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1. Using Adaptive Parsons Problems to Scaffold Write-Code Problems

   https://doi.org/10.1145/3501385.3543977  

   Hou, Xinying; Ericson, Barbara Jane; Wang, Xu (August 2022, Proceedings of the 2022 ACM Conference on International Computing Education Research)

   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. 

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2. Investigating the Impact of Using a Live Programming Environment in a CS1 Course

   Huang, Ruanqianqian; Ferdowsifard, Kasra; Selvaraj, Ana; Soosai Raj, Adalbert Gerald; Lerner, Sorin (March 2022, In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education)

   Novice programmers often struggle with code understanding and debugging. Live Programming environments visualize the runtime values of a program each time it is modified to provide immediate feedback, which help with tracing the program execution. This paper presents the use of a Live Programming tool in a CS1 course to better understand the impact of Live Programming on novices’ learning metrics and their perceptions of the tool. We conducted a within-subjects study at a large public university in a CS1 course in Python (N=237) where students completed tasks in a lab setting, in some cases with a Live Programming environment, and in some cases without. Through post-lab surveys and open-ended feedback, we measured how well students understood the material and how students perceived the programming environment. To understand the impact of Live Programming, we compared the collected data for students who used Live Programming with the data for students who did not. We found that while learning outcomes were the same regardless of whether Live Programming was used or not, students who used the Live Programming tool completed some code tracing tasks faster. Furthermore, students liked the Live Programming environment more, and rated it as more helpful for their learning. 

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3. From Novice to Expert: Analysis of Token Level Effects in a Longitudinal Eye Tracking Study

   https://doi.org/10.1109/ICPC52881.2021.00025  

   Al Madi, Naser; Peterson, Cole S.; Sharif, Bonita; Maletic, Jonathan I. (May 2021, 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC))

   null (Ed.)

   Program comprehension is a vital skill in software development. This work investigates program comprehension by examining the eye movement of novice programmers as they gain programming experience over the duration of a Java course. Their eye movement behavior is compared to the eye movement of expert programmers. Eye movement studies of natural text show that word frequency and length influence eye movement duration and act as indicators of reading skill. The study uses an existing longitudinal eye tracking dataset with 20 novice and experienced readers of source code. The work investigates the acquisition of the effects of token frequency and token length in source code reading as an indication of program reading skill. The results show evidence of the frequency and length effects in reading source code and the acquisition of these effects by novices. These results are then leveraged in a machine learning model demonstrating how eye movement can be used to estimate programming proficiency and classify novices from experts with 72% accuracy. 

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4. An Automated Approach to Recommending Relevant Worked Examples for Programming Problems

   https://doi.org/10.1145/3641554.3701951  

   Hoq, M; Patil, A; Akhuseyinoglu, K; Brusilovsky, P; Akram, B (February 2025, ACM Technical Symposium on Computer Science Education)

   Novice programmers can greatly improve their understanding of challenging programming concepts by studying worked examples that demonstrate the implementation of these concepts. Despite the extensive repositories of effective worked examples created by CS education experts, a key challenge remains: identifying the most relevant worked example for a given programming problem and the specific difficulties a student faces solving the problem. Previous studies have explored similar example recommendation approaches. Our research introduces a novel method by utilizing deep learning code representation models to generate code vectors, capturing both syntactic and semantic similarities among programming examples. Driven by the need to provide relevant and personalized examples to programming students, our approach emphasizes similarity assessment and clustering techniques to identify similar code problems, examples, and challenges. This method aims to deliver more accurate and contextually relevant recommendations based on individual learning needs. Providing tailored support to students in real-time facilitates better problem-solving strategies and enhances students' learning experiences, contributing to the advancement of programming education. 

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5. The Effect of Informing Agency in Self-Directed Online Learning Environments

   https://doi.org/10.1145/3386527.3405928  

   Xie, Benjamin; Nelson, Greg L.; Akkaraju, Harshitha; Kwok, William; Ko, Amy J. (August 2020, ACM Learning at Scale)

   Background and Context: Current introductory instruction fails to identify, structure, and sequence the many skills involved in programming. Objective: We proposed a theory which identifies four distinct skills that novices learn incrementally. These skills are tracing, writing syntax, comprehending templates (reusable abstractions of programming knowledge), and writing code with templates. We theorized that explicit instruction of these skills decreases cognitive demand. Method: We conducted an exploratory mixed-methods study and compared students’ exercise completion rates, error rates, ability to explain code, and engagement when learning to program. We compared material that reflects this theory to more traditional material that does not distinguish between skills. Findings: Teaching skills incrementally resulted in improved completion rate on practice exercises, and decreased error rate and improved understanding of the post-test. Implications: By structuring programming skills such that they can be taught explicitly and incrementally, we can inform instructional design and improve future research on understanding how novice programmers develop understanding. 

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