Art der Publikation: Beitrag in Sammelwerk

Learning to Program: Mapping Errors and Misconceptions of Python Novices to Support the Design of Intelligent Programming Tutors

Autor(en):

van der Heyden, Lisa; Batur, Fatma; Chounta, Irene Angelica

Titel des Sammelbands:

Proceedings of the 17th International Conference on Computer Supported Education - Volume 1: CSEDU

Seiten:

224-231

Verlag:

SciTePress

Ort(e):

Porto, Portugal

Veröffentlichung:

2025

ISBN:

978-989-758-746-7

Digital Object Identifier (DOI):

doi:10.5220/0013203100003932

Link zum Volltext:

https://www.scitepress.org/Link.aspx?doi=10.5220/0013203100003932

Zitation:

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Kurzfassung

Students often struggle with basic programming tasks after their first programming course. Adaptive tutoring systems can support students’ practice by generating tasks, providing feedback, and evaluating students’ progress in real-time. Here, we describe the first step for building such a system focusing on designing tasks that address common errors and misconceptions. To that end, we compiled a collection of Python tasks for novices. In particular, a) we identified errors occurring during introductory programming and mapped them to learning tasks; b) we conducted a survey to validate our mapping; c) we conducted semi-structured interviews with instructors to understand potential reasons for such errors and best practices for addressing them. Synthesizing our findings, we discuss the creation of a tasks’ corpus to serve as a basis for adaptive tutors. This work contributes to the standardization and systematization of computing education and provides insights regarding the design of learning tasks tailored to addressing errors