Background: In CS1 courses, Parsons problems are a popular activity in which students are given blocks of code and asked to rearrange them into the correct order. Parsons problems often include incorrect blocks of code referred to as distractor blocks. Despite their widespread use, there have been few investigations into how distractor blocks impact student learning. Objectives: Our goals are to understand (1) the impact that including distractor blocks in Parsons problems has on learning and (2) the causality underlying that learning, if any. Methods: In this paper, we present the results of an explanatory sequential mixed methods study investigating the impact of distractor blocks on student learning. For the initial, quantitative stage, we use a randomized control trial to quantify the learning outcomes from practice with Parsons problems that include distractor blocks, as measured via post-test taken immediately after the practice activity and a retention test taken a week later. This study is followed by think-aloud interviews with 10 students practicing using a mix of Parsons problems that do and do not contain distractors to understand differences in how students approach those problems. Findings: Our findings show that students who practiced using Parsons problems that contained distractors performed 11 percentage points better on the immediate post-test (statistically significant) and 10 percentage points better on the retention test (approaching significance). The results of the think-aloud interviews indicate that grouping distractors with blocks of correct code causes students to more closely attend to the details of the code within those blocks. Implications: The results of this study indicate that distractors are essential when Parsons problems are used in a formative context. When they are not included, students may be able to successfully place blocks of code without attending to details of the code. This in turn limits their ability to learn new concepts or reinforce existing knowledge from those code blocks.

Parsons problems are drag-and-drop computer programming puzzles that require learners to place code blocks in the correct order and sometimes indentation. Introductory computer programming instructors use them to teach novice programmers how to code while optimizing problem-solving efficiency and cognitive load. While there is research on the design of Parsons problems for programmers without disabilities and programmers with visual or motor impairments, research regarding their accessibility for programmers with cognitive disabilities is scant. To identify the accessibility barriers and benefits of Parsons problems for neurodiverse programmers, an exploratory multiple-case study was conducted. Participants were asked to read eight chapters of an interactive eBook on Python and to solve Parsons problems. Within-case analyses of 15 retrospective think-aloud interviews with five novice programmers with disabilities led to four recommendations for improving the cognitive accessibility of Parsons problems. For example, programmers with seizure disorders may experience seizures when solving programming problems that require numeric calculations. Hence, creating a range of Parsons problems that do not require mental arithmetic could improve the learning experience for programmers with seizure disorders and those who struggle with mental calculations by lowering their cognitive load. Given this study's qualitative and exploratory approach, it does not offer conclusive, broadly generalizable results. Yet, it reveals detailed and promising avenues for exploration in computing education research that might elude many quantitative techniques.

poster Share on Codespec: A Computer Programming Practice Environment Authors: Carl Christopher Haynes-Magyar School of Information, University of Michigan, USA School of Information, University of Michigan, USAView Profile , Nathaniel James Haynes-Magyar Center for Academic Innovation, University of Michigan, USA Center for Academic Innovation, University of Michigan, USAView Profile Authors Info & Claims ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2August 2022Pages 32–34https://doi.org/10.1145/3501709.3544278Published:07 August 2022Publication History 1citation158DownloadsMetricsTotal Citations1Total Downloads158Last 12 Months111Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Adaptive Parsons problems could be used to reduce the difficulty of introductory programming courses and increase the use of active learning in lecture. Parsons problems provide mixed-up code blocks that must be placed in order. In adaptive Parsons problems, if a learner is struggling to solve a problem it can dynamically be made easier. This makes it possible for students to correctly solve a problem in a limited time, even if they are struggling. Previous research on the effectiveness and efficiency of solving Parsons problems for learning has been conducted in controlled conditions or in lab/discussion. We tested the efficiency of solving adaptive Parsons problems versus writing the equivalent code as lecture assignments through three between-subjects experiments. The median time to solve each Parsons problem was less than the median time to write the equivalent code for all but two of the problems, both with complex conditionals. However, that difference was significant for only six of the 10 problems. Our hypothesis for why two problems had a higher median time to solve as a Parsons problem than as a write code problem was that the problem instructions did not match the Parsons problem solution and/or they also had a large number of possible correct solutions. Results from student surveys also provided evidence that most students (78%) find solving adaptive Parsons problems in lecture helpful for their learning, but that some (36.2%) would rather write the code themselves. These findings have implications for how to best use Parsons problems.