Contextual Feedback in Automated Exercises for Novice Programmers : A Pilot Study
Year: 2022
Type: dissertation
Abstract: Introductory programming courses lay the very foundation that programmers build upon to learn how to create and improve digital systems that our modern world depends on. In these courses, students are frequently instructed to solve programming exercises for practice - something that many studies encourage. Although repeated practice is indeed a dominant aspect of mastering a skill, it is widely acknowledged that feedback on one’s work is another major influence on learning and achievement. Unfortunately, composing feedback for each exercise attempt is time-consuming for teachers when the course is attended by a great number of students. It is also time-consuming to create exercises for students to solve. Therefore, numerous researchers have studied and developed automated question generators. However, not as much research have been conducted on automated feedback generators. Ideally, teachers would use a generator that could generate programming exercises with incorporated effective feedback. In this pilot study, a concept of generating effective and contextual feedback together with programming exercises is presented and evaluated. It was evaluated by first year Computer Science students who solved exercises by being provided with either contextual feedback or normal compiler error messages. The results suggest that contextual feedback is a good learning tool for novice programmers, but further research is required in order to present more solid conclusions.
Authors: Christensson, Gabriel, Binett, Alexander
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6990129901