Collaborative problem-solving

This study explores the manifestations, impacts, and broader implications of disparate disciplinary practices in U.S. public schools, with a primary focus on the differential treatment of black students compared to their white peers. While discriminatory disciplinary measures are well-documented, this research delves into how these disparities manifest, the systemic factors that sustain them, and the long-term consequences for affected students. The study examines the disproportionate rates of suspensions, expulsions, and referrals to law enforcement, which contribute to academic disengagement and the school-to-prison pipeline. Additionally, it highlights the role of implicit bias, institutional policies, and structural racism in perpetuating these disparities. The research also considers alternative disciplinary approaches, such as Positive Behavioral Interventions and Supports (PBIS) and restorative justice, as potential solutions for creating a more equitable educational environment. The findings underscore the urgent need for policy reforms that promote fairness, accountability, and inclusivity in school discipline practices to mitigate long-lasting harm to students and communities.

Collaborative problem solving (CPS) is a complex construct comprised of skills associated with social and cognitive dimensions. The diverse set of skills within these dimensions make CPS difficult to measure. Typically, research on measuring CPS has used highly constrained environments that help narrow the problem space. In the current study, we applied the in-task assessment framework to support the exploration of CPS skills at a deep level in an open digital environment in which three students worked together to solve an electronics problem. The construct of CPS was defined in depth prior to the implementation of the environment through the development of a complex, hierarchical ontology. The features from the ontology were identified in the data and four theoretically-grounded profiles of types of collaborative problemsolvers were produced-high social/high cognitive, high social/low cognitive, low social/high cognitive, and low social/low cognitive. Results showed that students in the low social/low cognitive profile group demonstrated poorer performance than students in other profile groups. Further, having at least one high social/high cognitive member in a team facilitated performance. This study offers groundwork for future studies in measuring CPS with an approach suitable for less constrained collaborative environments.

Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21 st century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers interacting with an online electronics task. The profiles were created based on 11 theoretically-grounded CPS skills defined a priori. The resulting four profiles correlated in expected directions with in-task performance and had interesting relationships with external measures associated with prior knowledge and CPS skills. These results inform and partially replicate findings from our previous research using a similar approach on a smaller dataset. Implications and comparisons between the two studies will be discussed.

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students' interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving (CPS). After interaction with the task, students' log files were tagged for the presence of 11 CPS skills from the ontology. The frequencies of the skills were clustered to identify four unique profiles of collaborative problem solvers-Chatty Doers, Social Loafers, Group Organizers, and Active Collaborators. Relationships among cluster membership, task performance, and external ratings of collaboration provide initial validity evidence that these are meaningful profiles of collaborative problem solvers.

Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agr...

In this paper, we describe a theoretically-grounded data mining approach to identify types of collaborative problem solvers based on students’ interactions with an online simulation-based task about electronics concepts. In our approach, we developed an ontology to identify the theoretically-grounded features of collaborative problem solving (CPS). After interaction with the task, students’ log files were tagged for the presence of 11 CPS skills from the ontology. The frequencies of the skills were clustered to identify four unique profiles of collaborative problem solvers – Chatty Doers, Social Loafers, Group Organizers, and Active Collaborators. Relationships among cluster membership, task performance, and external ratings of collaboration provide initial validity evidence that these are meaningful profiles of collaborative

Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21 century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers interacting with an online electronics task. The profiles were created based on 11 theoretically-grounded CPS skills defined a priori. The resulting four profiles correlated in expected directions with in-task performance and had interesting relationships with external measures associated with prior knowledge and CPS skills. These results inform and partially replicate findings from our previous research using a similar approach on a smaller dataset. Implications and comparisons between the two studies will be discussed.

Collaborative problem solving (CPS) is a complex construct comprised of skills associated with social and cognitive dimensions. The diverse set of skills within these dimensions make CPS difficult to measure. Typically, research on measuring CPS has used highly constrained environments that help narrow the problem space. In the current study, we applied the in-task assessment framework to support the exploration of CPS skills at a deep level in an open digital environment in which three students worked together to solve an electronics problem. The construct of CPS was defined in depth prior to the implementation of the environment through the development of a complex, hierarchical ontology. The features from the ontology were identified in the data and four theoretically-grounded profiles of types of collaborative problemsolvers were produced-high social/high cognitive, high social/low cognitive, low social/high cognitive, and low social/low cognitive. Results showed that students in the low social/low cognitive profile group demonstrated poorer performance than students in other profile groups. Further, having at least one high social/high cognitive member in a team facilitated performance. This study offers groundwork for future studies in measuring CPS with an approach suitable for less constrained collaborative environments.

Benefits of metacognition and active learning on student performance are well understood; however, little is known about the effect of the "explicit teaching of metacognition" combined with active learning on student performance in chemistry courses. Using a quasi-experimental study design, we investigated: (1) differences in performance in the General Chemistry I course between students exposed to the explicit teaching of metacognition combined with active learning (treatment group) and their counterparts who were exposed to active learning alone (comparison group) and (2) the effect of the teaching method, gender, race/ethnicity, first-generation status, and preterm overall GPA on student performance, after controlling for ACT math scores, data consisting of exam scores on three midterm exams, and a cumulative final exam. Independent t-test results indicated that the treatment group performed significantly better than the comparison group on the midterm exam 3 and the cumulative final exam (p < 0.05), with mean differences of 6% and 5%, respectively. Descriptive statistics also indicated 8% and 10% lower numbers of DF letter grades (DFs) in the exam 3 and the final exam, respectively, and an 8% lower number of withdrawals from the course (Ws) for the treatment group compared to those of the comparison group. Hierarchical regression results indicated that being in the treatment group increased the performances in exam 3 and the final exam by 6.11 and 4.20 points, respectively. The increase was statistically significant (p < 0.001). Results suggest that metacognitive instruction infused with active learning has a significant effect on student performance in General Chemistry, particularly on cognitively demanding chemistry concepts.

The current study explores students' collaboration and problem solving (CPS) abilities using a human-to-agent (H-A) computerbased collaborative problem solving assessment. Five CPS assessment units with 76 conversation-based items were constructed using the PISA 2015 CPS framework. In the experiment, 53,855 ninth and tenth graders in Taiwan were recruited, and a multidimensional item response analysis was used to develop CPS scales and represent the students' collaboration and problem solving performance. The results show that the developed H-A approach is feasible for measuring students' CPS skills, and the CPS scales are also shown to be reliable. In addition, the students' CPS performance scores are further explored and discussed under the PISA CPS framework.

Project Based Learning is a complex concept that is related to Prob- lem Based Learning and Collaborative Problem Solving. These latter concepts are well represented in the literature by models and frameworks that can useful- ly be adapted to develop a framework for the analysis of Project Based Learn- ing. We present such a framework that has been designed for learning situations that involve the use of technology. This technology can be used to capture data about learners&#39; interactions as well as to support their learning. We suggest that this data can be combined with data collated by human observers and analysed using the framework.