Add to ORKGMastery learning in intelligent tutoring systems produces a differential attrition of students over time, based on their levels of knowledge and ability. This results in a systematic bias when student data are aggregated to produce learning curves. We outline a formal framework, based on Bayesian Knowledge Tracing, to evaluate the impact of differential student attrition in mastery learning systems, and use simulations to investigate the impact of this effect in both homogeneous and mixed populations of learners
This paper presents an evaluation study that measures the effect of modifying feedback generality in...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore,...
This work describes a unified approach to two problems pre-viously addressed separately in Intellige...
By implementing mastery learning, intelligent tutoring systems aim to present students with exactly ...
Learning curves are a crucial tool to accurately measure learners skills and give meaningful feedbac...
The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, espec...
Using data from student use of educational technologies to evaluate and improve cognitive models of ...
An effective tutor—human or digital—must determine what a student does and does not know. Inferring ...
We propose a new model to assess the mastery level of a given skill efficiently. The model, called B...
One function of a student model in tutoring systems is to select future tasks that will best meet st...
We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by ...
Abstract. Evidence of the strong relationship between learning and emotion has fueled recent work in...
Modeling students’ knowledge is a fundamental part of intelligent tutoring systems. One of the most ...
When modeling student learning, tutors that use the Knowl-edge Tracing framework often assume that a...
Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide lear...
This paper presents an evaluation study that measures the effect of modifying feedback generality in...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore,...
This work describes a unified approach to two problems pre-viously addressed separately in Intellige...
By implementing mastery learning, intelligent tutoring systems aim to present students with exactly ...
Learning curves are a crucial tool to accurately measure learners skills and give meaningful feedbac...
The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, espec...
Using data from student use of educational technologies to evaluate and improve cognitive models of ...
An effective tutor—human or digital—must determine what a student does and does not know. Inferring ...
We propose a new model to assess the mastery level of a given skill efficiently. The model, called B...
One function of a student model in tutoring systems is to select future tasks that will best meet st...
We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by ...
Abstract. Evidence of the strong relationship between learning and emotion has fueled recent work in...
Modeling students’ knowledge is a fundamental part of intelligent tutoring systems. One of the most ...
When modeling student learning, tutors that use the Knowl-edge Tracing framework often assume that a...
Intelligent tutoring systems (ITSs) teach skills using learning-by-doing principles and provide lear...
This paper presents an evaluation study that measures the effect of modifying feedback generality in...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore,...
This work describes a unified approach to two problems pre-viously addressed separately in Intellige...