GEOSPATIAL DEEP LEARNING: A CONTEXTUAL-CONSTRUCTIVE APPROACH TO GEOGRAPHY LEARNING TO EMPOWER SPATIAL THINKING SKILLS STUDENTS - Singgih Prihadi; Chatarina Muryani; Sugiyanto Sugiyanto; Rita Noviani; Imasti Dhani Pratiwi (Universitas Sebelas Maret (UNS), 2026)
Geography education in the digital native era requires students to develop critical, creative, and innovative thinking through a spatial approach. Learning geography is not just about learning theory, but about learning deeply by optimising technology and geospatial data. This study aims to analyse the concepts and characteristics of Geospatial Deep Learning in geography education and to analyse the potential of Geospatial Deep Learning in empowering spatial thinking skills. This study employs a qualitative descriptive approach, utilising 10 informants of geography teachers and vice principals of senior high schools in Surakarta City. The sampling technique used is purposive sampling, as informants were selected based on the researcher's considerations. Data collection techniques include in-depth interviews and observations of teaching practices. The researcher employs an interactive data analysis model. The validity of the data used was triangulated by source and method. The research findings indicate that Geospatial Deep Learning in the context of high school geography education in Surakarta has already utilised geospatial data to enhance students' spatial thinking skills, though not yet to its full potential. Teachers have endeavoured to help students use data from Google Earth to strengthen their analysis. The potential of Geospatial Deep Learning in geography learning is significant, particularly in developing spatial thinking skills. Teachers noticed an improvement in spatial representation, which is evident in students' ability to visualise and manipulate spatial data in a more interesting, interactive, and informative way.