The AutoMentor project is developing a system for producing automated professional mentoring in virtual internships. This system will provide professional expertise and critical feedback to students using simulations of real-world practices.
The automated mentoring technology that we are developing integrates previous research on intelligent tutoring systems (especially AutoTutor, a computer tutor that helps students learn by conversing with them in natural language) and evidence-centered assessment design (especially epistemic network analysis, a suite of novel analytical tools).
By automating basic mentoring tasks, teachers will be able to mentor larger numbers of students, and they will have more time to provide individualized guidance. This will thus improve the efficacy of virtual internships and expand access to an effective learning technology.
(Note: If you use any products, services, or data developed or provided by EGG/GAPS—including virtual internships and epistemic network analysis—in your research or in any publications or presentations, please read our guidelines for acknowledgment.)
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Keshtkar, F., Burkett, C., Li, H., & Graesser, A.C. (2014). “Using data mining techniques to detect the personality of players in an educational game.” In A. Pena-Ayala (Ed.), Educational data mining: Applications and trends (pp. 125-150). New York: Springer.
Li, H., Duan, Y., Clewley, D., Morgan, B., Graesser, A.C., Shaffer, D.W., & Saucerman, J. (2014). Question asking during collaborative problem solving in an online game environment. Paper presented at the International Conference on Intelligent Tutoring Systems. Honolulu, HI.
Dowell, N.M., Cade, W.L., Tausczik, Y.R., Pennebaker, J.W., & Graesser, A.C. (2014). “What works: Creating adaptive and intelligent systems for collaborative learning support.” Paper presented at the International Conference on Intelligent Tutoring Systems. Honolulu, HI.
Li, H., Samei, B., Olney, A.M., Graesser, A.C., & Shaffer, D.W. (2014). Question classification in an epistemic game. Paper presented at the International Conference on Intelligent Tutoring Systems. Honolulu, HI.
Samei, B., Li, H., Keshtkar, F., Rus, V., & Graesser, A.C. (2014). “Context-based speech act classification in intelligent tutoring systems.” Paper presented at the International Conference on Intelligent Tutoring Systems. Honolulu, HI.
Morgan, B., Keshtkar, F., Graesser, A.C., & Shaffer, D.W. (2013). Automating the mentor in a serious game: A discourse analysis using finite state machines. Paper presented at the International Conference on Human-Computer Interaction. Las Vegas, NV.
Bagley, E. & Shaffer, D.W. (2012). Epistemic mentoring in virtual and face-to-face environments. (Unpublished doctoral dissertation). University of Wisconsin-Madison.
Moldovan, C., Rus, V., & Graesser, A.C. (2011). Automated speech act classification for online chat. Paper presented at the Midwest Artificial Intelligence and Cognitive Science Conference. Cincinnati, OH.
Nash, P. & Shaffer, D.W. (2010). Mentor modeling: The internalization of modeled professional thinking in an epistemic game. Paper presented at the International Conference of the Learning Sciences. Chicago, IL.
Shaffer, D.W. & Graesser, A.C. (2010). Using a quantitative model of participation in a community of practice to direct automated mentoring in an ill-formed domain. Paper presented at the Intelligent Tutoring Systems Conference. Pittsburgh, PA.