Identifying typical approaches and errors in Prolog programming with argument-based machine learning

作者：

Highlights：

• Abstract-syntax-tree (AST) patterns as attributes for classifying Prolog programs.

• Identification of AST patterns for detecting errors and programming approaches.

• An argument-based algorithm for learning rules suitable for tutoring.

• Evaluation of extracted patterns and rules on 42 Prolog exercises.

摘要

•Abstract-syntax-tree (AST) patterns as attributes for classifying Prolog programs.•Identification of AST patterns for detecting errors and programming approaches.•An argument-based algorithm for learning rules suitable for tutoring.•Evaluation of extracted patterns and rules on 42 Prolog exercises.

论文关键词：Argument-based machine learning,Rule learning,Programming tutors,Abstract syntax tree,Syntactic patterns

论文评审过程：Received 3 January 2018, Revised 30 April 2018, Accepted 11 June 2018, Available online 15 June 2018, Version of Record 26 June 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.06.029