Mega Reskyanti
Universitas Negeri Makassar

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The Interactive Technology-Based Engagement Model (ITEM): Optimizing Geo-TPACK and Spatial Cognition in Secondary Distance Learning Mega Reskyanti; Firdaus; Nur Wahyuni Nengsi
Future Space: Studies in Geo-Education Vol. 3 No. 3 (2026): Future Space: Studies in Geo-Education
Publisher : CV Bumi Spasial

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69877/fssge.v3i3.149

Abstract

While technology-mediated distance education expands access, secondary geography instruction persistently grapples with diminished student engagement and superficial spatial processing. This study aims to develop and empirically validate the Interactive Technology-Based Engagement Model (ITEM), an integrative framework engineered to optimize Geo-TPACK and instructional effectiveness in distance geography learning. Grounded in a Design-Based Research (DBR) paradigm, a sequential three-phase methodology was executed: (1) a PRISMA-guided systematic review of 47 peer-reviewed articles (2017–2024); (2) expert panel validation (n = 6, CVI = 0.91); and (3) a quasi-experimental pre-test–post-test pilot trial involving 42 Grade X students at SMAN 11 Maros, Indonesia. Thematic synthesis established four critical success factors: pedagogically aligned instructional design, multi-platform integration, participatory content delivery, and continuous formative feedback. Pilot testing demonstrated statistically significant enhancements in overall student engagement (d = 0.78, p < .001), with cognitive engagement yielding the most pronounced gain (d = 0.84), alongside substantial improvements in geography learning outcomes (d = 0.65, p < .001). Unlike generic e-learning models, the ITEM framework uniquely synthesizes constructivist geo-pedagogy with an equity-driven, offline-compatible contingency pathway designed for topographically constrained and broadband-blind regions. It provides a theoretically grounded blueprint for transforming passive distance instruction into interactive, context-responsive spatial learning.