AI-assisted programming question generation

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

Datos Bibliográficos

ID21743927
AutoresCheng-Yu Chung (0000-0002-1258-9380, Arizona State University, autor de correspondencia), 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)
Año2023
Volumen55
Número1
Páginas94-110
Fecha de publicación2023-01-03
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of Research on Technology in Education (JOURNAL)
Identificadores de la revistaISSN: 1539-1523 • E-ISSN: 1945-0818
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/15391523.2022.2123872
OpenAlexW4296701967
IdiomaEN
Citas recibidas1
Referencias 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