Patrick D. Hagge Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
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Biography and Research Information
OverviewAI-generated summary
Patrick D. Hagge's research investigates the integration of technology and evolving pedagogical methods within higher education, particularly in geography and geographic information systems (GIS) coursework. His work explores the use of machine learning with survey data to understand perceptual regions and evaluates the efficacy of virtual reality (VR) applications, such as 'Wooorld,' for geography lectures. Hagge has also examined student-generated mental maps and preference mappings of Arkansas counties, as well as spatial identification tasks in university classrooms. His publications address the challenges and potential of immersive technologies like Google Earth VR in educational settings. Furthermore, Hagge has investigated teaching strategies for GIS in non-GIS classrooms and the role of AI in addressing academic integrity concerns in AI-influenced educational environments.
Metrics
- h-index: 5
- Publications: 19
- Citations: 93
Selected Publications
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AI and GIS Education: The Last Holdout Against AI Cheating (For Now) (2025)
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To HyFlex or Not to HyFlex: Observations on Teaching Introduction to Geographic Information Systems in the HyFlex System (2024)
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Metaverse in in the geography lecture classroom? Evaluating ‘group VR’ possibilities using the multiplayer ‘Wooorld’ VR app (2024)
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The rise and stagnation of <i>Google Earth VR</i> : dashing the hopes of immersive geography classrooms? (2023)
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Ranking Arkansas: Student-Surveyed Mental Maps and Preference Mapping of Arkansas Counties, 2018-2021 (2023)
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GIS in the Non-GIS Classroom: Using Student Mapping Assignments to Incorporate GIS in Traditional Lecture Classes (2023)
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Find It on a Map: Country Location Identification in a University Geography Classroom, 2016–2022 (2023)
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Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions (2022)
Collaboration Network
Top Collaborators
- Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions
- Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions
- Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions
- Fusing machine learning with place-based survey methods: revisiting questions surrounding perceptual regions
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