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Individualized Bayesian Knowledge Tracing Models [chapter]

Michael V. Yudelson, Kenneth R. Koedinger, Geoffrey J. Gordon
2013 Lecture Notes in Computer Science  
Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems.  ...  However, a large body of research strongly suggests that studentspecific variability in the data, when accounted for, could enhance model accuracy [5, 6, 8] .  ...  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

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

Zachary A. Pardos, Neil T. Heffernan
2010 Lecture Notes in Computer Science  
Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data.  ...  The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995) , to track student knowledge for over a decade.  ...  Corbett and colleagues later released a toolkit [7] using non-individualized Bayesian knowledge tracing to allow researchers to fit their own data and student models with DBNs. 4 Zachary A.  ... 
doi:10.1007/978-3-642-13470-8_24 fatcat:diun7bhx7rdznaxc73crupzgva

The Student Skill Model [chapter]

Yutao Wang, Neil T. Heffernan
2012 Lecture Notes in Computer Science  
The original Knowledge Tracing model does not allow for individualization.  ...  One of the most popular methods for modeling students' knowledge is Corbett and Anderson's[1] Bayesian Knowledge Tracing (KT) model.  ...  Introduction One of the most popular methods for modeling students' knowledge is Corbett and Anderson's [1] Bayesian Knowledge Tracing model.  ... 
doi:10.1007/978-3-642-30950-2_51 fatcat:me73eobaxjf6tedk3jcmxlrkci

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 (Corbett & Anderson, 1995) , investigating how well each model variant predicts 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:10.1007/978-3-642-13470-8_7 fatcat:ofw2thjwovcuhfpurlk2niua2q

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  ...  To confirm whether this individualized method can be generalized to other knowledge tracing models, we also test it on logistic knowledge tracing models.  ...  In this paper, we focus on the promotion of KT models' performance and take the Bayesian Knowledge Tracing model and logistic knowledge tracing models as examples.  ... 
doi:10.26855/er.2020.12.005 fatcat:cljwl2rjzbcu5eviocv22nldja

Develop Academic Question Recommender Based on Bayesian Network for Personalizing Student's Practice

Qingsheng Zhang, Di Yang, Pengjun Fang, Nannan Liu, Lu Zhang
2020 International Journal of Emerging Technologies in Learning (iJET)  
In this paper, an academic question recommender based on Bayesian network is developed for personalizing practice question sequence with tracing mastery level of student on knowledge components.  ...  Study in Literatures shows that tracing knowledge state of student is corner stone of intelligent tutoring system for personalized learning.  ...  Bayesian inference scheme is used to estimate posterior probability in this tracing knowledge state model.  ... 
doi:10.3991/ijet.v15i18.11594 fatcat:wpt5qzucxfar5pfwzmb4r3krda

A Comparison of Two Different Methods to Individualize Students and Skills [chapter]

Yutao Wang, Neil Heffernan
2013 Lecture Notes in Computer Science  
The original Knowledge Tracing model does not allow for individualization.  ...  One of the most popular methods for modeling students' knowledge is Corbett and Anderson's [1] Bayesian Knowledge Tracing (KT) model.  ...  Introduction One of the most popular methods for modeling students knowledge is Corbett and Anderson's [1] Bayesian Knowledge Tracing model.  ... 
doi:10.1007/978-3-642-39112-5_125 fatcat:2wurkieyyneermn2xsm2g7zh4a

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

Properties of the Bayesian Knowledge Tracing Model

Brett Van de Sande
2013 Zenodo  
Bayesian knowledge tracing has been used widely to model student learning.  ...  However, the name \Bayesian knowledge tracing" has been applied to two related, but distinct, models: The first is the Bayesian knowledge tracing Markov chain which predicts the student-averaged probability  ...  BAYESIAN KNOWLEDGE TRACING ALGORITHM In order to predict P (L j ) for an individual student in real time, the Knowledge Tracing Algorithm [Corbett and Anderson 1995] may be employed.  ... 
doi:10.5281/zenodo.3554629 fatcat:md3fmkzm2fhwrlq4ha47ixdy4m

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

Yutao Wang, Joseph Beck
2013 Lecture Notes in Computer Science  
Most of the models, including two of the most popular approaches: Knowledge Tracing model and Performance Factor Analysis, all have similar assumption: the information needed to model the student is the  ...  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  ...  Two of the most popular approaches are Bayesian Knowledge Tracing (KT) [1] , which uses a dynamic Bayesian Network to model student learning, and Performance Factor Analysis (PFA) [2] , which uses a  ... 
doi:10.1007/978-3-642-39112-5_16 fatcat:et5mieq3vvcbldiqacghq32uba

A Survey of Knowledge Tracing [article]

Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, Yonghe Zheng
2021 arXiv   pre-print
Knowledge Tracing (KT), which aims to monitor students' evolving knowledge state, is a fundamental and crucial task to support these intelligent services.  ...  In this survey, we propose a new taxonomy of existing basic KT models from a technical perspective and provide a comprehensive overview of these models in a systematic manner.  ...  Bayesian Knowledge Tracing To the best of our knowledge, Bayesian Knowledge Tracing (BKT) was the first KT model to be proposed [13] .  ... 
arXiv:2105.15106v2 fatcat:723wl2krqzd3ziboc2vmhdu23q

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  
individually.  ...  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 Structural Equation Modeling: A Business Culture Application in Kenya

Mutitu Ephantus Mwangi
2016 Science Journal of Applied Mathematics and Statistics  
A Bayesian approach to SEM allows inclusion of this uncertainty and directly models the uncertainties in predictive models.  ...  Bayesian SEM with non-informative prior gave the best estimates indicating that personal distance, individualism and long term orientation were significantly related to business performance.  ...  Inclusion of Prior information in Bayesian approach to SEM permits the model to mirror elementary beliefs about the circumstance and provision of knowledge for under identified parameters in traditional  ... 
doi:10.11648/j.sjams.20160402.13 fatcat:b26akgytsjenpmqmxvqdudrwm4

Towards a Recommendation System for the Learner from a Semantic Model of Knowledge in a Collaborative Environment [chapter]

Chahrazed Mediani, Marie-Hélène Abel, Mahieddine Djoudi
2015 IFIP Advances in Information and Communication Technology  
We have chosen to use a Bayesian formula to calculate the knowledge level of a learner on a concept of the application ontology describing the pedagogical content of training.  ...  This architecture is based on an original model of the learner tak-ing into account the definition of data (learning indicators). A knowledge base containing this information was constructed.  ...  ., 2012) define an original traces model that distinguishes private actions, individual, collective and collaborative.  ... 
doi:10.1007/978-3-319-19578-0_26 fatcat:tpesxf255feibh6zcqmc4qrbje

Incorporating forgetting in the Personalized, Clustered, Bayesian Knowledge Tracing (PC-BKT) model

Prema Nedungadi, M S Remya
2015 2015 International Conference on Cognitive Computing and Information Processing(CCIP)  
In our previous work, we had proposed the Personalized, Clustered, Bayesian Knowledge Tracing (PC-BKT) model that individualizes the learning of skills for each student and additionally improves the prediction  ...  The Bayesian Knowledge Tracing (BKT) Student Model is a time-tested method that maintains information about students' knowledge levels for the different skills in the topic domain.  ...  Bayesian Knowledge Tracing Model The Bayesian Knowledge Trading model [5] is a four parameter model; with two knowledge parameters, the initial knowledge and the rate of learning, and two performance  ... 
doi:10.1109/ccip.2015.7100688 fatcat:nm4imwm6aranjkjjns5do6ltdy
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