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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. 

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. 

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. 

We describe a system called Qr-Hint that, given a (correct) target query Q* and a (wrong) working query Q, both expressed in SQL, provides actionable hints for the user to fix the working query so that it becomes semantically equivalent to the target. It is particularly useful in an educational setting, where novices can receive help from Qr-Hint without requiring extensive personal tutoring. Since there are many different ways to write a correct query, we do not want to base our hints completely on how Q* is written; instead, starting with the user's own working query, Qr-Hint purposefully guides the user through a sequence of steps that provably lead to a correct query, which will be equivalent to Q* but may still look quite different from it. Ideally, we would like Qr-Hint's hints to lead to the smallest possible corrections to Q. However, optimality is not always achievable in this case due to some foundational hurdles such as the undecidability of SQL query equivalence and the complexity of logic minimization. Nonetheless, by carefully decomposing and formulating the problems and developing principled solutions, we are able to provide provably correct and locally optimal hints through Qr-Hint. We show the effectiveness of Qr-Hint through quality and performance experiments as well as a user study in an educational setting. 

Data analytics skills have become an indispensable part of any education that seeks to prepare its students for the modern workforce. Essential in this skill set is the ability to work with structured relational data. Relational queries are based on logic and may be declarative in nature, posing new challenges to novices and students. Manual teaching resources being limited and enrollment growing rapidly, automated tools that help students debug queries and explain errors are potential game-changers in database education. We present a suite of tools built on the foundations of database theory that has been used by over 1600 students in database classes at Duke University, showcasing a high-impact application of database theory in database education. 

Undergraduate teaching assistants (UTAs) office hours are an approachable way for students to get help, but little is known about why and for what do the students choose to attend office hours. We sought to understand what kind of help the students believe they need by analyzing the problem-solving step students self-reported when joining the office hours queue app. We used the UPIC framework to aggregate course specific problem-solving steps to enable comparing between seven data sets from a CS1 and a data science course across four semesters. We then compared the class-level and student-level phase distributions to understand the differences between the two courses and the two levels in the courses. We found most students have a "primary phase" where a majority of their interactions fall, and there are significant individual differences in their phase distributions. Moreover, we did not find either students' demographics or the context of their first visits to significantly impact their individual differences in the phase distributions, suggesting students may have fixed beliefs on how to approach office hours. Finally, a strong majority of interactions happen within 3 days of the deadline, such that the UPIC distribution for those days looks like the class-level phase distribution.