Quantification of students’ active learning in design, build, and test engineering modules

Author:

Syed Jelena Milisavljevic1

Affiliation:

1. Cranfield University / University of Liverpool

Publisher

Liverpool University Press

Subject

General Medicine

Reference38 articles.

1. Allen, L. K., Crossley, S. A., & McNamara, D. S. (2015). Predicting misalignment between teachers’ and students’ essay scores using natural language processing tools. In C. Conati, N. Heffernan, A. Mitrovic, & M. Verdejo (eds). Artificial Intelligence in Education. AIED 2015 (pp. 529–532). Cham: Springer.

2. Leveraging self-assessment to encourage learning through reflection on doing;Autrey J. L.;The International Journal of Engineering Education,2018

3. Autrey, M. J. L., Mistree, F., & Siddique, Z. (2016, June 26–29). Fostering learning principles of engineering design [Conference presentation]. 2016 ASEE Annual Conference and Exposition: Research on Design Learning. New Orleans, LA, United States.

4. Balyan, R., McCarthy, K. S., McNamara, D. S. (2017). Combining machine learning and natural language processing to assess literary text comprehension. In A. Hershkovitz & L. Paquette (eds.), Proceedings of the 10th International Conference on Educational Data Mining (pp. 244–249). Wuhan, China.

5. Engineering student attitudes assessment;Besterfield‐Sacre M.;Journal of Engineering Education,1998

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