Abstract: Traditional introductory computer programming practice has included writing pseudocode, code-reading and tracing, and code-writing. These problem types are often time-intensive, frustrating, cognitively complex, in opposition to learners’ self-beliefs, disengaging, and demotivating—and not much has changed in the last decade. Pseudocode is a plain language description of the steps in a program. Code-reading and tracing involve using paper and pencil or online tools such as PythonTutor to trace the execution of a program, and code-writing requires learners to write code from scratch. In contrast to these types of programming practice problems, mixed-up code (Parsons) problems require learners to place blocks of code in the correct order and sometimes require the correct indentation and/or selection between a distracter block and a correct code block. Specifically, my research has provided evidence that Parsons problems can improve problem-solving efficiency, lower cognitive load, support pattern application and acquisition, and impact learners’ engagement and motivation positively. It has also led to four recommendations for improving the cognitive accessibility of Parsons and other computer programming problems for neurodiverse learners. Despite the potential of Parsons problems, few platforms offer a seamless transition between different problem types. Hence, Codespec, a computer programming tutor I’ve created, supports learners in solving the same programming problem as a Pseudocode Parsons problem, a Parsons problem, a Faded Parsons problem, a fix-code problem, or a write-code problem. Codespec’s help-seeking features include providing the correct order of Parsons problem blocks, removing distractor blocks, providing indentation, and showing pseudocode. This talk will feature experiments designed to investigate the problem-solving efficiency, cognitive load, pattern application and acquisition, cognitive accessibility, and scaffolding potential of Parsons problems.

Short Bio: Carl C. Haynes-Magyar is a Presidential Postdoctoral Fellow and an International Society of the Learning Sciences (ISLS) Emerging Scholar at Carnegie Mellon University's School of Computer Science in the Human-Computer Interaction Institute. He received his Ph.D. at the University of Michigan School of Information in 2022 and a master's degree in Library and Information Science with honors from Syracuse University's School of Information Studies (iSchool) in 2016. Carl's research has produced computer science instruction and assessment materials that increase engagement and optimize learning.