facilitate successful learning.
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