Computing students often encounter real-world problems that are open-ended, ambiguous, and lack a single correct solution. This paper examines how computing students navigate such problems in collaborative team settings and how these experiences shape peer evaluation. Using data from a project-based visualization course, we analyze team Discord discussions, participation and activity metrics, and written peer evaluations across multiple teams through a mixed-methods approach. Our findings show that teams commonly encounter decision conflict when working on open-ended problems, which they address through negotiation, resource sharing, and iterative discussion. Disagreement often coincides with sustained engagement and collaborative sensemaking while avoiding breakdown. Instructors are typically treated as collaborative resources, with teams retaining ownership over decisions. Peer evaluations reflect these processes only weakly: visible participation and time investment show limited association with peer ratings, while peers emphasize the relevance, timing, and impact of contributions. Differences between self- and peer evaluations further highlight the socially situated nature of peer judgment. These findings provide empirical insight into collaborative open-ended problem solving in computing education and how peer evaluation captures students' perceptions of contribution.

The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction.