Reflection activities in introductory programming courses help students assess their learning experience, but prior work overlooks how they translate struggles into improvement plans. We analyzed 906 open-ended reflections from two introductory programming courses on coding struggles (code design, debugging, concepts, timeliness) vs. improvement plans (timeliness, debugging, implementation, collaboration) aggregating these into themes. Some themes aligned strongly; others did not, revealing what students perceive as challenging yet actionable in their learning.

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.

When students leave office hours, what do they do next? This study introduces preliminary work on a behavior-based measure of help-seeking success by exploring the relationship between office hour (OH) interactions and subsequent coding progress. We ask: Can students' code changes following help-seeking interactions serve as evidence of forward progress?

Background and Context. Existing works in computing students' help-seeking and resource selection identified an expanding set of important dimensions that students consider when choosing a help resource. However, most works either assume a predefined list of help resources or focus on one specific help resource, while the landscape of help resources evolve at a faster speed.

Students can have widely varying experiences while working on CS2 coding projects. Challenging experiences can lead to lower motivation and less success in completing these assignments. In this paper, we identify the common struggles CS2 students face while working on course projects and examine whether or not there is evidence of improvement in these areas of struggle between projects. While previous work has been conducted on understanding the importance of self-regulated learning to student success, it has not been fully investigated in the scope of CS2 coursework. We share our observations on investigating student struggles while working on coding projects through their self-reported response to a project reflection form. We apply emergent coding to identify student struggles at three points during the course and compare them against student actions in the course, such as project start times and office hours participation, to identify if students were overcoming these struggles. Through our coding and analysis we have found that while a majority of students encounter struggles with time management and debugging of failing tests, students tend to emphasize wanting to improve their time management skills in future coding assignments.

The help-seeking interactions faculty encounter will vary depending upon the course structure and the students enrolled. While the course structure tends to remain the same, the students enrolled change each semester, presenting a new set of students who seek help in many different ways. We share our observations in investigating student behavior when using course resources, including office hours and online discussion forums, in two introductory computer science courses. Our goal is to explore differences in help-seeking behavior to construct Student "Help-Seeking" Personas. Preliminary analysis has shown that, for these two introductory CS courses, there are no well-defined personas that emerge from the grouping of help-seeking behaviors. The demographic of students exhibiting various help-seeking behaviors tends to be a near-proportionate subset of the overall course demographic. Thus, no distinct personas emerge from the students' help-seeking behaviors in introductory CS courses that faculty can utilize to better understand their students.