Process data are becoming more and more popular in education research. In the field of computer-based assessments of collaborative problem solving (ColPS), process data have been used to identify students' test-taking strategies while working on the assessment, and such data can be used to complement data collected on accuracy and overall performance. Such information can be used to understand, for example, whether students are able to use a range of styles and strategies to solve different problems, given evidence that such cognitive flexibility may be important in labor markets and societies. In addition, process information might help researchers better identify the determinants of poor performance and interventions that can help students succeed. However, this line of research, particularly research that uses these data to profile students, is still in its infancy and has mostly been centered on small- to medium-scale collaboration settings between people (i.e., the human-to-human approach). There are only a few studies involving large-scale assessments of ColPS between a respondent and computer agents (i.e., the human-to-agent approach), where problem spaces are more standardized and fewer biases and confounds exist. In this study, we investigated students' ColPS behavioral patterns using latent profile analyses (LPA) based on two types of process data (i.e., response times and the number of actions) collected from the Program for International Student Assessment (PISA) 2015 ColPS assessment, a large-scale international assessment of the human-to-agent approach. Analyses were conducted on test-takers who: (a) were administered the assessment in English and (b) were assigned the "Xandar" unit at the beginning of the test. The total sample size was N = 2,520. Analyses revealed two profiles (i.e., Profile 1 [95%] vs. Profile 2 [5%]) showing different behavioral characteristics across the four parts of the assessment unit. Significant differences were also found in overall performance between the profiles.