@inproceedings{ed765a073c5d427daed82ab1154be87e,
title = "Inferring learners{\textquoteright} knowledge from observed actions",
abstract = "Teachers gain significant information about their students through close observation of classroom activities. By noting which actions a student takes to achieve particular goals, a teacher can often infer the knowledge possessed by the student and diagnose misconceptions. In this work, we develop a framework for automatically inferring a student{\textquoteright}s underlying beliefs from a set of observed actions. This framework relies on modeling how student actions follow from beliefs about the effects of those actions. We demonstrate the practicality of this approach by modeling empirical student data from an educational game and validate its performance via a controlled lab experiment. In the educational game, inferences were consistent with conventional assessment measures; in the lab experiment, the model{\textquoteright}s inferences reflect participants{\textquoteright} stated beliefs.",
author = "Rafferty, {Anna N.} and LaMar, {Michelle M.} and Griffiths, {Thomas L.}",
note = "Funding Information: crobe game data (supported by NSF grant number DRL-0816359). We also thank Benj Shapiro and HyeYoung Shin for help with data collection. This work was supported by a DoD NDSEG Fellowship to ANR and NSF grant number IIS-0845410 to TLG.; 5th International Conference on Educational Data Mining, EDM 2012 ; Conference date: 19-06-2012 Through 21-06-2012",
year = "2012",
language = "English (US)",
series = "Proceedings of the 5th International Conference on Educational Data Mining, EDM 2012",
publisher = "www.educationaldatamining.org",
editor = "Kalina Yacef and Zaiane, {Osmar R.} and Arnon Hershkovitz and Michael Yudelson",
booktitle = "Proceedings of the 5th International Conference on Educational Data Mining, EDM 2012",
}