CONSTRUCTIVISM IN ONLINE AND HYBRID LEARNING BEFORE AND AFTER COVID-19: A SYSTEMATIC LITERATURE REVIEW

Faiz Rizqullah Pratama, Harry Budi Santoso, Kasiyah Junus, Jahns Michael, Ilma Alpha Mannix, Hisyam Athaya

Abstract


Purpose – Accelerated by the COVID-19 epidemic, the move to online and hybrid learning settings has underlined the need for constructivist ideas in contemporary education. Digital education methods fit well with constructivism, which stresses active, student-centred learning via interaction and teamwork. This study explores the evolving application of constructivism in online and hybrid learning environments before and after the pandemic, identifying key opportunities, challenges, and factors influencing its effectiveness.

Methodology – Covering 2016 to 2024, the article used a Systematic Literature Review (SLR) methodology using the Kitchenham technique. Literature selection guided by the PICOC framework, Boolean search techniques, and quality evaluations led to 69 chosen papers, including three primary phases: planning, execution, and reporting.

Findings – The study discovered that online education employing Learning Management Systems (LMS), virtual reality (VR), and collaborative technologies has progressively integrated constructivism. These technologies enable peer cooperation, inquiry-based learning, and problem-based learning. The study revealed continuous problems despite these advances, including technological challenges, instructor readiness, and student participation. The efficacy of the constructivist method was strongly affected by social and technical factors like access to technology and collaboration dynamics.

Contribution – The research provides an insightful analysis of how constructivism changes to fit the evolving educational environment. It gives teachers, legislators, and technology creators strategic ideas to create engaging, efficient, and inclusive digital-era learning environments.

Keywords


Blended Learning; Constructivism; COVID-19 Pandemic; Hybrid Learning; Online Learning

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References


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DOI: https://doi.org/10.36987/jes.v12i4.7314

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