Problem-Based Learning Models in Computer Science Education Supported by Digital Platforms: A Systematic Literature Review

Abstract

Computer science education requires strong problem-solving skills, algorithmic thinking, and collaboration. Problem-Based Learning (PBL) is widely regarded as a relevant pedagogical approach to address these demands, particularly when supported by digital platforms that enable collaborative, exploratory, and contextual learning. This study aims to synthesize PBL models in computer science education supported by digital platforms, identify the roles of digital platforms in facilitating PBL, and evaluate its impacts and implementation challenges. A Systematic Literature Review with a qualitative synthesis approach was conducted on peer-reviewed journal articles and conference proceedings published between 2014 and 2024. A total of 36 studies were included and analyzed through thematic analysis and narrative synthesis. The findings indicate that digital PBL is implemented through several dominant patterns, including full PBL, hybrid PBL, and project-based PBL, with a strong emphasis on authentic problems reflecting real-world computing practices. Digital platforms function as pedagogical mediators that support collaboration, solution exploration, and reflective learning. Although digital PBL demonstrates positive effects on problem-solving skills, motivation, and collaboration, its effectiveness is highly dependent on pedagogical alignment, instructor readiness, and students’ digital literacy.