Designing and Building a Platform for Teaching Introductory Programming supported by Large Language Models

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Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis

Date

2024-01-22

Department

Major/Subject

Security and Cloud Computing

Mcode

SCI3084

Degree programme

Master’s Programme in Computer, Communication and Information Sciences

Language

en

Pages

77

Series

Abstract

Large language models (LLMs) have the potential to improve programming education by providing feedback and guidance to students. Despite their potential benefits, the integration of LLMs into education presents unique challenges, including the risk of over-reliance on their feedback and the inconsistency of feedback quality. Addressing these concerns requires research to identify effective ways of integrating LLMs into programming education, which itself is challenging due to the rapid evolution of LLMs. To meet this challenge, this thesis introduces a flexible platform that can integrate multiple LLMs, providing an experimental space for research and innovative approaches to enhance programming education through LLMs. Guided by the Design Science Research Methodology framework, the thesis outlines the design, development, and evaluation of this educational platform. Conducted at Aalto University’s LeTech research group, the thesis presents an introductory programming learning platform specifically tailored to the group’s research objectives. The platform facilitates data collection, and enables the students to have a personalized learning experience with the help of LLM feedback. The work advances our understanding of LLMs in education and feedback mechanisms’ importance. The developed platform effectively demonstrates the feasibility of integrating LLMs into programming education. A small-scale study evaluating the platform’s overall usability received an average rating of 4.21 out of 5.00, while the LLM feedback received an average usefulness rating of 4.28 out of 5.00, highlighting its effectiveness and value in assisting students. Though the study sample size was small, the findings are encouraging. Future research could use the platform to explore multiple LLMs and conduct studies to improve the feedback mechanisms.

Description

Supervisor

Hellas, Arto

Thesis advisor

Koutcheme, Charles

Keywords

large language models, programming education, feedback, interactive learning environment

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Citation