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In today’s digital society, computer-supported collaborative learning (CSCL) and collaborative problem solving (CPS) have received increasing attention. CPS studies have often emphasized outcomes such as skill levels of CPS, whereas the action transitions in the paths to solve the problems related to these outcomes have been scarcely studied. The patterns within action transitions are able to capture the mutual influence of actions conducted by pairs and demonstrate the productivity of students’ CPS. The purpose of the study presented in this paper is to examine Finnish sixth graders’ (N = 166) patterns of action transitions during CPS in a computer-based assessment environment in which the students worked in pairs. We also investigated the relation between patterns of action transitions and students’ social and cognitive skill levels related to CPS. The actions in the sequential processes of computer-based CPS tasks included using a mouse to drag objects and typing texts in chat windows. Applying social network analysis to the log file data generated from the assessment environment, we created transition networks using weighted directed networks (nodes for those actions conducted by paired students and directed links for the transitions between two actions when the first action is followed by the second action in sequence). To represent various patterns of action transitions in each transition network, we calculated the numbers of nodes (numbers of actions conducted), density (average frequency of transitions among actions), degree centralization (the dispersion of attempts given to different actions), reciprocity (the extent to which pairs revisit the previous one action immediately), and numbers of triadic patterns (numbers of different repeating formats within three actions). The results showed that pairs having at least one member with high social and high cognitive CPS skills conducted more actions and demonstrated a higher average frequency of action transitions with a higher tendency to conduct actions for different number of times, implying that they attempted more paths to solve the problem than the other pairs. This could be interpreted as the pairs having at least one student with high social and high cognitive CPS skills exhibiting more productive CPS than the other pairs. However, we did not find a significant difference across the pairs in terms of alternating sequences of two or three actions. Investigating the patterns of action transitions of the dyads in this study deepens our understanding of the mutual influence between the CPS actions occurring within dyads. Regarding pedagogical implication, our results offer empirical evidence recommending greater awareness of the students’ social and cognitive capacities in CPS when assigning them into pairs for computer-based CPS tasks. Further, this study contributes to the methodological development of process-oriented research in CSCL by integrating an analysis of action transition patterns with a skill-based assessment of CPS.

Collaborative problem-solving (CPS) involves the interaction and interdependence of students’ social and cognitive skills, making it a complex learning process. To delve into the complex dynamics of CPS, previous research has categorized socio-cognitive roles, providing insights into social-cognitive frameworks. However, despite the specific cognitive and social interaction structures employed by roles to engage in CPS interactions, most existing research primarily focuses on individual roles, neglecting inter-role interactions. To fill this gap, twelve triad groups were formed by engaging 36 undergraduate students in online CPS activities to examine differences in social and cognitive interaction structures across different roles and group compositions. Additionally, analyze the differences in CPS processes among various group compositions. The analyses identified five roles ( Lurkers, Followers, Drivers, Influential Actors , and Innovators ) and three group compositions ( Balanced groups, Decentralized groups , and Power Struggle groups ). The socio-cognitive structure of Balanced groups , along with other evidence, indicates effective information sharing and negotiation interactions. In contrast, Decentralized and Power Struggle groups exhibited various deficiencies in their socio-cognitive structures, negatively impacting group collaboration processes. These insights provide educators with a comprehensive guide to fostering effective group compositions and role dynamics in online CPS settings, thereby enhancing the overall success of CPS. Additionally, possible activity design considerations and scaffolding strategies are also discussed.

In the human-to-human Collaborative Problem Solving (CPS) test, students’ problem-solving process reflects the interdependency among partners. The high interdependency in CPS makes it very sensitive to group composition. For example, the group outcome might be driven by a highly competent group member, so it does not reflect all the individual performances, especially for a low-ability member. As a result, how to effectively assess individuals’ performances has become a challenging issue in educational measurement. This research aims to construct the measurement model to estimate an individual’s collaborative problem-solving ability and correct the impact of partners’ abilities. First, 175 eighth graders’ dyads were divided into six cooperative groups with different levels of problem-solving (PS) ability combinations (i.e., high-high, high-medium, high-low, medium-medium, medium–low, and low-low). Then, they participated in the test of three CPS tasks, and the log data of the dyads were recorded. We applied Multidimensional Item Response Theory (MIRT) measurement models to estimate an individual’s CPS ability and proposed a mean correction method to correct the impact of group composition on individual ability. Results show that (1) the multidimensional IRT model fits the data better than the multidimensional IRT model with the testlet effect; (2) the mean correction method significantly reduced the impact of group composition on obtained individual ability. This study not only successfully increased the validity of individuals’ CPS ability measurement but also provided useful guidelines in educational settings to enhance individuals’ CPS ability and promote an individualized learning environment.

Collaborative problem solving (CPS) enables student groups to complete learning tasks, construct knowledge, and solve problems. Previous research has argued the importance of examining the complexity of CPS, including its multimodality, dynamics, and synergy from the complex adaptive systems perspective. However, there is limited empirical research examining the adaptive and temporal characteristics of CPS, which may have led to an oversimplified representation of the real complexity of the CPS process. To expand our understanding of the nature of CPS in online interaction settings, the present research collected multimodal process and performance data (i.e., speech, computer screen recordings, concept map data) and proposed a three-layered analytical framework that integrated AI algorithms with learning analytics to analyze the regularity of groups’ collaboration patterns. The results surfaced three types of collaborative patterns in groups, namely the behaviour-oriented collaborative pattern (Type 1) associated with medium-level performance, the communication-behaviour-synergistic collaborative pattern (Type 2) associated with high-level performance, and the communication-oriented collaborative pattern (Type 3) associated with low-level performance. This research further highlighted the multimodal, dynamic, and synergistic characteristics of groups’ collaborative patterns to explain the emergence of an adaptive, self-organizing system during the CPS process. According to the empirical research results, theoretical, pedagogical, and analytical implications were discussed to guide the future research and practice of CPS.

Collaborative problem solving (CPS) has been receiving increasing international attention because much of the complex work in the modern world is performed by teams. However, systematic education and training on CPS is lacking for those entering and participating in the workforce. In 2015, the Programme for International Student Assessment (PISA), a global test of educational progress, documented the low levels of proficiency in CPS. This result not only underscores a significant societal need but also presents an important opportunity for psychological scientists to develop, adopt, and implement theory and empirical research on CPS and to work with educators and policy experts to improve training in CPS. This article offers some directions for psychological science to participate in the growing attention to CPS throughout the world. First, it identifies the existing theoretical frameworks and empirical research that focus on CPS. Second, it provides examples of how recent technologies can automate analyses of CPS processes and assessments so that substantially larger data sets can be analyzed and so students can receive immediate feedback on their CPS performance. Third, it identifies some challenges, debates, and uncertainties in creating an infrastructure for research, education, and training in CPS. CPS education and assessment are expected to improve when supported by larger data sets and theoretical frameworks that are informed by psychological science. This will require interdisciplinary efforts that include expertise in psychological science, education, assessment, intelligent digital technologies, and policy.

Productive Failure (PF) is an instructional design that implements a problem-solving phase which aims at preparing students for learning from a subsequent instruction. PF has been shown to facilitate students’ conceptual knowledge acquisition in the mathematical domain. Collaboration has been described as a vital design component of PF, but studies that have investigated the role of collaboration in PF empirically so far, were not able to confirm the necessity of collaboration in PF. However, these studies have diverged significantly from prior traditional PF studies and design criteria. Therefore, the role of collaboration in PF remains unclear. In an experimental study that is based on the traditional design of PF, we compared a collaborative and an individual problem-solving setting. It was hypothesized that collaboration facilitates the beneficial preparatory mechanisms of the PF problem-solving phase: prior knowledge activation, awareness of knowledge gaps, and recognition of deep features. In a mediation analysis, the effects of collaborative and individual problem solving on conceptual knowledge acquisition as mediated through the preparatory mechanisms were tested. In contrast to the hypotheses, no mediations or differences between conditions were found. Thus, collaboration does not hold a major preparatory function in itself for the design of PF.

Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

Since life on the planet, water has been claiming as a lifeline for human beings. The efficient use of water retains productive agriculture, environmental sustainability, poverty reduction, and economic growth. Manageable irrigation in Pakistan is considered as lowest amongst the world yet. Incredibly, the quality of groundwater is not merely declining by overexploitation but also getting worse. This study conducted in District Sargodha, Punjab-Pakistan, had the purpose of analyzing the impact of three farmers Learning Models such as the Value Management Model (VM), Collaborative Problem-Solving Workshops (CPSW) model, and Discovery Learning Model (DL). Particularly in materials and methods, a well-structured survey instrument was prepared for data collection. Both validity and reliability of the instrument were ensured before conducting the full-length study. Census study was designed because of not having a substantial trained population. A cross-sectional survey design was adopted for data collection, and data were collected from 92 trained farmers. In the analysis, both descriptive and inferential statistics were applied to analyse the data in SPSS. In descriptive, mean, standard deviation, rank, frequency, and percentage were computed. In Inferential, a paired t-test was computed to analyse the impact of training in enhancing small farmers’ income. The regression model was also computed to predict the variables such as farmers’ knowledge level before receiving training regarding Learning Models, Methods of Irrigation, Water Saving Technique, and different salt/nutrient and water measurement tools. RESUMO: Desde que existe vida no planeta, a água tem sido considerada uma tábua de salvação para os seres humanos. O uso eficiente da água mantém a agricultura produtiva, a sustentabilidade ambiental, a redução da pobreza e o crescimento econômico. A irrigação administrável no Paquistão é considerada a mais baixa do mundo até o momento. Incrivelmente, a qualidade das águas subterrâneas não está apenas declinando devido à superexploração, mas também piorando. Este estudo, conduzido no distrito de Sargodha, Punjab-Paquistão, teve como objetivo analisar o impacto de três modelos de aprendizagem para agricultores, como o Modelo de Gestão de Valor (VM), o modelo de Oficinas Colaborativas de Resolução de Problemas (CPSW) e o Modelo de Aprendizagem por Descoberta (DL). Particularmente em relação aos materiais e métodos, um instrumento de pesquisa bem estruturado foi preparado para a coleta de dados. Tanto a validade quanto a confiabilidade do instrumento foram garantidas antes da realização do estudo completo. O estudo censitário foi concebido devido à falta de uma população treinada substancial. Um delineamento de pesquisa transversal foi adotado para a coleta de dados, e os dados foram coletados de 92 agricultores treinados. Na análise, estatísticas descritivas e inferenciais foram aplicadas para analisar os dados no SPSS. Na análise descritiva, foram calculados média, desvio-padrão, classificação, frequência e porcentagem. Na análise inferencial, foi calculado um teste t pareado para analisar o impacto do treinamento no aumento da renda de pequenos agricultores. O modelo de regressão também foi calculado para prever variáveis como o nível de conhecimento dos agricultores antes do treinamento sobre Modelos de Aprendizagem, Métodos de Irrigação, Técnicas de Economia de Água e diferentes ferramentas de medição de sal/nutrientes e água.

We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users’ speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (n = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills.

The Organisation for Economic Co-operation and Development (OECD) asserts that Collaborative Problem-Solving (CPS) is an essential skill for students, also assessed in the Programme for International Student Assessment (PISA). Yet, research on how factors influence student discussion participation in the Mathematical Collaborative Problem-Solving Process remains limited. This paper integrates the Theory of Planned Behavior (TPB) and Self-Determination Theory (SDT) to explore factors that are related to student discussion participation during mathematical collaborative problem solving. The study collected data from 406 respondents from Guangxi, China, which has implemented collaborative problem-solving processes in recent years. The data were analyzed using Structural Equation Modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approaches. The SEM results indicate that 11 out of 13 factors significantly influence discussion participation in discussions during mathematical collaborative problem solving. The fsQCA identified three solutions consisting of various combinations of elements that can lead to high student discussion participation in CPS. These fsQCA results complement the SEM findings by offering an asymmetric perspective. Together, these findings offer practical implications for mathematics teachers and school leaders. Specifically, they underscore the importance of cultivating positive attitudes, supporting students’ sense of autonomy and competence, and creating classroom environments that encourage peer interaction and collective problem-solving. By applying these insights, educators can design more effective CPS activities that actively engage students and improve learning outcomes.