32 research outputs found
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW),
201
Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning
Large-scale behavioral datasets enable researchers to use complex machine
learning algorithms to better predict human behavior, yet this increased
predictive power does not always lead to a better understanding of the behavior
in question. In this paper, we outline a data-driven, iterative procedure that
allows cognitive scientists to use machine learning to generate models that are
both interpretable and accurate. We demonstrate this method in the domain of
moral decision-making, where standard experimental approaches often identify
relevant principles that influence human judgments, but fail to generalize
these findings to "real world" situations that place these principles in
conflict. The recently released Moral Machine dataset allows us to build a
powerful model that can predict the outcomes of these conflicts while remaining
simple enough to explain the basis behind human decisions.Comment: Camera ready version for Cognitive Science Conferenc
Deep learning for knowledge tracing in learning analytics: An overview
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting,
analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the
environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of
the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid
failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep
Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge
Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering
of educational data from many different sources such as online learning platforms, intelligent objects,
and mainstream IT-based systems for education. DKT predicts the student’s performances by using
the information embedded in the collected data. Moreover, it has been shown to be able to outperform
the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by
focusing on their prominent contribution in solving the KT task
Deep learning for knowledge tracing in learning analytics: An overview
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering of educational data from many different sources such as online learning platforms, intelligent objects, and mainstream IT-based systems for education. DKT predicts the student’s performances by using the information embedded in the collected data. Moreover, it has been shown to be able to outperform the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by focusing on their prominent contribution in solving the KT task