In the 1990s, educators advocated for projects in statistical courses to enrich student learning. Prior research showcases the positive impact of Project‐Based Learning (PBL), where students complete course‐driven projects. In agreement with this perspective, we implemented PBL methodologies within two statistical courses at a North American research‐intensive university: “Survey, Sampling, & Design” and “Experimental Design.” Students were invited to participate in an optional survey to share their opinions regarding the course project. Consistent with existing literature, our findings indicate that students hold favorable views towards course‐based projects, noticing benefits such as understanding real‐life applications, collaboration, and enhancing data analysis skills. Additionally, many students have incorporated the use of generative AI for their works, such as ChatGPT, and shared the advantages of such tools in their coursework. Drawing from our experiences, we propose strategies to enhance course projects and address concerns related to the overreliance of generative AI tools.

In contrast with studies that have identified why students commit academic offences, many educators are familiar with the excuse that an accused student did not know the behavior counted as dishonest. Given the variations in policy and the ways collaboration and code sharing occur in professional and hobbyist spaces, this might be plausible. Mismatches between students' conceptions of academic honesty and course policy can have major consequences, from being kicked out of programs to being too nervous to study with peers. In this work, we investigate what students understand about academic integrity in computer science courses and if there are differences based on university, country, demographic, or online versus in-person courses. We present a study that surveys undergraduate computer science students (N = 1,011) at three universities (Australia, Canada, and the United States of America). The results show that all three institutions take academic integrity seriously, and their students are aware of its importance, but confusion on what is covered under the policies is common. Interestingly, the results also show that course instructors play a huge role as to what students perceive to be a violation of the academic integrity policy at their institution. By understanding student's perspectives on academic integrity, educators can better develop policies and practices that reduce inadvertent and mistaken violations of academic integrity policies.

Entropy and extropy are emerging concepts in machine learning and computer science. Within the past decade, statisticians have created estimators for these measures. However, associated variability metrics, specifically varentropy and varextropy, have received comparably less attention. This paper presents a novel methodology for computing varentropy and varextropy, drawing inspiration from Bayesian nonparametric methods. We implement this approach using a computational algorithm in R and demonstrate its effectiveness across various examples. Furthermore, these new estimators are applied to test uniformity in data.