Teaching the software engineers of the future to write high-quality code with good style and structure is important. This systematic literature review identifies existing instructional approaches, their objectives, and the strategies used for measuring their effectiveness. Building on an existing mapping study of code quality in education, we identified 53 papers on code structure instruction. We classified these studies into three categories: (1) studies focused on developing or evaluating automated tools and their usage (e.g., code analyzers, tutors, and refactoring tools), (2) studies discussing other instructional materials, such as learning resources (e.g., refactoring lessons and activities), rubrics, and catalogs of violations, and (3) studies discussing how to integrate code structure into the curriculum through a holistic approach to course design to support code quality. While most approaches use analyzers that point students to problems in their code, incorporating these tools into classrooms is not straightforward. Combined with further research on code structure instruction in the classroom, we call for more studies on effectiveness. Over 40% of instructional studies had no evaluation. Many studies show promise for their interventions by demonstrating improvement in student performance (e.g., reduced violations in student code when using the intervention compared with code that was written without access to the intervention). These interventions warrant further investigation on learning, to see how students apply their knowledge after the instructional supports are removed.

Individuals who have sustained a Spinal Cord Injury (SCI) undergo abrupt changes in their functional abilities, impacting all aspects of their lives and imposing a life-long reliance on assistive tools and support from others. This paper aims to understand individuals’ support-seeking behavior in social media as they adjust to their “new normal”—life with reduced mobility and sensation. To understand their online support-seeking behavior, we conducted content analysis on 960 post-threads from SCI-specific subreddit groups. We found that individuals seek informational and emotional support regardless of injury level and time elapsed since injury. Additionally, individuals seek and receive online informational support concerning assistive logistics, motor-functionality, newly acquired self-care, and daily living activities. Similarly, individuals seek emotional support for motivation, and creating new self-identity. Finally, we discuss how social media support dynamics might facilitate reconstructing self-identity, adopting assistive technology, and improving relationships to help adjust to the “new normal.”

How does students' knowledge of code structure improve as they progress through their degree, and where do students struggle? We conducted a comparative study between introductory (CS1) and intermediate CS students (CS2) to explore these questions. Using an online survey with several tasks, including identification of expert patterns, judgment of readable structure, code comprehension, code writing, and editing, we focused on two important code structures: (S1) returning boolean expressions directly and (S2) unique vs. repeated code within if and else. Student performance varied based on structure and task: in both S1 and S2, CS2 students demonstrated higher performance in identifying patterns, judgment of readable structure, and editing. However, evidence of improvement in code writing was only found for S1, and improvement in code comprehension was only found for S2. Therefore, students may need different supports across different code structures. With the exception of comprehension of S1, student performance was far below ceiling, suggesting a need for more support.

How can we teach students to use more readable code structures? How common is it for students to choose less readable (but still functional) alternatives? We explore these questions for a specific anti-pattern: using sequential if statements when conditions are exclusive (rather than using else-if or else). We created and validated an automated detector to identify this anti-pattern in student's code. Running the detector on 1,764 homework submissions (from 270 students in a CS2 class on data structures and algorithms) showed that this anti-pattern was common and varied by assignment: across 12 assignments, 3% to 50% of submissions used sequential ifs for exclusive cases. However, using this anti-pattern did not preclude using else-ifs: across assignments, up to 34% of the submissions used both forms. Further, students used sequential if statements in surprising ways, such as checking a condition and then the negation of that condition, indicating a more novice level of understanding than expected for an intermediate course. Hand-inspection of the detector-flagged cases suggests that sequential ifs for exclusive cases may be a code smell that can indicate larger problems with logic and abstraction.