Assessing student-generated design justifications in engineering virtual internships

Vasile Rus, Dipesh Gautam, Zachari Swiecki, David W. Shaffer, Arthur C. Graesser

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 9th International Conference on Educational Data Mining
EditorsT. Barnes, M. Chi, M. Feng
Place of PublicationRaleigh NC USA
PublisherInternational Educational Data Mining Society
Number of pages6
Publication statusPublished - 2016
Externally publishedYes
EventEducational Data Mining 2016 - Sheraton Raleigh Hotel, Raleigh, United States of America
Duration: 29 Jun 20162 Jul 2016
Conference number: 9th


ConferenceEducational Data Mining 2016
Abbreviated titleEDM 2016
CountryUnited States of America
Internet address


  • Auto-assessment
  • Epistemic frame theory
  • Machine learning
  • Virtual internships

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