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Novice programmers need to write basic code as part of the learning process, but they often face difficulties. To assist struggling students, we recently implemented personalized Parsons problems, which are code puzzles where students arrange blocks of code to solve them, as pop-up scaffolding. Students found them to be more engaging and preferred them for learning, instead of simply receiving the correct answer, such as the response they might get from generative AI tools like ChatGPT. However, a drawback of using Parsons problems as scaffolding is that students may be able to put the code blocks in the correct order without fully understanding the rationale of the correct solution. As a result, the learning benefits of scaffolding are compromised. Can we improve the understanding of personalized Parsons scaffolding by providing textual code explanations? In this poster, we propose a design that incorporates multiple levels of textual explanations for the Parsons problems. This design will be used for future technical evaluations and classroom experiments. These experiments will explore the effectiveness of adding textual explanations to Parsons problems to improve instructional benefits. 

Introductory programming courses aim to teach students to write code independently. However, transitioning from studying worked examples to generating their own code is often difficult and frustrating for students, especially those with lower CS self-efficacy in general. Therefore, we investigated the impact of using Parsons problems as a code-writing scaffold for students with varying levels of CS self-efficacy. Parsons problems are programming tasks where students arrange mixed-up code blocks in the correct order. We conducted a between-subjects study with undergraduate students (N=89) on a topic where students have limited code-writing expertise. Students were randomly assigned to one of two conditions. Students in one condition practiced writing code without any scaffolding, while students in the other condition were provided with scaffolding in the form of an equivalent Parsons problem. We found that, for students with low CS self-efficacy levels, those who received scaffolding achieved significantly higher practice performance and in-practice problem-solving efficiency compared to those without any scaffolding. Furthermore, when given Parsons problems as scaffolding during practice, students with lower CS selfefficacy were more likely to solve them. In addition, students with higher pre-practice knowledge on the topic were more likely to effectively use the Parsons scaffolding. This study provides evidence for the benefits of using Parsons problems to scaffold students’ write-code activities. It also has implications for optimizing the Parsons scaffolding experience for students, including providing personalized and adaptive Parsons problems based on the student’s current problem-solving status. 

Novice programmers struggle with writing code from scratch. One possible way to help them is by using an equivalent Parsons problem on demand, where learners place mixed-up code blocks in the correct order. In a classroom study with 89 undergraduate students, we examined how using a Parsons problem as scaffolding impacts performance and problem-solving efficiency. Results showed that students in the Parsons as Help group achieved significantly higher practice performance and problem-solving efficiency than students who wrote code without help, while achieving the same level of posttest scores. These results improve the understanding of Parsons problems and contribute to the design of future coding practices. 

In this paper, we explore using Parsons problems to scaffold novice programmers who are struggling while solving write-code problems. Parsons problems, in which students put mixed-up code blocks in order, can be created quickly and already serve thousands of students while other types of programming support methods are expensive to develop or do not scale. We conducted two studies in which novices were given equivalent Parsons problems as optional scaffolding while solving write-code problems. We investigated when, why, and how students used the Parsons problems as well as their perceptions of the benefits and challenges. A think-aloud observational study with 11 undergraduate students showed that students utilized the Parsons problem before writing a solution to get ideas about where to start; during writing a solution when they were stuck; and after writing a solution to debug errors and look for better strategies. Semi-structured interviews with the same 11 undergraduate students provided evidence that using Parsons problems to scaffold write-code problems helped students to reduce the difficulty, reduce the problem completion time, learn problem-solving strategies, and refine their programming knowledge. However, some students found them less useful if the Parsons solution did not match their approach or if they did not understand the solution. We then conducted a between-subjects classroom study with 81 undergraduate students to investigate the effects on learning. We found that students who received Parsons problems as scaffolding during write-code problems spent significantly less time solving those problems. However, there was no significant learning gain in either condition from pretest to posttest. We also discuss the design implications of our findings.