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NLP/AI Based Techniques for Programming Exercises Generation

Authors Tiago Carvalho Freitas, Alvaro Costa Neto , Maria João Varanda Pereira , Pedro Rangel Henriques



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Author Details

Tiago Carvalho Freitas
  • ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal
Alvaro Costa Neto
  • Instituto Federal de Educação, Ciência e Tecnologia de São Paulo, Barretos, Brazil
Maria João Varanda Pereira
  • Research Centre in Digitalization and Intelligent Robotics, Polythechnic Insitute of Bragança, Portugal
Pedro Rangel Henriques
  • ALGORITMI Research Centre/LASI, University of Minho, Braga, Portugal

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Tiago Carvalho Freitas, Alvaro Costa Neto, Maria João Varanda Pereira, and Pedro Rangel Henriques. NLP/AI Based Techniques for Programming Exercises Generation. In 4th International Computer Programming Education Conference (ICPEC 2023). Open Access Series in Informatics (OASIcs), Volume 112, pp. 9:1-9:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/OASIcs.ICPEC.2023.9

Abstract

This paper focuses on the enhancement of computer programming exercises generation to the benefit of both students and teachers. By exploring Natural Language Processing (NLP) and Machine Learning (ML) methods for automatic generation of text and source code, it is possible to semi-automatically construct programming exercises, aiding teachers to reduce redundant work and more easily apply active learning methodologies. This would not only allow them to still play a leading role in the teaching-learning process, but also provide students a better and more interactive learning experience. If embedded in a widely accessible website, an exercises generator with these Artificial Intelligence (AI) methods might be used directly by students, in order to obtain randomised lists of exercises for their own study, at their own time. The emergence of new and increasingly powerful technologies, such as the ones utilised by ChatGPT, raises the discussion about their use for exercise generation. Albeit highly capable, monetary and computational costs are still obstacles for wider adoption, as well as the possibility of incorrect results. This paper describes the characteristics and behaviour of several ML models applied and trained for text and code generation and their use to generate computer programming exercises. Finally, an analysis based on correctness and coherence of the resulting exercise statements and complementary source codes generated/produced is presented, and the role that this type of technology can play in a programming exercise automatic generation system is discussed.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Computer science education
  • Software and its engineering → Imperative languages
  • Computing methodologies → Machine learning
  • Software and its engineering → Parsers
Keywords
  • Natural Language Processing
  • Computer Programming Education
  • Exercises Generation
  • Text Generation
  • Code Generation

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