AI-assisted programming question generation

Constructing semantic networks of programming knowledge by local knowledge graph and abstract syntax tree

Dados Bibliográficos

ID21743927
AutoresCheng-Yu Chung (0000-0002-1258-9380, Arizona State University, autor correspondente), I-Hsin Hsiao (0000-0002-1888-3951, Santa Clara University), Yi-Ling Lin (0000-0003-4555-697X, National Chengchi University, Taipei, Taiwan), Yiling Lin (0000-0003-4955-4146, National Chengchi University)
Ano2023
Volume55
Fascículo1
Páginas94-110
Data de publicação2023-01-03
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoJournal of Research on Technology in Education (JOURNAL)
Identificadores do periódicoISSN: 1539-1523 • E-ISSN: 1945-0818
EditoraInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15391523.2022.2123872
OpenAlexW4296701967
IdiomaEN
Citações recebidas1
Referências citadas23

Creating practice questions for programming learning is not an easy job. It requires the instructor to diligently organize heterogeneous learning resources. Although educational technologies have been adopted across levels of programming learning, programming question generation (PQG) is still predominantly performed by instructors without advanced technological support. This study proposes a knowledge-based PQG model that aims to help the instructor generate new programming questions and expand the assessment items by the Local Knowledge Graph and Abstract Syntax Tree. A group of experienced instructors was recruited to evaluate the PQG model and expressed significantly positive feedback on the generated questions

Graph · Inductive programming · Knowledge graph · Programming language · Programming paradigm · Syntax · Computer Science · Software Engineering Research · Teaching and Learning Programming · Topic Modeling · Artificial Intelligence · Theoretical Computer Science