Automated Grading of Regular Expressions

With the rapid transition to distance learning, automatic grading software becomes more important to both teachers and students. We study the problem of automatically grading the regular expressions submitted by students in courses related to automata and formal language theory. In order to utilize the semantic information of the regular expression, we define a declarative logic that can be described by regular language and at the same time has natural language characteristics, and use it for the following tasks: 1) to assign partial grades for incorrect regular expressions and 2) to provide helpful feedback to students to make them understand the reason for the grades and a way to revise the incorrect regular expressions into correct ones. We categorize the cases when students’ incorrect submissions deserve partial grades and suggest how to assign appropriate grades for each of the cases. In order to optimize the runtime complexity of the algorithm, two heuristics based on automata theory are proposed and evaluated on the dataset collected from undergraduate students. In addition, we suggest Regex2NL which translates regular expressions to natural language descriptions to give insight to students so that they can understand how the regular expressions work.