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1. 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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2. Rethinking Computing Students’ Help Resource Utilization through Sequentiality

   https://doi.org/10.1145/3716860  

   Ko, Shao-Heng; Stephens-Martinez, Kristin (March 2025, ACM Transactions on Computing Education)

   Background. Academic help-seeking benefits students’ achievement, but existing literature either studies important factors in students’ selection of all help resources via self-reported surveys or studies their help-seeking behavior in one or two separate help resources via actual help-seeking records. Little is known about whether computing students’ approaches and behavior match, and not much is understood about how they transition sequentially from one help resource to another. Objectives. We aim to study post-secondary computing students’ academic help-seeking approach and behavior. Specifically, we seek to investigate students’ self-reported orders of resource usage and whether these approaches match with students’ actual utilization of help resources. We also examine frequent patterns emerging from students’ chronological help-seeking records in course-affiliated help resources. Context and Study Method. We surveyed students’ self-reported orders of resource usage across 12 offerings of seven courses at two institutions, then analyzed their responses using various help resource dimensions identified by existing works. From two of these courses (an introduction to programming course and a data science course, 11 offerings), we obtained students’ help-seeking records in all course-affiliated help resources, along with code autograder records. We then compared students’ reported orders in these two courses against their actions in the records. Finally, we mined sequences of student help-seeking events from these two courses to reveal frequent sequential patterns. Findings. Students’ reported orders of help resource usage form a progression of clusters where resources in each cluster are more similar to each other by help resource dimensions than to resources outside of their cluster. This progression partially confirms phenomena and decision factors reported by existing literature, but no factor/dimension alone can explain the entire progression. We found students’ actual help-seeking records did not deviate much from their self-reported orders. Mining of the sequential records revealed that help-seeking from course-affiliated human resources led to measurable progress more often than not, and students’ usage of consulting/office hours (mainly run by undergraduate teaching assistants) itself was the best indicator for future usage within the lifespan of the same assignment. Implications. Our results demonstrate that computing students’ help resource selection/utilization is a sophisticated process that should be modeled and analyzed with sufficient awareness of its inherent sequentiality. We identify future research directions through this preliminary analysis, which can lead to a better understanding of computing students’ help-seeking behavior and better resource utilization/management in large-scale instructional contexts. 

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3. Admitting you have a problem is the first step: Modeling when and why students seek help in programming assignments.

   Gao, Zhikai; Erickson, Bradley; Xu, Yiqiao; Lynch, Collin; Heckman, Sarah; Barnes, Tiffany (July 2022, Proceedings of the 15th International Confer- ence on Educational Data Mining)

   Mitrovic, A.; Bosch, N. (Ed.)

   In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected student’s help requests during coding assignments on two different platforms in a CS2 course, and categorized those requests into eight categories (including implementation, addressing test failures, general debugging, etc.). Then we analyzed the proportion of each type of requests and how they changed over time. We also collected student’s coding status (including what part of the code changed and the frequency of commits) before they seek help to investigate if students share a similar code change behavior leading to certain type of help requests. 

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4. Undergraduate Student Concerns in Introductory STEM Courses: What They Are, How They Change, and What Influences Them

   https://doi.org/10.1007/s41979-020-00031-1  

   Meaders, Clara L.; Lane, A. Kelly; Morozov, Anya I.; Shuman, J. Kenny; Toth, Emma S.; Stains, Marilyne; Stetzer, MacKenzie R.; Vinson, Erin; Couch, Brian A.; Smith, Michelle K. (April 2020, Journal for STEM Education Research)

   Abstract Introductory STEM courses represent entry points into a major, and student experiences in these courses can affect both their persistence and success in STEM disciplines. Identifying course-based student concerns may help instructors detect negative perceptions, areas of struggle, and potential barriers to success. Using an open-response survey question, we identified 13 common concerns expressed by students in introductory STEM courses. We converted these student-generated concerns into closed-ended items that were administered at the beginning and middle of the semester to students in 22 introductory STEM course sections across three different institutions. Students were asked to reflect on each item on a scale from very concerned to not concerned. A subset of these concerns was used to create a summary score of course-based concern for each student. Overall levels of student concern decreased from the first week to the middle of the semester; however, this pattern varied across different demographic groups. In particular, when controlling for initial concern and course grades, female students held higher levels of concern than their peers. Since student perceptions can impact their experiences, addressing concerns through communication and instructional practices may improve students’ overall experiences and facilitate their success. 

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5. Increase Performance and Retention: Teach Students How To Study

   https://doi.org/10.1145/3478431.3499340  

   Lionelle, Albert; Ghosh, Sudipto; Ourada, Shannon; Musser, Westin (February 2022, SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education)

   Intervention in the form of changing one's teaching style is beneficial for boosting student grades and retention. However, in spite of the availability of multiple intervention approaches, a key hindrance is reliance on the belief that students know how to study. We dedicated time and resources to not only teach the discipline of Computer Science, but also to teach students how to study using techniques grounded in psychology. We offered a one-credit "booster" course to students taking CS 2: Data Structures. Through direct advisor intervention based on the first exam grade, students were encouraged to take the booster course along with traditional interventions. We then tracked student growth across exams for the course as students were learning and being held accountable to study techniques not often emphasized in Computer Science. The students continued to increase their grades throughout the semester relative to the students who chose to not take the booster class. The students who were targeted for intervention but did not take the booster course continued to have lower grades throughout the semester, and only 41% of them passed the course. Students who participated in the booster course showed a 31% rate of growth across the semester, taking a failing grade to a passing grade, with 100% passing the course with a C or above. These results show a significant influence to help students succeed, which led to higher retention and increased grades. If we want students to truly succeed, we must teach them to study. 

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