The Empirical Research on Human Knowledge Processing in Natural Language Within Engineering Education

2017 ◽  
pp. 3-15 ◽  
Author(s):  
Stefan Svetsky ◽  
Oliver Moravcik
Keyword(s):  
Natural Language ◽  
Engineering Education ◽  
Empirical Research ◽  
Human Knowledge ◽  
Knowledge Processing

One Belt One Road

2020 ◽  
pp. 180-202
Author(s):  
Jiabin Zhu ◽  
Guoyang Zhang ◽  
Yaxin Huang
Keyword(s):  
International Students ◽  
Engineering Education ◽  
Empirical Research ◽  
Multiple Stakeholders ◽  
International Recognition ◽  
One Belt One Road ◽  
The One

In the context of the One Belt One Road (OBOR) initiative, Chinese engineering education has encountered new opportunities and possible challenges. This chapter starts with a synthesis of the overarching trends in Chinese engineering education, analyzing its overall strengths and weaknesses, particularly highlighting the critical impact of China’s membership in the Washington Accord on its engineering degrees’ international recognition and the relevant ongoing engineering education innovations. The chapter also points out the lack of empirical research in Chinese engineering education to support the development of Chinese engineering education. The chapter further zeroes in on the specific advantages, and drawbacks, in attracting international students, and reviews additional models for Chinese engineering education to “go global.” Specific suggestions for multiple stakeholders are proposed to facilitate Chinese engineering education going global.

Network engineering education: Prospects for the implementation of the Worldwide CDIO Initiative in nuclear areas

2020 ◽  
Vol 34 (6) ◽  
pp. 440-445
Author(s):  
Nikolay I. Geraskin ◽  
Andrey A. Krasnoborodko ◽  
Vasily B. Glebov
Keyword(s):  
Engineering Education ◽  
Empirical Research ◽  
Feasibility Study ◽  
Nuclear Industry ◽  
Nuclear Materials ◽  
Specialist Training ◽  
Educational Challenges ◽  
Safe Management ◽  
Network Engineering ◽  
Network Form

This article summarises the results of a preliminary feasibility study and the experience of implementing Conceive-Design-Implement-Operate (CDIO) ideas during 2016–2019 in the education of nuclear specialists. The study is a form of empirical research. The results and findings regarding implementation of the CDIO approach are presented in relation to the Russia–Kazakhstan network programme of specialist training for the nuclear industry of Kazakhstan. The authors conclude that CDIO ideas effectively promote solutions to educational challenges facing the nuclear industries of specific countries. Key findings are (a) that the network form of education is well-suited to the implementation of the CDIO approach and (b) that the principle of the safe management of nuclear materials should be considered in the context of engineering education.

Games and Exercises for Teaching and Research: Exploring How Learning Varies Based on Fidelity and Participant Experience

2018 ◽  
Author(s):  
Erica Gralla ◽  
Zoe Szajnfarber
Keyword(s):  
Engineering Education ◽  
Empirical Research ◽  
Real World ◽  
Ecological Validity ◽  
The Other ◽  
Future Research ◽  
High Fidelity ◽  
Experience Level ◽  
Teaching And Research ◽  
Participant Experience

It has long been recognized that games are useful in engineering education, and more recently they have also become a common setting for empirical research. Games are useful for both teaching and research because they mimic aspects of reality and require participants to reason within that realistic context, and they allow researchers to study phenomena empirically that are hard to observe in reality. This paper explores what can be learned by students and by researchers, based on the authors’ experience with two sets of games. These games vary in both the experience level of the participants and the “fidelity” or realism of the game itself. Our experience suggests that what can be learned by participants and by researchers depends on both these dimensions. For teaching purposes, inexperienced participants may struggle to connect lessons from medium-fidelity games to the real world. On the other hand, experienced participants may learn more from medium-fidelity games that provide the time and support to practice and reflect on new skills. For research purposes, high-fidelity games are best due to their higher ecological validity, even with inexperienced participants, although experienced participants may enable strong validity in medium-fidelity settings. These findings are based on experience with two games, but provide promising directions for future research.

Artificial Intelligence, Smart Assistants and the Role of Language Professionals

2017 ◽  
Vol 62 (2) ◽  
Author(s):  
François Massion
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Human Knowledge ◽  
Long Tail ◽  
Knowledge Resources

AbstractThe article looks at the mechanisms of Artificial Intelligence related to the processing of human knowledge and natural language from a multilingual perspective. Undoubtedly, AI has made very impressing progresses in these areas, but they are unsatisfactory when it comes to what is called “the long tail”, i. e. the interpretation of less frequent words or concepts. In addition, AI has deficiencies when the context plays an important role, which is often the case. Most of the knowledge resources and methods actually used by AI have not been modelled to take multilingual and multicultural aspects into consideration. The article describes these issues and suggests some remedies, opening new opportunities for translators and interpreters.

APL: A Natural Language for Engineering Education?

1971 ◽  
Vol 14 (4) ◽  
pp. 174-185 ◽  
Author(s):  
Garth H. Foster
Keyword(s):  
Natural Language ◽  
Engineering Education

scholarly journals Finding a Common Ground in Human and Machine-Based Text Processing

2018 ◽  
Vol 5 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Роман Тарабань ◽  
Кодуру Лакшмоджі ◽  
Марк ЛаКур ◽  
Філіп Маршалл
Keyword(s):  
Natural Language ◽  
Engineering Education ◽  
Expressive Writing ◽  
Extraction Method ◽  
Engineering Students ◽  
Latent Dirichlet Allocation ◽  
Annual Conference ◽  
Ethics Course ◽  
American Society ◽  
Dirichlet Allocation

Language makes human communication possible. Apart from everyday applications, language can provide insights into individuals’ thinking and reasoning. Machine-based analyses of text are becoming widespread in business applications, but their utility in learning contexts are a neglected area of research. Therefore, the goal of the present work is to explore machine-assisted approaches to aid in the analysis of students’ written compositions. A method for extracting common topics from written text is applied to 78 student papers on technology and ethics. The primary tool for analysis is the Latent Dirichlet Allocation algorithm. The results suggest that this machine-based topic extraction method is effective and supports a promising prospect for enhancing classroom learning and instruction. The method may also prove beneficial in other applied applications, like those in clinical and counseling practice. References Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022. Bruner, J. (1990). Acts of meaning. Cambridge, MA: Harvard University Press. Chen, K. Y. M., & Wang, Y. (2007). Latent dirichlet allocation. http://acsweb.ucsd.edu/~yuw176/ report/lda.pdf. Chung, C. K., & Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132. Feldman, S. (1999). NLP meets the Jabberwocky: Natural language processing in information retrieval. Online Magazine, 23, 62-73. Retrieved from: http://www.onlinemag.net/OL1999/ feldmann5.html Mishlove, J. (2010). https://www.youtube.com/watch?v=0XTDLq34M18 (Accessed June 12, 2018). Ostrowski, D. A. (2015). Using latent dirichlet allocation for topic modelling in twitter. In Semantic Computing (ICSC), 2015 IEEE International Conference (pp. 493-497). IEEE. Pennebaker, J. W. (2004). Theories, therapies, and taxpayers: On the complexities of the expressive writing paradigm. Clinical Psychology: Science and Practice, 11(2), 138-142. Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin. Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., & Beaver, D. I. (2014). When small words foretell academic success: The case of college admissions essays. PLoS ONE, 9(12), e115844. Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296-1312. Recchia, G., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Computational intelligence and neuroscience, 2015, 1-18. Salzmann, Z. (2004). Language, Culture, and Society: An Introduction to Linguistic Anthropology (3rd ed). Westview Press. Schank, R. C., Goldman, N. M., Rieger III, C. J., & Riesbeck, C. (1973). MARGIE: Memory analysis response generation, and inference on English. In IJCAI, 3, 255-261. Taraban, R., Marcy, W. M., LaCour Jr., M. S., & Burgess II, R. A. (2017). Developing machine-assisted analysis of engineering students’ ethics course assignments. Proceedings of the American Society of Engineering Education (ASEE) Annual Conference, Columbus, OH. https://www.asee.org/public/conferences/78/papers/19234/view. Taraban, R., Marcy, W. M., LaCour, M. S., Pashley, D., & Keim, K. (2018). Do engineering students learn ethics from an ethics course? Proceedings of the American Society of Engineering Education – Gulf Southwest (ASEE-GSW) Annual Conference, Austin, TX. http://www.aseegsw18.com/papers.html. Taraban, R., & Marshall, P. H. (2017). Deep learning and competition in psycholinguistic research. East European Journal of Psycholinguistics, 4(2), 67-74. Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. Winograd, T. (1972). Understanding natural language. New York: Academic Press.

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