Model United Nations and Deep Learning: Theoretical and Professional Learning

Article Properties
Cite
Engel, Susan, et al. “Model United Nations and Deep Learning: Theoretical and Professional Learning”. Journal of Political Science Education, vol. 13, no. 2, 2017, pp. 171-84, https://doi.org/10.1080/15512169.2016.1250644.
Engel, S., Pallas, J., & Lambert, S. (2017). Model United Nations and Deep Learning: Theoretical and Professional Learning. Journal of Political Science Education, 13(2), 171-184. https://doi.org/10.1080/15512169.2016.1250644
Engel S, Pallas J, Lambert S. Model United Nations and Deep Learning: Theoretical and Professional Learning. Journal of Political Science Education. 2017;13(2):171-84.
Journal Category
Political science
Refrences
Title Journal Journal Categories Citations Publication Date
About Simulations, Games and Role Play in University Education 2012
Teaching for Quality Learning at University: What the Student Does 2011
International Relations Theory and the UN: A Short Primer 2015
The Purpose of Intervention: Changing Beliefs About the Use of Force 2003
Using Simulations to Promote Learning in Higher Education: An Introduction 2002
Citations
Title Journal Journal Categories Citations Publication Date
Model Arctic Council for sustainable development Polar Geography
  • Geography. Anthropology. Recreation: Geography (General)
 2023
Survival!: A Portable Simulation That Encourages Failure Journal of Political Science Education
  • Political science
 2023
Training for the United Nations in the Twenty-First Century; Professionalism Training on Leadership, Negotiation, and Gender for Model United Nations Simulations

 International Studies Perspectives
  • Political science: Political institutions and public administration (General)
  • Political science: International relations
  • Social Sciences
 2023
Inquiry-based learning as an adaptive signature pedagogy in international relations

 International Studies Perspectives
  • Political science: Political institutions and public administration (General)
  • Political science: International relations
  • Social Sciences
 2023
Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education

 Sustainability
  • Technology: Mechanical engineering and machinery: Renewable energy sources
  • Geography. Anthropology. Recreation: Environmental sciences
  • Geography. Anthropology. Recreation: Environmental sciences
  • Geography. Anthropology. Recreation: Environmental sciences
  • Technology: Environmental technology. Sanitary engineering
  • Science: Biology (General): Ecology
 2023
Citations Analysis
Category Category Repetition
Political science9
Social Sciences6
Political science: Political institutions and public administration (General)2
Political science: International relations2
Technology: Mechanical engineering and machinery: Renewable energy sources1
Geography. Anthropology. Recreation: Environmental sciences1
Technology: Environmental technology. Sanitary engineering1
Science: Biology (General): Ecology1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
Science: Science (General): Cybernetics: Information theory1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
Education: Theory and practice of education1
Education1
Medicine: Internal medicine: Special situations and conditions: Industrial medicine. Industrial hygiene1
Medicine: Public aspects of medicine1
Geography. Anthropology. Recreation: Geography (General)1
Education: Education (General)1
Language and Literature: English language1
The category Political science 9 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Active Learning in Large Graduate Classes: Reflections on an “Attaining Citizenship” Simulation and was published in 2018. The most recent citation comes from a 2023 study titled Inquiry-based learning as an adaptive signature pedagogy in international relations. This article reached its peak citation in 2019, with 7 citations. It has been cited in 14 different journals, 14% of which are open access. Among related journals, the Journal of Political Science Education cited this research the most, with 8 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year