Work in Progress: Data-Rich Learning Environments for Engineering Education

被引:0
|
作者
DeLuca, V. William [1 ]
Clark, Aaron [1 ]
Ernst, Jeremy [1 ]
Lari, Nasim [2 ]
机构
[1] North Carolina State Univ, Dept STEM Educ, Raleigh, NC 27695 USA
[2] North Carolina State Univ, Res GRID Project, Raleigh, NC USA
关键词
Data-driven models; Renewable energy; STEM education; Critical thinking;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Green Research for Incorporating Data in the Classroom (GRID(C)) is a National Science Foundation project designed to improve instructional practices in the curricula areas of science, technology, engineering, and mathematics (STEM). The project uses data collected from renewable energy technologies at the NC Solar House, and enables students in engineering and education to analyze, synthesize, and evaluate downloadable data. Students and instructors create data-driven and conceptual models to explain information obtained from the project's website using a variety of methods involved in technical data presentation. This paper explains the GRIDC project and how students in engineering and pre-service technology, engineering and design teacher education develop higher-order thinking skills. Preliminary research has been conducted on the effective use of these materials in college level environmental engineering classes and technical animation courses in engineering graphics. This research provides a base for continued research and development on using data-rich learning environments to further develop higher-order thinking skills for students across the country.
引用
收藏
页数:3
相关论文
共 50 条
  • [1] Marketing Analytics for Data-Rich Environments
    Wedel, Michel
    Kannan, P. K.
    [J]. JOURNAL OF MARKETING, 2016, 80 (06) : 97 - 121
  • [2] Testing for Common Autocorrelation in Data-Rich Environments
    Cubadda, Gianluca
    Hecq, Alain
    [J]. JOURNAL OF FORECASTING, 2011, 30 (03) : 325 - 335
  • [3] Forecasting macroeconomic variables in data-rich environments
    Medeiros, Marcelo C.
    Vasconcelos, Gabriel F. R.
    [J]. ECONOMICS LETTERS, 2016, 138 : 50 - 52
  • [4] Spatial economic analysis in data-rich environments
    Bell, Kathleen P.
    Dalton, Timothy J.
    [J]. JOURNAL OF AGRICULTURAL ECONOMICS, 2007, 58 (03) : 487 - 501
  • [5] Voronoi Representation for Areal Data Processing in Data-rich Environments
    Breitkreutz, David
    Lee, Ickjai
    [J]. ISI: 2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS, 2009, : 167 - +
  • [6] Geospatial clustering in data-rich environments: Features and issues
    Lee, I
    [J]. KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 4, PROCEEDINGS, 2005, 3684 : 336 - 342
  • [7] A feedback model for data-rich learning experiences
    Pardo, Abelardo
    [J]. ASSESSMENT & EVALUATION IN HIGHER EDUCATION, 2018, 43 (03) : 428 - 438
  • [8] Fast Cluster Polygonization and its Applications in Data-Rich Environments
    Ickjai Lee
    Vladimir Estivill-Castro
    [J]. GeoInformatica, 2006, 10 : 399 - 422
  • [9] Fast cluster polygonization and its applications in data-rich environments
    Lee, Ickjai
    Estivill-Castro, Vladimir
    [J]. GEOINFORMATICA, 2006, 10 (04) : 399 - 422
  • [10] Key Information Processes for Thinking Critically in Data-Rich Environments
    Leighton, Jacqueline P.
    Cui, Ying
    Cutumisu, Maria
    [J]. FRONTIERS IN EDUCATION, 2021, 6