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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.  ...  The p and guidance from Shri Mat Chancellor of Amrita Universit  ... 
doi:10.1109/ccip.2015.7100688 fatcat:nm4imwm6aranjkjjns5do6ltdy

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 also find that this detector only needs limited amounts of student data (the first 20% of a student's data from a tutor lesson) in order to reach near-asymptotic predictive power.  ...  We compare this model to Bayesian Knowledge Tracing -a student model shown to predict post-test performance -as a predictor of transfer.  ... 
doi:10.1007/978-3-642-21869-9_6 fatcat:v3pt62xzwfec7gurbxhywyh2xq

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.  ...  The posterior probability P (L n |Answer) is estimated by a Bayesian inference scheme, as follows: (2) Dynamic Bayesian Knowledge Tracing BKT models the parameters of each KC individually, i.e., it employs  ... 
arXiv:2105.15106v2 fatcat:723wl2krqzd3ziboc2vmhdu23q

Detecting the Moment of Learning [chapter]

Ryan S. J. d. Baker, Adam B. Goldstein, Neil T. Heffernan
2010 Lecture Notes in Computer Science  
Implications for knowledge tracing and potential uses in "discovery with models" educational data mining analyses are discussed, including analysis of which skills are learned gradually, and which are  ...  In this paper, we present a machine-learned model that can assess the probability that a student learned a skill at a specific problem step (instead of at the next or previous problem step).  ...  the Classroom", award number DGE0742503, and by the Pittsburgh Science of Learning Center, NSF award number not yet available.  ... 
doi:10.1007/978-3-642-13388-6_7 fatcat:l3nzibkgj5afnlqkhevqik7x64

How deep is knowledge tracing? [article]

Mohammad Khajah and Robert V. Lindsey and Michael C. Mozer
2016 arXiv   pre-print
tracing or DKT---has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT.  ...  In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, general-purpose models whose parameters  ...  DISCUSSION Our goal in this article was to investigate the basis for the impressive predictive advantage of deep knowledge tracing over Bayesian knowledge tracing.  ... 
arXiv:1604.02416v2 fatcat:wsl4oujmszfgbmfedqndihurlm

Design of Intelligent E-Learning Assessment Framework Using Bayesian Belief Network

Rohit B. Kaliwal, Santosh. L. Deshpande
2021 Journal of Engineering Education Transformations  
In this work, a probability-based ITSs system is proposed consisting of four models specifically the learner's behaviour model, pedagogical model, knowledge base model and learner assessment model.  ...  The importance has been given to the learner assessment model where an element of uncertainty has been introduced and handled by the Bayesian Belief Network (BBN).  ...  based on the Corbett and Anderson's Bayesian Knowledge Tracing model.  ... 
doi:10.16920/jeet/2021/v34i0/157238 fatcat:te5ueuzd6rgqzcsq7ubtau5v3m

Multiple Features Fusion Attention Mechanism Enhanced Deep Knowledge Tracing for Student Performance Prediction

Dong Liu, Yunping Zhang, Jun Zhang, Qinpeng Li, Congpin Zhang, Yu Yin
2020 IEEE Access  
the knowledge tracing model.  ...  Generally, student performance prediction is achieved by tracing the evolution of each student's knowledge states via a series of learning activities.  ...  In order to consider the variability of students, Yudelson proposed individualized bayesian knowledge tracing model that incorporates student-specific parameters (such as the initial knowledge mastery  ... 
doi:10.1109/access.2020.3033200 fatcat:oyov7avxjnaktksdc36bg2cgxy

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  
Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users.  ...  In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills  ...  Bayesian Knowledge-Tracing Bayesian Knowledge-Tracing (BKT) is another approach that relies on Bayesian theory.  ... 
doi:10.1007/s11257-011-9106-8 fatcat:3gstd2457ze4vji53tbmlgftnu

Knowledge tracing: Modeling the acquisition of procedural knowledge

Albert T. Corbett, John R. Anderson
1995 User modeling and user-adapted interaction  
As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process called knowledge tracing.  ...  APT is constructed around a production rule cognitive model of programming knowledge, called the ideal student model.  ...  Acknowledgements This research was supported by the Office of Naval Research grant N00014-91-J-1597.  ... 
doi:10.1007/bf01099821 fatcat:l4oscn5ybvdktgkx4cv7nh3w3y

Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics [chapter]

Maria Ofelia Clarissa Z. San Pedro, Ryan S. J. d. Baker, Ma. Mercedes T. Rodrigo
2011 Lecture Notes in Computer Science  
Bayesian Knowledge Tracing and its variant, the Contextual-Slip-and-Guess Estimation, are used to model and predict carelessness behavior in the Scatterplot Tutor.  ...  A student is said to have committed a careless error when a student's answer is wrong despite the fact that he or she knows the answer .  ...  This process is based on Corbett and Anderson's Bayesian Knowledge Tracing (BKT) model [11] .  ... 
doi:10.1007/978-3-642-21869-9_40 fatcat:d6y7qgyttrdzpoxn6lfpgle5im

Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation [article]

Kevin H. Wilson, Yan Karklin, Bojian Han, Chaitanya Ekanadham
2016 arXiv   pre-print
We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results.  ...  to their formulation as Bayesian probabilistic models.  ...  Two classical families of methods for estimating proficiency are Item Response Theory (IRT) [8, 13] and Bayesian Knowledge Tracing (BKT) [2] .  ... 
arXiv:1604.02336v2 fatcat:v77xnwzf5fc55fx6n3zn3vl5dm

Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users

Shreya Ghosh, Soumya K. Ghosh
2017 Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion  
users, summarizes individuals' GPS traces and clusters users based on the semantics of their movement patterns.  ...  To alleviate labelled data scarcity problem while user categorization in a particular region of interest (ROI), we propose a method to transfer knowledge derived from a set of GPS traces of a geographically  ...  Modeling of User-Trace Summary The behavioral patterns of the human movement traces (collected through GPS footprints) can be effectively modelled using Bayesian network.  ... 
doi:10.1145/3041021.3054150 dblp:conf/www/GhoshG17 fatcat:cvmc4x7v5ng7loamf6adlqj2pe

Educational Data Mining [chapter]

John R. Buri, Amy Gunty, Norbert M. Seel, Reuven Dukas, Ruhama Even, Oliver Scheuer, Bruce M. McLaren, Zuhal Okan, D. Erik Everhart, Maren Schmidt-Kassow, Sii Ching Hii, Soon Fook Fong (+91 others)
2012 Encyclopedia of the Sciences of Learning  
Data of interest is not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input to quizzes and interactive exercises) but might also include data  ...  , eye-tracking, and video data, opening up new opportunities to study how students learn with technology.  ...  An example application in EDM is the estimation of Bayesian Knowledge Tracing (BKT) parameters.  ... 
doi:10.1007/978-1-4419-1428-6_618 fatcat:edqvuplqqvgehgnexg7hdq2uyy

Predictive Student Modeling in Educational Games with Multi-Task Learning

Michael Geden, Andrew Emerson, Jonathan Rowe, Roger Azevedo, James Lester
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Modeling student knowledge is critical in adaptive learning environments.  ...  Predictive student modeling enables formative assessment of student knowledge and skills, and it drives personalized support to create learning experiences that are both effective and engaging.  ...  Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or SSHRC. References  ... 
doi:10.1609/aaai.v34i01.5406 fatcat:dtl4gtdihnawxamyrtli7z6smi

Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory [article]

Chun-Kit Yeung
2019 arXiv   pre-print
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have  ...  In this paper, we propose Deep-IRT which is a synthesis of the item response theory (IRT) model and a knowledge tracing model that is based on the deep neural network architecture called dynamic key-value  ...  Bayesian Based Knowledge Tracing The Bayesian knowledge tracing (BKT) model was proposed by Corbett and Anderson [4] during the 1990s.  ... 
arXiv:1904.11738v1 fatcat:szgfp6ex45btjpogz7gawvqucm
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