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Enhancing Learning Outcomes in Dynamic Electricity through Problem-Based Learning Supported by Virtual Labs

Authors:
Mentari Putri Momongan, Rolles Nixon Palilingan, Jovialine Albertine Rungkat, H. Tanaumang, M. Rengkuan, Tiurlina Siregar

Addresses:
Department of Science Education Study Program, Manado State University, Tondano, North Sulawesi, Indonesia.

Abstract:

Dynamic Electricity Material Student Learning Outcomes Using the Problem-Based Learning Model and Virtual Lab. Postgraduate Science Education Study Program, Manado State University. This study examines the effects of Problem-Based Learning (PBL), Virtual Lab, and their combination on student learning outcomes in dynamic electricity content. The study used a quantitative quasi-experimental design at SMK Negeri 1 Langowan. The study sample included four treatment groups: control, PBL, Virtual Lab, and PBL integrated with Virtual Lab. Multiple-choice exams with established validity and reliability provided learning-outcome data. At the 0.05 significance level, researchers used descriptive statistics, normality testing, homogeneity testing, one-way ANOVA, and Tukey's post hoc tests to analyze the data. All study data were normally distributed and homogeneous. The control class averaged 69.50 posttest scores, the PBL class 80.50, the Virtual Lab class 81.50, and the PBL integrated Virtual Lab class 86.75. The ANOVA test revealed significant differences between treatment groups (Sig. = 0.000 < 0.05). The Tukey additional test demonstrated that PBL, Virtual Lab, and integration models outperformed conventional learning. The PBL-integrated Virtual Lab class achieved the highest learning outcomes and was significantly different from both the PBL and Virtual Lab classes. The findings show that the Problem-based learning paradigm, when linked with Virtual Lab, improved student learning outcomes in dynamic electricity material, making it an excellent learning alternative.

Keywords: Problem-Based Learning; Virtual Lab; Learning Outcomes; Dynamic Electricity Material; Conventional Learning; Integration Model; Normality Testing.

Received on: 25/02/2025, Revised on: 12/04/2025, Accepted on: 03/07/2025, Published on: 05/06/2026

DOI: 10.69888/FTSHS.2026.000759

FMDB Transactions on Sustainable Humanities and Society, 2026 Vol. 3 No. 2, Pages: 74-86

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