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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. Objectives. We seek to study how students value each dimension in the help landscape in their resource selection and utilization processes, as well as how their identities relate to their perceptions of the landscape. Method. We surveyed N=1,625 students on their perceptions of 8 dimensions across 12 offerings of 7 courses at 2 institutions. Findings. We found a consistent pattern of four distinct dimension tiers ordered from most to least important: (1) timeliness of help, (2) availability and adaptability of the resource, (3) the resource's time/space anchor and the effort to phrase the help need, (4) formality and socialness of the resource. We also found men and first-years rate all dimensions as less important than their classmates. Implications. Our results reveal what the students collectively value most when selecting help resources and thus can inform practitioners seeking to improve their course help ecosystem. 

The accessibility and effectiveness of help-seeking resources plays a pivotal role in contributing to the success of students in Computer Science courses. However, students do not always choose to utilize these resources, and when they do, their experiences can vary. While some students commend help-seeking resources for effectively providing clarification on assignment instructions, debugging code, and addressing questions about course concepts, others share instances where their problems were not resolved, or, in some cases, they did not receive any meaningful guidance from these resources. In this study, we examine the experiences of students enrolled in a CS2 course, all of whom had access to the course's help-seeking resources. These experiences were gathered through qualitative interviews at three time points within a semester. Our findings, derived from emergent coding, reveal thematic patterns in student encounters with help-seeking resources and contribute to a broader theme regarding help-seeking resource utilization at different phases of the semester. The findings of this investigation contribute to the wider conversation on student success and help-seeking resource utilization in Computer Science education. 

Within K-12 computing education, the building blocks that contribute to student success and equitable outcomes are broadly captured in the CAPE framework (i.e., capacity, access, participation, experience). However, these broad com- ponents provide limited detail on the important factors that can support academic achievement, particularly within each component. Our research question for this study was: What are factors comprising each component of CAPE that support academic achievement among K-12 CS students?To answer this question, we first created an a priori set of factors based on previous research findings that have been found to contribute to academic achievement. After organizing these factors within each CAPE component, we conducted a systematic mapping review of K-12 CS education research (2019-2021) (n = 196) from publicly available peer-reviewed articles from the K-12 CS Education Research Resource Center. Through this mapping, we identified an additional set of factors that have been studied by CS education researchers and added these to our set of factors. More importantly, we found that capacity was the component investigated the most frequently and access was the least. There are many areas (or categories) within each component that remain unstudied (i.e., dual credit offerings, career guidance), even though they play a role in computing education. The expanded CAPE framework is now publicly available and can be used to inform researchers and practitioners about what each CAPE component comprises. These factors are accompanied by descriptions of each factor. Not only does it highlight the many factors to be considered when designing and delivering computing education to K-12 students, it also provides a solid framework for future research that synthesizes or analyzes homogeneous factors or explores how various factors may be correlated. 

Promptly addressing students’ help requests on their programming assignments has become more and more challenging in computer science education. Since the pandemic, most instructors use online office hours to answer questions. Prior studies have shown increased student participation with online office hours. This popularity has led to significantly longer wait times in the office hours queue, and various strategies for selecting the next student to help may impact wait time. For example, prioritizing students who have not been seen on the day of the deadline will extend the wait time for students who are frequently rejoining the queue. To better understand this problem, we explored students’ behavior when they are waiting in the queue. We investigate the amount of time students are willing to wait in the queue by modeling the distribution of cancellation time. We find that after waiting for 49 minutes, most students will cancel their help request. Then, we looked at students’ coding actions during the waiting period and found that only 21% of students have commits while waiting. Surprisingly, students who waited for hours did not commit their work for automated feedback. Our findings suggest that time in the queue should be considered in addition to other factors like last interaction when selecting the next student to help during office hours to minimize canceled interactions. 

Problem. To investigate and identify promising practices in eq- uitable K-12 and tertiary computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being con- ducted and the lack of research that supports teaching al students computing. Research Question. Our research question for our study was: In what ways can existing research standards and practices inform methodologically sound, equity-enabling computing education research? Methodology. We conducted a concept analysis using existing re- search and various standards (e.g. European Educational Research Association, Australian Education Research Organisation, Ameri- can Psychological Association). We then synthesised key features ni the context of equity-focused K-12 computing education research. Findings. We present aset of guidelines for general research design that takes into account best practices across the standards that are infused with equity-enabling research practices. Implications. Our guidelines wil directly impact future equitable computing education research by providing guidance on conducting high-quality research such that the findings can be aggregated and impact future policy with evidence-based results. Because we have crafted these guidelines to be broadly applicable across a variety of settings, we believe that they will be useful to researchers operating in a variety of contexts. 

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. 

Software testing is a critical skill for computing students, but learning and practicing testing can be challenging, particularly for beginners. A recent study suggests that a lightweight testing checklist that contains testing strategies and tutorial information could assist students in writing quality tests. However, students expressed a desire for more support in knowing how to test the code/scenario. Moreover, the potential costs and benefits of the testing checklist are not yet examined in a classroom setting. To that end, we improved the checklist by integrating explicit testing strategies to it (ETS Checklist), which provide step-by-step guidance on how to transfer semantic information from instructions to the possible testing scenarios. In this paper, we report our experiences in designing explicit strategies in unit testing, as well as adapting the ETS Checklist as optional tool support in a CS1.5 course. With the quantitative and qualitative analysis of the survey responses and lab assignment submissions generated by students, we discuss students' engagement with the ETS Checklists. Our results suggest that students who used the checklist intervention had significantly higher quality in their student-authored test code, in terms of code coverage, compared to those who did not, especially for assignments earlier in the course. We also observed students' unawareness of their need for help in writing high-quality tests.