A Review of AI-Supported Tutoring Approaches for Learning Programming

Le, Nguyen-Thinh; Strickroth, Sven; Groß, Sebastian; Pinkwart, Niels

doi:10.1007/978-3-319-00293-4_20

Cited by 32 publications

(19 citation statements)

References 11 publications

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“…Nevertheless, the feedback provided by Jutge.org (see Sections 3 and 4) supports self-learning of programming up to a certain extent. In contrast, various feedback-based AIsupported tutoring approaches exist [4,15,24,32]. Most of them use AI techniques to give examples, provide simulations, promote incremental or stage by stage programming and facilitate dialogues between students and human tutors.…”

Section: Comparison To Related Workmentioning

confidence: 99%

<uri>Jutge.org</uri>: Characteristics and Experiences

Petit

Roura

Carmona

et al. 2018

IEEE Trans. Learning Technol.

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Jutge.org is an open educational online programming judge designed for students and instructors, featuring a repository of problems that is well organized by courses, topics and difficulty. Internally, Jutge.org uses a secure and efficient architecture and integrates modern verification techniques, formal methods, static code analysis and data mining. Jutge.org has exhaustively been used during the last decade at the Universitat Politècnica de Catalunya to strengthen the learning-by-doing approach in several courses. This paper presents the main characteristics of Jutge.org and shows its use and impact on a wide range of courses covering basic programming, data structures, algorithms, artificial intelligence, functional programming and circuit design.

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Section: Comparison To Related Workmentioning

confidence: 99%

<uri>Jutge.org</uri>: Characteristics and Experiences

Petit

Roura

Carmona

et al. 2018

IEEE Trans. Learning Technol.

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“…We have found almost twenty reviews of tools for learning programming, mostly on automated assessment (AA) tools [1,3,6,10,20,21,23] or learning environments for programming [4,5,7,9,11,13,14,18,19,24]. Generating feedback is important for both kinds of tools.…”

Section: Related Workmentioning

confidence: 99%

Towards a Systematic Review of Automated Feedback Generation for Programming Exercises

Keuning

Jeuring

Heeren

2016

Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education

120

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Formative feedback, aimed at helping students to improve their work, is an important factor in learning. Many tools that offer programming exercises provide automated feedback on student solutions. We are performing a systematic literature review to find out what kind of feedback is provided, which techniques are used to generate the feedback, how adaptable the feedback is, and how these tools are evaluated. We have designed a labelling to classify the tools, and use Narciss' feedback content categories to classify feedback messages. We report on the results of the first iteration of our search in which we coded 69 tools. We have found that tools do not often give feedback on fixing problems and taking a next step, and that teachers cannot easily adapt tools to their own needs.

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“…A recent overview by Le et al (2013) describes the various AI-supported approaches that have been used in programming tutors. Striewe and Goedicke (2014) review one such approach in detail: static analysis for diagnosing student programs.…”

Section: Related Workmentioning

confidence: 99%

Ask-Elle: an Adaptable Programming Tutor for Haskell Giving Automated Feedback

Gerdes

Heeren

Jeuring

et al. 2016

Int J Artif Intell Educ

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Ask-Elle is a tutor for learning the higher-order, strongly-typed functional programming language Haskell. It supports the stepwise development of Haskell programs by verifying the correctness of incomplete programs, and by providing hints. Programming exercises are added to Ask-Elle by providing a task description for the exercise, one or more model solutions, and properties that a solution should satisfy. The properties and model solutions can be annotated with feedback messages, and the amount of flexibility that is allowed in student solutions can be adjusted.The main contribution of our work is the design of a tutor that combines (1) the incremental development of di↵erent solutions in various forms to a programming exercise with (2) automated feedback and (3) teacher-specified programming exercises, solutions, and properties. The main functionality is obtained by means of strategy-based model tracing and property-based testing. We have tested the feasibility of our approach in several experiments, in which we analyse both intermediate and final student solutions to programming exercises, amongst others.

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A Review of AI-Supported Tutoring Approaches for Learning Programming

Cited by 32 publications

References 11 publications

<uri>Jutge.org</uri>: Characteristics and Experiences

<uri>Jutge.org</uri>: Characteristics and Experiences

Towards a Systematic Review of Automated Feedback Generation for Programming Exercises

Ask-Elle: an Adaptable Programming Tutor for Haskell Giving Automated Feedback

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