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1. You asked, now what? Modeling Students’ Help-Seeking and Coding actions from Re- quest to Resolution

   Zhikai Gao; Bradley Erickson; Yiqiao Xu; Collin Lynch; Sarah Heckman; Tiffany Barnes (December 2022, Journal of educational data mining)

   Nigel Bosch; Antonija Mitrovic; Agathe Merceron (Ed.)

   Demand for education in Computer Science has increased markedly in recent years. With increased demand has come to an increased need for student support, especially for courses with large programming projects. Instructors commonly provide online post forums or office hours to address this massive demand for help requests. Identifying what types of questions students are asking in those interactions and what triggers their help requests can in turn assist instructors in better managing limited help-providing resources. In this study, we aim to explore students’ help-seeking actions from the two separate approaches we mentioned before and investigate their coding actions before help requests to understand better what motivates students to seek help in programming projects. We collected students’ help request data and commit logs from two Fall offerings of a CS2 course. In our analysis, we first believe that different types of questions should be related to different behavioral patterns. Therefore, we first categorized students’ help requests based on their content (e.g., Implementation, General Debugging, or Addressing Teaching Staff (TS) Test Failures). We found that General Debugging is the most frequently asked question. Then we analyzed how the popularity of each type of request changed over time. Our results suggest that implementation is more popular in the early stage of the project cycle, and it changes to General Debugging and Addressing TS Failures in the later stage. We also calculated the accuracy of students’ commit frequency one hour before their help requests; the results show that before Implementation requests, the commit frequency is significantly lower, and before TS failure requests, the frequency is significantly higher. Moreover, we checked before any help request whether students changed their source code or test code. The results show implementation requests related to higher chances of source code changes and coverage questions related to more test code changes. Moreover, we use a Markov Chain model to show students’ action sequences before, during, and after the requests. And finally, we explored students’ progress after the office hours interaction and found that over half of the students improved the correctness of their code after 20 minutes of their office hours interaction addressing TS failures ends. 

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2. Investigating Students' Perspectives on the Value of Help-Seeking Resources in CS Education

   https://doi.org/10.1145/3649165.3690130  

   Zahn, Matthew; Heckman, Sarah; Battestilli, Lina (December 2024, ACM)

   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. 

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3. Characterizing Student Development Progress: Validating Student Adherence to Project Milestones

   Erickson, Bradley; Heckman, Sarah; Lynch, Collin F. (March 2022, Proceedings of the 53rd ACM Technical Symposium on Com- puter Science Education)

   Merkle, Larry; Doyle, Maureen; Sheard, Judithe; Soh, Leen-Kiat; Dorn, Brian (Ed.)

   As enrollment in CS programs have risen, it has become increasingly difficult for teaching staff to provide timely and detailed guidance on student projects. To address this, instructors use automated assessment tools to evaluate students’ code and processes as they work. Even with automation, understanding students’ progress, and more importantly, if students are making the ‘right’ progress toward the solution is challenging at scale. To help students manage their time and learn good software engineering processes, instructors may create intermediate deadlines, or milestones, to support progress. However, student’s adherence to these processes is opaque and may hinder student success and instructional support. Better understanding of how students follow process guidance in practice is needed to identify the right assignment structures to support development of high-quality process skills. We use data collected from an automated assessment tool, to calculate a set of 15 progress indicators to investigate which types of progress are being made during four stages of two projects in a CS2 course. These stages are split up by milestones to help guide student activities. We show how looking at which progress indicators are triggered significantly more or less during each stage validates whether students are adhering to the goals of each milestone. We also find students trigger some progress indicators earlier on the second project suggesting improving processes over time. 

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4. What Makes a Successful Online Learner?: Community College Students’ Perceptions of Online Learning Challenges and Strategies

   Zhou, X.; Li, Q.; Xu, D.; Bostian, B. (June 2021, Online Learning Research Center)

   null (Ed.)

   An extensive theoretical and empirical literature stresses the challenges of online learning, especially among students enrolled in open-access institutions who often struggle more due to job and family commitments and a lack of self-regulated learning skills. As online expansion continues in higher education, understanding the specific challenges students encounter in online coursework, and learning strategies that can help them cope with these challenges, can provide valuable insights to be widely shared. Using open-ended survey data collected from 365 students at a state community college system, this study examined students’ perceptions of challenges of online learning that may lead to undesirable learning outcomes and specific strategies they found effective in addressing these challenges. We combined structural topic modeling and human coding in analyzing student responses. Three sets of challenges—including insufficient time management skills, greater tendencies of multitasking and being distracted in an online learning environment, and ineffective interaction and frustrations with help-seeking—emerged from student responses. In response to these challenges, students reflected on ways to improve online learning experiences and outcomes, including improving time management skills, maintaining an organized and distraction-free study environment, proactively seeking help, and using study strategies to improve learning effectiveness. 

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5. The Trees in the Forest: Characterizing Computing Students' Individual Help-Seeking Approaches

   https://doi.org/10.1145/3632620.3671099  

   Ko, Shao-Heng; Stephens-Martinez, Kristin (August 2024, ACM)

   Background and Context. Academic help-seeking is vital to post-secondary computing students’ effective learning. However, most empirical works in this domain study students’ help resource selection and utilization by aggregating the entire student body as a whole. Moreover, existing theoretical frameworks often implicitly assume that whether/how much a student seeks help from a specific resource only depends on context (the type of help needed and the properties of the resources), not the individual student. Objectives. To address the gap, we seek to investigate individual computing students’ help-seeking approaches by analyzing what help-seeking characteristics are individual-driven (and thus stay consistent for the same student across different course contexts) and what are context-driven. Method. We analyzed N = 597 students’ survey responses on their help resource utilization as well as their actual help-seeking records across 6 courses. We examined relations between individual students’ frequency-based help usage metrics, type-of-help requested in office/consulting hours, self-reported order of ideal help resource usage, and their collaboration inclination in small-scale sections. Findings. We found that students’ frequency-based help metrics and their order of ideal help resource usage stays relatively consistent across different course contexts, and thus may be treated as part of students’ individual help-seeking approaches. On the other hand, the type of help students seek in office/consulting hours and how much they collaborate with peers in small sections do not seem to stay consistent across different contexts and thus might be deemed more context-driven than individual-driven. Implications. Our findings reveal that part of students’ help-seeking characteristics is individual-driven. This opens up a possibility for institutions to track students’ help-seeking records in early/introductory courses, so that some preliminary understanding of students can be acquired before they enter downstream courses. Our insights may also help instructors identify which part of students’ help-seeking behavior are more likely to be influenced by their course context and design. 

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