Step Tutor: Supporting Students through Step-by-Step Example-Based Feedback

By Wengran Wang, Yudong Rao, Rui Zhi, Samiha Marwan, Ge Gao, Thomas William Price. 2020

33 citations recorded by the source corpus.

Students often get stuck when programming independently, and need help to progress. Existing, automated feedback can help students progress, but it is unclear whether it ultimately leads to learning. We present Step Tutor, which helps struggling students during programming by presenting them with relevant, step-by-step examples. The goal of Step Tutor is to help students progress, and engage them in comparison, reflection, and learning. When a student requests help, Step Tutor adaptively selects an example to demonstrate the next meaningful step in the solution. It engages the student in comparing "before" and "after" code snapshots, and their corresponding visual output, and guides them to reflect on the changes. Step Tutor is a novel form of help that combines effective aspects of existing support features, such as hints and Worked Examples, to help students both progress and learn. To understand how students use Step Tutor, we asked nine undergraduate students to complete two programming tasks, with its help, and interviewed them about their experience. We present our qualitative analysis of students' experience, which shows us why and how they seek help from Step Tutor, and Step Tutor's affordances. These initial results suggest that students perceived that Step Tutor accomplished its goals of helping them to progress and learn.

Subfields: Artificial Intelligence, Computer Science Applications, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Paul Denny
2. Stephen MacNeil
3. Seth Bernstein
4. Andrew Tran

In Computer Science Applications

1. Paul Denny
2. Juho Leinonen
3. Sami Sarsa
4. Arto Hellas
5. Stephen MacNeil
6. Seth Bernstein
7. Andrew Tran
8. Joanne Kim

In Human-Computer Interaction

1. Sami Sarsa
2. Juho Leinonen
3. Arto Hellas
4. Paul Denny
5. Seth Bernstein
6. Andrew Tran
7. Stephen MacNeil
8. Joanne Kim

Citations in the corpus

• Comparing Code Explanations Created by Students and Large Language Models (2023, background): “The ability to understand and explain code is an important skill for computer science students to develop [7, 29, 44].”
• Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book (2022, background): “The ability to write and modify code, understand its purpose, and articulate its functionality are key skills that CS students must develop [7, 26, 42].”