Using Knowledge Building to Support Deep Learning, Collaboration and Innovation in Engineering Education

被引:0
作者
Ellis, Glenn W. [1 ]
Rudnitsky, Alan N. [1 ]
Moriarty, Mary A. [1 ]
机构
[1] Smith Coll, Northampton, MA 01063 USA
来源
2010 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE) | 2010年
关键词
deep learning; discourse; knowledge building; narrative; preparation for future learning;
D O I
暂无
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Knowledge building is a potentially transformative approach to engineering education. In knowledge building students participate in an interactive discourse in which they work together to broaden ideas, reform problems and share knowledge-the result being a deeper level of understanding and the collaborative production of new knowledge. In 2009 we conducted a knowledge building pilot study in the Picker Engineering Program at Smith College. In this study students worked together to formulate a question about the potential for a conscious machine and then engaged in an intensive knowledge building discourse. Assessment data showing the effectiveness of the approach and research questions arising from the study are presented.
引用
收藏
页数:5
相关论文
共 50 条
[41]   Building Engineering Cost Prediction Based On Deep Learning: Model Construction and Real - Time Optimization [J].
Zhang, Guoping ;
Zhang, Qiuyue .
JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (05) :176-+
[42]   Ensemble Learning of Lightweight Deep Learning Models Using Knowledge Distillation for Image Classification [J].
Kang, Jaeyong ;
Gwak, Jeonghwan .
MATHEMATICS, 2020, 8 (10)
[43]   RESEARCH ON INNOVATION OF DAILY IDEOLOGICAL AND POLITICAL EDUCATION FOR COLLEGE STUDENTS BASED ON DEEP LEARNING MODEL [J].
Zhang, Xianwei ;
Zhang, Yueyan .
3C TECNOLOGIA, 2023, 12 (01) :108-125
[44]   Diversified Curriculum Innovation for Japanese Language Education in Colleges and Universities under the Deep Learning Model [J].
Zhou R. .
Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
[45]   Effective Hypertension Detection Using Predictive Feature Engineering and Deep Learning [J].
Abbas, Sidra ;
Sampedro, Gabriel Avelino ;
Krichen, Moez ;
Alamro, Meznah A. ;
Mihoub, Alaeddine ;
Kulhanek, Rastislav .
IEEE ACCESS, 2024, 12 :89055-89068
[46]   An automatic prediction of students' performance to support the university education system: a deep learning approach [J].
Alshamaila, Yazn ;
Alsawalqah, Hamad ;
Aljarah, Ibrahim ;
Habib, Maria ;
Faris, Hossam ;
Alshraideh, Mohammad ;
Salih, Bilal Abu .
MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (15) :46369-46396
[47]   An automatic prediction of students’ performance to support the university education system: a deep learning approach [J].
Yazn Alshamaila ;
Hamad Alsawalqah ;
Ibrahim Aljarah ;
Maria Habib ;
Hossam Faris ;
Mohammad Alshraideh ;
Bilal Abu Salih .
Multimedia Tools and Applications, 2024, 83 :46369-46396
[48]   Construction of business innovation model for sports industry using a deep learning algorithm [J].
Lv, Chenchen ;
Wang, Yifeng ;
Ma, Yin .
SOFT COMPUTING, 2022, 26 (20) :10753-10763
[49]   Extracting users' ideas in open innovation community using deep learning methods [J].
Tang H. ;
Cai X. ;
Zhang Y. ;
Li Z. .
Zhang, Yanlin (forest_zhang@163.com), 1600, Systems Engineering Society of China (41) :2488-2500
[50]   Construction of business innovation model for sports industry using a deep learning algorithm [J].
Chenchen Lv ;
Yifeng Wang ;
Yin Ma .
Soft Computing, 2022, 26 :10753-10763