In recent years, computer science (CS) education has undergone rapid change, first due to the shift to online learning during the COVID-19 pandemic, and more recently with the rise of generative AI (GenAI) tools. How these changes impact students' learning habits and problem-solving attitude remains unknown. This paper presents a long-term analysis of attitudinal survey data from a CS2 course across eight semesters. Drawing on the Computing Attitudes Survey (CAS v4) and the Computer Science Attitudes (CSA) survey, we measure changes in seven constructs, including confidence, mindset, strategies, and motivation. We observe stable or positive shifts in earlier semesters, but sharp negative trends in Fall 2023 and Spring 2024 semesters coinciding with the widespread availability of Generative AI (GenAI) tools. These results highlight the urgent need for instructors to consider how new technologies shape not just students' learning outcomes but their beliefs about their own ability to succeed in computing.

Office hours are a core feature of CS courses. They provide a crucial vector for personalized instruction and individual support. Despite their importance, we the impact of these personal events has not been well assessed each office hour interaction is often hidden from instructors. We do not know if students actually understand the solution or the advice given during the interaction. This lack of knowledge presents a challenge to efforts geared at improving instructional practices and student outcomes. In this poster we report on the results of a survey analysis of office hour participants. Our results show that students and instructional staff had divergent assessments of the interactions with teaching staff overestimating the benefits more than 18.9% of the time. This work highlights detailed analyses of these results along with implications for teaching strategies.

Figure 1: EclipseMonitor workflow diagram.It shows how the student's coding session data from Eclipse development environment has been collected through the EclipseMonitor.

Promptly and properly addressing students' help requests during office hours is a critical challenge for large CS courses. With a large number of help requests, the queue gets longer and students have to endure long wait times. To address this problem, we try to quantify students' patience in the queue through survival analysis. Our results show that half of the students are willing to stay in the queue after waiting for 142.5 minutes. Moreover, we find that female students, morning requests, returning students, and requests about test failures are more likely to stay in the queue for a longer time.

To promote good coding practices, we need to understand what students do when they are on their own. In this research study, we explore students' testing behavior and response to persistent errors to better understand their coding patterns. We investigate how those patterns change when they struggle, and how help-seeking might influence their coding behaviors. We define struggle during coding as failing the same unit test case consecutively for more than four submission events, considering only unit test cases created by the instructors. To analyze the students' coding data, we use progress indicators, student test implementation indicators, and both student-generated and instructor-generated unit test results from each student submission event. In addition, we use office hours attendance records and amount of assignment-related posts created on the course forum. Results show that students tend not to follow test-driven development practices, even when explicitly directed to, and tend to create unit tests only to earn assignment credit rather than to guide their software development. Students also tend not to modify their own unit tests once they have earned the related credits, even when facing coding struggle; they tend to modify their unit tests only after they have been facing coding struggle for an extended number of submission events.

In Computer Science (CS) education, instructors use office hours for one-on-one help-seeking. Prior work has shown that traditional in-person office hours may be underutilized. In response many instructors are adding or transitioning to virtual office hours. Our research focuses on comparing in-person and online office hours to investigate differences between performance, interaction time, and the characteristics of the students who utilize in-person and virtual office hours. We analyze a rich dataset covering two semesters of a CS2 course which used in-person office hours in Fall 2019 and virtual office hours in Fall 2020. Our data covers students' use of office hours, the nature of their questions, and the time spent receiving help as well as demographic and attitude data. Our results show no relationship between student's attendance in office hours and class performance. However we found that female students attended office hours more frequently, as did students with a fixed mindset in computing, and those with weaker skills in transferring theory to practice. We also found that students with low confidence in or low enjoyment toward CS were more active in virtual office hours. Finally, we observed a significant correlation between students attending virtual office hours and an increased interest in CS study; while students attending in-person office hours tend to show an increase in their growth mindset.

The application of unmanned aerial vehicle (UAV) to achieve precision irrigation in agriculture is a hot research topic in the industry. However, much spay and much leakage of pesticide are tricky for the current UAV-based irrigation methods to deal with. In this paper, we propose a new UAV-based irrigation system for precision agriculture. First, considering that different areas in the same farmland may have different pesticide shortage, a map preprocess strategy is introduced to divide the entire farmland into pieces. Second, we establish a UAV precision irrigation model and put forward an adaptive and fast dynamic ant colony optimization (AFD-ACO) algorithm to minimize the longest flight path with the lowest energy consumption and pesticide residues. In order to promote the efficiency and the optimization effect, we utilize the scent pervasion rule to make the global map preprocessed and the neighborhood adaptive search policy to accomplish planning work. Finally, comparing with other two ACO-based algorithms, the proposed algorithm is proved to be effective for the research problem, especially when the more pieces the farmland is divided, the better our solution performs.