Vector Learning in Vocational and Engineering Education: A Systematic Review, Narrative Synthesis, and Limited Meta-Analysis Using JASP - Denal Mulia; Fadhilah; Dedy Irfan (Postgraduate, University of Mataram, 2025)
Vector learning is a fundamental component of physics and engineering but is often perceived as difficult due to its abstract concepts. This study aims to map publication trends, instructional models, and the effectiveness of vector learning from 2016 to 2025 through a systematic literature review and limited meta-analysis. Twenty-one articles from Scopus, Sinta 1–2, and DOAJ were analyzed using the PRISMA protocol. Seven quantitative studies were synthesized through meta-analysis using JASP, while fourteen were reviewed narratively. The findings show a steady increase in publications, with notable peaks in 2018 and 2020. Problem-based learning and augmented reality emerged as the most widely implemented instructional approaches. The pooled effect size indicates a large impact (d = 1.01), with augmented reality (d = 0.74) and blended learning (d = 0.62) showing high effectiveness. Narrative synthesis highlights conceptual difficulties, ongoing media innovation, and the development of supporting teaching materials. The study concludes that vector learning research remains active but requires further exploration in AI integration, web-based gamification, and long-term retention assessment.