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Learning Bayesian Knowledge Tracing Parameters with a Knowledge Heuristic and Empirical Probabilities [chapter]

William J. Hawkins, Neil T. Heffernan, Ryan S. J. D. Baker
2014 Lecture Notes in Computer Science  
Due to its predictive accuracy, interpretability and ability to infer student knowledge, Corbett & Anderson's Bayesian Knowledge Tracing is one of the most popular student models.  ...  We instead fit parameters by estimating the mostly likely point that each student learned the skill, developing a new method that avoids the above problems while achieving similar predictive accuracy.  ...  Bayesian Knowledge Tracing Bayesian Knowledge Tracing [5] is a student model used in ITS research that infers a student's knowledge given their history of responses to problems, which it can use to predict  ... 
doi:10.1007/978-3-319-07221-0_18 fatcat:yavwvab2pbd2rk6ujjgjfr4x4q

Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor [chapter]

Ryan S. J. d. Baker, Albert T. Corbett, Sujith M. Gowda, Angela Z. Wagner, Benjamin A. MacLaren, Linda R. Kauffman, Aaron P. Mitchell, Stephen Giguere
2010 Lecture Notes in Computer Science  
In this paper, we compare the Contextual-Guess-and-Slip variant on Bayesian Knowledge Tracing to classical four-parameter Bayesian Knowledge Tracing and the Individual Difference Weights variant of Bayesian  ...  Knowledge Tracing estimates are controlled for.  ...  This research was supported by the National Science Foundation via grant "Empirical Research: Emerging Research: Robust and Efficient Learning: Modeling and Remediating Students' Domain Knowledge", award  ... 
doi:10.1007/978-3-642-13470-8_7 fatcat:ofw2thjwovcuhfpurlk2niua2q

Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing [chapter]

Zachary A. Pardos, Neil T. Heffernan
2010 Lecture Notes in Computer Science  
With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter.  ...  In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously  ...  All of the opinions expressed in this paper are those of the authors and do not necessarily reflect the views of our funders.  ... 
doi:10.1007/978-3-642-13470-8_24 fatcat:diun7bhx7rdznaxc73crupzgva

Class vs. Student in a Bayesian Network Student Model [chapter]

Yutao Wang, Joseph Beck
2013 Lecture Notes in Computer Science  
This paper extends the Student-Skill extension of Knowledge Tracing, to take into account the class information, and learns four parameters: prior knowledge, learn, guess and slip for each class of students  ...  However, there are other sources of information that are not utilized, such as the performance on other students in same class.  ...  The opinions expressed are those of the authors and do not necessarily represent the views of the Foundation.  ... 
doi:10.1007/978-3-642-39112-5_16 fatcat:et5mieq3vvcbldiqacghq32uba

Individualized Bayesian Knowledge Tracing Models [chapter]

Michael V. Yudelson, Kenneth R. Koedinger, Geoffrey J. Gordon
2013 Lecture Notes in Computer Science  
In the standard BKT implementation, there are only skill-specific parameters.  ...  Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems.  ...  Related Work Bayesian Knowledge Tracing There are four types of model parameters used in Bayesian Knowledge Tracing: initial probability of knowing the skill a priori -p(L 0 ) (or p-init), probability  ... 
doi:10.1007/978-3-642-39112-5_18 fatcat:l6upvmg5brdubeddzogojj46wi

Traditional Knowledge Tracing Models for Clustered Students

Deliang Wang, Zhi Zhang, Jiachen Song, Yu Lu
2021 The Educational Review USA  
In addition, an individualized method based on clustered students for the Bayesian Knowledge Tracing model is initially proposed, which changes the individualization level from a group of all students  ...  For more effective online learning, knowledge tracing that can dynamically estimate the knowledge state of learners should be paid more attention.  ...  Acknowledgements This study is funded by the Program for Student Research in the Faculty of Education, Beijing Normal University (1912103).  ... 
doi:10.26855/er.2020.12.005 fatcat:cljwl2rjzbcu5eviocv22nldja

The Effect of Variations of Prior on Knowledge Tracing

Matti Nelimarkka, Madeeha Ghori
2014 Educational Data Mining  
We are interested how well knowledge tracing performs when students' prior knowledge on the topic is extremely high or low.  ...  Knowledge tracing is a method which enables approximation of a student's knowledge state using a Bayesian network for approximation.  ...  INTRODUCTION The Bayesian Knowledge-Tracing (BKT) algorithm was developed in 1995 in an effort to model students' changing knowledge state during skill acquisition [5] .  ... 
dblp:conf/edm/NelimarkkaG14 fatcat:oxuctvco3vhchp42excbymmlni

Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems [chapter]

Ryan S. J. d. Baker, Zachary A. Pardos, Sujith M. Gowda, Bahador B. Nooraei, Neil T. Heffernan
2011 Lecture Notes in Computer Science  
Over the last decades, there have been a rich variety of approaches towards modeling student knowledge and skill within interactive learning environments.  ...  However, the ensembles of models perform marginally significantly worse than the best individual models, at predicting post-test performance.  ...  This research was supported by the National Science Foundation via grant "Empirical Research: Emerging Research: Robust and Efficient Learning: Modeling and Remediating Students' Domain Knowledge", award  ... 
doi:10.1007/978-3-642-22362-4_2 fatcat:l2ido4zcmreo7eeons6oyp2wdi

Bayesian Knowledge Tracing for Navigation through Marzano's Taxonomy

Francisco Cervantes-Pérez, Joaquin Navarro-Perales, Ana L. Franzoni-Velázquez, Luis Valentín
2021 International Journal of Interactive Multimedia and Artificial Intelligence  
For this we use the Bayesian Knowledge Tracing algorithm, performing an adaptive control of the navigation among different levels of cognition in online courses.  ...  The main improvements of this proposal are: 1) An adaptive transition between individual assessment questions determined by levels of cognition. 2) A student model based on the initial response of a group  ...  Bayesian Knowledge Tracing Transitions between lessons are defined according to the Bayesian Knowledge Tracing algorithm, a tool developed by Anderson and Corbett [30] that modelled the acquisition of  ... 
doi:10.9781/ijimai.2021.05.006 fatcat:sknhqcaitjhnxbous5cdpej76y

Estimating Individual Differences for Student Modeling in Intelligent Tutors from Reading and Pretest Data [chapter]

Michael Eagle, Albert Corbett, John Stamper, Bruce M. McLaren, Angela Wagner, Benjamin MacLaren, Aaron Mitchell
2016 Lecture Notes in Computer Science  
Standard BKT individualizes parameter estimates for skills, also referred to as knowledge components (KCs), but not for students.  ...  Past studies have shown that Bayesian Knowledge Tracing (BKT) can predict student performance and implement Cognitive Mastery successfully.  ...  This research was supported by the National Science Foundation under the grant "Knowing What Students Know: Using Education Data Mining to Predict Robust STEM Learning", award number DRL1420609.  ... 
doi:10.1007/978-3-319-39583-8_13 fatcat:jvibabfhpfcb5n3k6u3f3vprk4

Learner Modeling for Integration Skills

Yun Huang, Julio Guerra-Hollstein, Jordan Barria-Pineda, Peter Brusilovsky
2017 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17  
However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill.  ...  Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills.  ...  greater than the "sum" of the knowledge of individual skills [17] , some skills must be integrated with other skills to produce behavior [25] .  ... 
doi:10.1145/3079628.3079677 dblp:conf/um/HuangGBB17 fatcat:p5t72eo3yncovgm4brxntpp4li

Towards Predicting Future Transfer of Learning [chapter]

Ryan S. J. d. Baker, Sujith M. Gowda, Albert T. Corbett
2011 Lecture Notes in Computer Science  
We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper posttests of  ...  We present an automated detector that can predict a student's future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system  ...  This is substantially higher than the cross-validated correlation of Bayesian Knowledge Tracing, a measure of skill learning within the tutor software.  ... 
doi:10.1007/978-3-642-21869-9_6 fatcat:v3pt62xzwfec7gurbxhywyh2xq

KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model [chapter]

Zachary A. Pardos, Neil T. Heffernan
2011 Lecture Notes in Computer Science  
However, despite the positive results of models that take difficulty in to account, knowledge tracing is still used in its basic form due to its skill level diagnostic abilities that are very useful to  ...  Knowledge Tracing The standard Bayesian Knowledge Tracing (BKT) model, Fig 1, has a set of four parameters which are typically learned from data for each skill in the tutor.  ...  P(L 0 ) Model Parameter P(T[s]) = Individualized P(T) Node representation S = Student node Node states K K K P(T[s]) P(T[s]) P(L 0 ) S Knowledge Tracing with Individualized P(T) K K K Q Q Q P(T) P(T) Model  ... 
doi:10.1007/978-3-642-22362-4_21 fatcat:y56gar4hkrcwbdt5i65yztcqgm

The Fine-Grained Impact of Gaming (?) on Learning [chapter]

Yue Gong, Joseph E. Beck, Neil T. Heffernan, Elijah Forbes-Summers
2010 Lecture Notes in Computer Science  
Furthermore, we found that knowing the identity of the student is more important than knowing the skill for predicting whether gaming will occur.  ...  In addition, we found that students tend to game in those skills on which they have relatively low knowledge.  ...  Thus, student is more closely related to, and more predictive of gaming than, skill.  ... 
doi:10.1007/978-3-642-13388-6_24 fatcat:yo3dlmvr6fc2bgwdzjp5ts2dpa

A review of recent advances in learner and skill modeling in intelligent learning environments

Michel C. Desmarais, Ryan S. J. d. Baker
2011 User modeling and user-adapted interaction  
Probabilistic models for skill assessment are playing a key role in these advanced learning environments.  ...  Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users.  ...  Bayesian Knowledge-Tracing Bayesian Knowledge-Tracing (BKT) is another approach that relies on Bayesian theory.  ... 
doi:10.1007/s11257-011-9106-8 fatcat:3gstd2457ze4vji53tbmlgftnu
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