Using Micro Parsons Problems to Scaffold the Learning of Regular Expressions

Abstract

Regular expressions (regex) are a text processing method widely used in data analysis, web scraping, and input validation. However, students find regular expressions difficult to create since they use a terse language of characters. Parsons problems can be a more efficient way to practice programming than typing the equivalent code with similar learning gains. In traditional Parsons problems, learners place mixed-up fragments with one or more lines in each fragment in order to solve a problem. To investigate learning regex with Parsons problems, we introduce micro Parsons problems, in which learners assemble fragments in a single line. We conducted both a think-aloud study and a large-scale between-subjects field study to evaluate this new approach. The think-aloud study provided insights into learners' perceptions of the advantages and disadvantages of solving micro Parsons problems versus traditional text-entry problems, student preferences, and revealed design considerations for micro Parsons problems. The between-subjects field study of 3,752 participants compared micro Parsons problems with text-entry problems as an optional assignment in a MOOC. The dropout rate for the micro Parsons condition was significantly lower than the text-entry condition. No significant difference was found for the learning gain on questions testing comprehensive regex skills between the two conditions, but the micro Parsons group had a significantly higher learning gain on multiple choice questions which tested understanding of regex characters.