2018/08/27 by Rodrigo Pessoa Medeiros, Geber Ramalho, Geber Lisboa Ramalho +2 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Psychology · #Active learning (machine learning) #Artificial intelligence #Biomedical and Engineering Education #Categorization #Computer science #Context (archaeology) #Curriculum #Genetics, Bioinformatics, and Biomedical Research #Mathematics education #Pedagogy #Psychology #Syntax #Teaching and Learning Programming
paper · doi:10.1109/te.2018.2864133
openalex publication_date 2018/08/27 · crossref created 2018/08/27 · crossref issued 2019/05/01 · crossref published 2019/05/01 · crossref published-print 2019/05/01 · crossref deposited 2022/07/13 · openalex created_date 2025/10/10 · crossref indexed 2026/08/05 · openalex updated_date 2026/08/05
Contribution: This paper adds to the results of previous systematic literature reviews by addressing a more contemporary context of introductory programming. It proposes a categorization of introductory programming challenges, and highlights key issues for a research roadmap on introductory programming learning and teaching in higher education. Background: Despite the advances in methods and tools for teaching and learning introductory programming, dropout and failure rates are still high. Published surveys and reviews either cover papers only up to 2007, or focus on methods and tools for teaching introductory programming. Research Questions: 1) What previous skills and background knowledge are key for a novice student to learn programming? 2) What difficulties do novice students encounter in learning how to program? 3) What challenges do teachers encounter in teaching introductory programming? Methodology: Following a formal protocol, automatic and manual searches were performed for work from 2010 to 2016. Of 100 papers selected for data extraction, 89 were retained after quality assessment. Findings: The most frequently cited skills necessary for learning programming were related to problem solving and mathematical ability. Problem solving was also cited as a learning challenge, followed by motivation and engagement, and difficulties in learning the syntax of programming languages. The main teaching challenges concern the lack of appropriate methods and tools, as well as scaling and personalized teaching.