Item-based Bayesian Student Models
نویسندگان
چکیده
Many intelligent educational systems require a component that represents and assesses the knowledge state and the skills of the student. We review how student models can be induced from data and how the skills assessment can be conducted. We show that by relying on graph models with observable nodes, learned student models can be built from small data sets with standard Bayesian Network techniques and Naı̈ve Bayesian models. We also show how to feed a concept assessment model from a learned observable nodes model. Different experiments are reported to evaluate the ability of the models to predict item outcome and concept mastery.
منابع مشابه
Linear models of student skills for static data
Current student skills models rely on non linear models such as Bayesian Networks and Bayesian Knowledge Tracing, and on general linear models, such as IRT which can be considered a logistic regression. Only a handful of recent studies have looked at linear models based on matrix factorization techniques. These studies obtained good success over data from dynamic student knowledge states when c...
متن کاملBayesian Student Models Based on Item to Item Knowledge Structures
Bayesian networks are commonly used in cognitive student modeling and assessment. They typically represent the item-concepts relationships, where items are observable responses to questions or exercises and concepts represent latent traits and skills. Bayesian networks can also represent concepts-concepts and concepts-misconceptions relationships. We explore their use for modeling item-item rel...
متن کاملUsing Mutual Information for Adaptive Item Comparison and Student Assessment
The author analyzes properties of mutual information between dichotomous concepts and test items. The properties generalize some common intuitions about item comparison, and provide principled foundations for designing item-selection heuristics for student assessment in computer-assisted educational systems. The proposed item-selection strategies along with some common and conceivable methods, ...
متن کاملAdaptive Test Design with a Naive Bayes Framework
Bayesian graphical models are commonly used to build student models from data. A number of standard algorithms are available to train Bayesian models from student skills assessment data. These models can assess student knowledge and skills from a few observations. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the ...
متن کاملBack to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRTbased proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student’s future response given previous responses using two publicly available and on...
متن کامل