Recently, there has been a surge in developing curricula and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) programs. These environments foster authentic problem-solving while facilitating students' concurrent learning of STEM+C content. In our study, we analyzed students' behaviors as they worked in pairs to create computational kinematics models of object motion. We derived a domain-specific metric from students' collaborative dialogue that measured how they integrated science and computing concepts into their problem-solving tasks. Additionally, we computed social metrics such as equity and turn-taking based on the students' dialogue. We identified and characterized students' planning, enacting, monitoring, and reflecting behaviors as they worked together on their model construction tasks. This study investigates the impact of students' collaborative behaviors on their performance in STEM+C computational modeling tasks. By analyzing the relationships between group synergy, turn-taking, and equity measures with task performance, we provide insights into how these collaborative behaviors influence students' ability to construct accurate models. Our findings underscore the importance of synergistic discourse for overall task success, particularly during the enactment, monitoring, and reflection phases. Conversely, variations in equity and turn-taking have a minimal impact on segment-level task performance. "Educational relevance and implications statement": The complexities of collaborative problem-solving for computational modeling in science provide a unique opportunity to explore individual and group learning. Specifically, in this manuscript, we examined differences in collaborative problem-solving behaviors, characterized by social and domain-specific metrics, and their impact on groups' ability to complete computational modeling tasks in kinematics. We identified the impact of interactivity metrics, such as equity and turn-taking, and the interweaving of science and computing concepts during collaborative discourse, on groups' performance. Finally, we analyzed differences between groups' planning, enacting, monitoring, and reflecting behaviors through interactivity metrics and students' segment-level performance. Our findings highlight key differences in students' problem-solving behaviors that will have implications in future work targeting adaptive support for problem-solving tasks.