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UID:pretalx-qgis-uc2026-MQLP7T@talks.osgeo.org
DTSTART;TZID=CET:20261006T093000
DTEND;TZID=CET:20261006T100000
DESCRIPTION:How can initially reluctant beginners become engaged\, self-mot
 ivated users of QGIS? This presentation introduces a successful\, research
 -informed workflow that uses learner-generated\, field-based data as an ac
 cessible entry point into GIS thinking.\n\nThis QGIS case study presents a
 n effective educational framework to introduce new digital tools to learne
 rs who may not be predisposed toward their adoption and to student groups 
 with diverse technical and disciplinary backgrounds. Many students are mor
 e familiar with proprietary ecosystems and with the consumption\, rather t
 han creation\, of data. Under these conditions\, conventional tutorials us
 ing scaffolded concepts and preconfigured datasets are less engaging and p
 edagogically ineffective.\n \nTo address these challenges\, we use a “Fi
 ndings from the Field” approach that inverts the traditional tutorial mo
 del. Rather than beginning with abstract concepts and standardized dataset
 s\, students first identify a topic of personal or collective interest and
  gather geotagged data using mobile photography. Working individually or c
 ollaboratively\, they document phenomena encountered outside the classroom
  that interest them\, such as public infrastructure\, creative works\, or 
 commercial artifacts. Students are then introduced to core QGIS functions 
 as they import their geotagged photographs to create point layers and asso
 ciated attribute tables\; they then use the Map Tips feature to display th
 e images interactively. Because the dataset originates from student fieldw
 ork\, this process supports deeper reflection on connecting their interest
 s to geocoding practices\, spatial representation\, analysis\, and workflo
 ws.\n \nThis approach’s relative simplicity makes it an effective entry 
 point for both instructors and novice learners\, as well as a foundation f
 or further exploration\, while simultaneously reinforcing critical\, place
 -based research skills. Beyond the case study\, this presentation contribu
 tes to discussions on inclusive and interdisciplinary GIS pedagogy\, parti
 cularly in contexts with learners initially unaware of or disinterested in
  geospatial analysis and representation. The workflow we present is ground
 ed in classic learning theories that address motivation and skill acquisit
 ion: Self-Determination Theory explains how student-generated data fosters
  intrinsic motivation through autonomy and relevance (Ryan & Deci\, 2000)\
 ; Cognitive Load Theory informs how simplifying initial technical tasks su
 pports learning (Sweller\, Ayres\, & Kalyuga\, 2011)\; and the Zone of Pro
 ximal Development concept (Vygotsky\, 1978) guides the structured introduc
 tion of QGIS tools for deeper exploration. At a broader level\, the approa
 ch aligns with experiential and constructionist learning theories\, as stu
 dents build knowledge through field-based inquiry and the creation of mean
 ingful artifacts (Healey & Jenkins\, 2000).\n\nReferences/resources\nHeale
 y\, M.\, & Jenkins\, A. (2000). Kolb’s Experiential Learning Theory and 
 Its Application in Geography in Higher Education. Journal of Geography\, 9
 9(5)\, 185–195. https://doi.org/10.1080/00221340008978967\nRyan\, R. M.\
 , & Deci\, E. L. (2000). Intrinsic and extrinsic motivations: Classic defi
 nitions and new directions. Contemporary Educational Psychology\, 25(1)\, 
 54–67. https://doi.org/10.1006/ceps.1999.1020\nSweller\, J.\, Ayres\, P.
 \, & Kalyuga\, S. (2011). Cognitive load theory. Springer.\nVygotsky\, L. 
 S. (1978). Mind in society: The development of higher psychological proces
 ses. Harvard University Press.
DTSTAMP:20260724T230554Z
LOCATION:Bridge Cinema
SUMMARY:Introducing QGIS effectively with learner-generated data: A workflo
 w based on educational research for teaching QGIS to initially reluctant b
 eginners - Julian Kilker\, Yvonne Houy
URL:https://talks.osgeo.org/qgis-uc2026/talk/MQLP7T/
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