Assessing student-generated design justifications in engineering virtual internships
Abstract
Engineering virtual internships are simulations where students role play as interns at fictional companies, working to create engineering designs. To improve the scalability of these virtual internships, a reliable automated assessment system for tasks submitted by students is necessary. Therefore, we propose a machine learning approach to automatically assess student generated textual design justifications in two engineering virtual internships, Nephrotex and RescuShell. To this end, we compared two major categories of models: domain expert-driven vs. general text analysis models. The models were coupled with machine learning algorithms and evaluated using 10-fold cross validation. We found no quantitative differences among the two major categories of models, domain expert-driven vs. general text analysis, although there are major qualitative differences as discussed in the paper.
Publication Title
Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016
Recommended Citation
Rus, V., Gautam, D., Swiecki, Z., Shaffer, D., & Graesser, A. (2016). Assessing student-generated design justifications in engineering virtual internships. Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 496-501. Retrieved from https://digitalcommons.memphis.edu/facpubs/2546
