New Orleans, Louisiana
June 26, 2016
June 26, 2016
August 28, 2016
NSF Grantees Poster Session
A computational tool is developed to track student knowledge of cornerstone engineering concepts in large classes. The model that is attempting to be achieved is that individual student knowledge is just a hypothesis/model that needs to be tested using data obtained from assessment instruments. This fits naturally with probabilistic methodologies such as Bayesian inference that formalizes the scientific method. This computational tool will go beyond grading, and will allow instructors to provide formative feedback with respect to the challenging concepts specific to each individual student. An initial pilot experiment has already been performed during the Fall 2015 Statics class. The data collected from 37 students over three sequential quizzes has been used to inform the development of Bayesian networks for knowledge tracking. The main challenge identified during this study relates to the instantiation of conditional probabilities of the questions’ answers given the knowledge of the concepts. While the conditional probability when knowing the concept can be easily constructed, the more interesting probability of answering incorrectly when not knowing the concepts is more challenging to define. This is due to the probability distribution for these cases being highly dependent on the question and the type of misconceptions that the students have at the time of assessment. To address this challenge we propose to learn these conditional probabilities directly from the data.
Chen, C., & Madarshahian, R., & Caicedo, J. M., & Pierce, C. E., & Terejanu, G. (2016, June), Bayesian Network Models for Student Knowledge Tracking in Large Classes Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27282
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