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Score-Based Bayesian Skill Learning [chapter]

Shengbo Guo, Scott Sanner, Thore Graepel, Wray Buntine
2012 Lecture Notes in Computer Science  
We extend the Bayesian skill rating system of TrueSkill to accommodate score-based match outcomes.  ...  skills interact to generate score-based match outcomes.  ...  Skill Learning using TrueSkill Since our score-based Bayesian skill learning contributions build on TrueSkill [5] , we begin with a review of the TrueSkill Bayesian skill-learning graphical model for  ... 
doi:10.1007/978-3-642-33460-3_12 fatcat:vao623afnnah3b4likdnxxv234

A Mixed Methods Design for Assessing Physics Learning in the Online Learning Environment

Zhidong Zhang
2022 Journal of Education and Development  
This study explored a Bayesian assessment model for physics students in motion learning.  ...  The exploratory sequential model was developed based on a motion learning student model, which was a structured data collection template.  ...  Such a mastery learning cut-off score is a model-based and data-driven determination.  ... 
doi:10.20849/jed.v6i2.1142 fatcat:agatm7w3avhfhgiph5nbzajmkm

Improving Intelligent Tutoring Systems: Using Expectation Maximization to Learn Student Skill Levels [chapter]

Kimberly Ferguson, Ivon Arroyo, Sridhar Mahadevan, Beverly Woolf, Andy Barto
2006 Lecture Notes in Computer Science  
In particular, we build a Bayesian network (BN) based on student pretests of problems using 12 different skills and then run inference to predict a student's individual mastery of each skill.  ...  We utilize the Bayesian Information Criterion (BIC) to evaluate different skill models.  ...  Structural Learning uses Bayesian Information Criterion (BIC) to compute the Bayesian score of each model and declares the model with the maximum BIC score the best model [S78] .  ... 
doi:10.1007/11774303_45 fatcat:ixl45ib6h5dibdnm3t7s7b2sh4

Designing Cognitively Diagnostic Assessment for Algebraic Content Knowledge and Thinking Skills

Zhidong Zhang
2018 International Education Studies  
Using the theory driven model, the thinking skills of algebra learning was also examined. A Bayesian network model was applied to represent the theory model and the quantitative assessment structure.  ...  Through students' performance examples model-based achievement scores were reported at three levels: 1) evidential variable level, 2) explanatory variable lower level, and 3) explanatory variable higher  ...  The researchers should have sufficient domain and learning science knowledge. Thus the assessment model can provide effective diagnostic assessment information.  ... 
doi:10.5539/ies.v11n2p106 fatcat:ibaxz7j375hqbdx7eszlvdvgbq

Special feature: advanced technologies in educational assessment

Ronny Scherer, Marie Wiberg
2018 Behaviormetrika  
Psychometric modeling techniques to improve the assessment of learning and learning progressions in technology-based assessments Slater and Baker (2018) Errors in Bayesian knowledge tracing estimates  ...  assessment of skills Technology-based assessment with simulations of reallife problem situations and Crafting a multi-perspective validity argument for the resultant performance indicators and scores  ... 
doi:10.1007/s41237-018-0071-y fatcat:ml65wduztjhvjfaeccu3n4ij7a

A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

Ebedia Hilda Am, Indriana Hidayah, Sri Suning Kusumawardani
2021 Journal of Information Technology and Computer Science  
Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach.  ...  Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning.  ...  The RMSE score obtained by T2FBKT was around 0.1. Furthermore, the study performed by Meng et al. [6] proposed Cross-skill Bayesian Knowledge Tracing (CS-BKT).  ... 
doi:10.25126/jitecs.202162344 fatcat:zgfx2velsvftjgcjkhqjbmsqve

Assessing Argumentative Representation with Bayesian Network Models in Debatable Social Issues

Zhidong Zhang, Jingyan Lu
2014 International Education Studies  
Bayesian networks were used to assess and update student progress.  ...  Effective argumentative representation is a crucial step to bridge argumentative learning and assessment.  ...  Limitations Fifty-two students are still not sufficient for Bayesian network to learn to become a robust network.  ... 
doi:10.5539/ies.v7n11p120 fatcat:3ynbgtzo5jeuzinvqyadxcxd2m

Intelligent Tutoring System Using Bayesian Network for Vocational High Schools in Indonesia

Ikhwan Burhan Muhammad, Sediyono Eko, Adi Kusworo
2021 E3S Web of Conferences  
Based on those, this research proposes an Intelligent Tutoring System (ITS) model using Bayesian Network at Vocational High Schools (SMK) to determine the level of students' abilities and teach skills  ...  Some computer-based self-learning systems have been developed as solutions to these problems.  ...  Analysis of Learning Outcomes Based on the experimental result, the researchers compared the obtained pre-test scores and the post-test scores.  ... 
doi:10.1051/e3sconf/202131705027 doaj:8d9fbf230a184cb98fb7046d3477bde1 fatcat:hno5kmdnerbtner5mzkge4pjue

Automatically Learning to Teach to the Learning Objectives

Rika Antonova, Joe Runde, Min Hyung Lee, Emma Brunskill
2016 Proceedings of the Third (2016) ACM Conference on Learning @ Scale - L@S '16  
misalignment between their course, and their desired learning objectives.  ...  Our experimental results with a histogram tutoring system suggest that Bayesian Optimization can quickly (with only a small amount of student data) find good parameters, and may help instructors identify  ...  These skills represent our learning objectives for students. We also created a test based closely on assessment items previously made to evaluate some of these skills [3] .  ... 
doi:10.1145/2876034.2893443 dblp:conf/lats/AntonovaRLB16 fatcat:2op3ew2bgjdlfpm7j6lsr22hwa

Utilizing Dynamic Bayes Nets to Improve Early Prediction Models of Self-regulated Learning [chapter]

Jennifer Sabourin, Bradford Mott, James Lester
2013 Lecture Notes in Computer Science  
This work builds upon these findings and presents a dynamic Bayesian approach that significantly improves the classification accuracy of student selfregulated learning skills.  ...  Students who lack these skills are markedly less successful in self-guided learning environments and require additional scaffolding to be able to navigate them successfully.  ...  This material is based upon work supported under a National Science Foundation Graduate Research Fellowship.  ... 
doi:10.1007/978-3-642-38844-6_19 fatcat:vpcksn54qnep3fufnn4okrgfna

Page 600 of Behavior Research Methods Vol. 40, Issue 2 [page]

2008 Behavior Research Methods  
The most usual score metrics are penalized maximum likelihood, a Bayes¬ ian score known as marginal likelihood, and scores based on information theory.  ...  For a review of score + search methods for learning Bayesian networks from data, see Heckerman, Geiger, and Chickering (1995). 3.  ... 

Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks [chapter]

Zachary Pardos, Neil Heffernan, Brigham Anderson, Cristina Heffernan
2010 Handbook of Educational Data Mining  
We view this as support for fine-grained skill models despite the finest grain model not predicting the standardized test scores most effectively. 1 This paper is an expansion of work presented at the  ...  We employ the use of Bayesian networks to model user knowledge and prediction of student responses.  ...  ., skill). However, cognitive scientists such as Anderson & Lebiere [1] believe that students are learning individual skills and might learn one skill but not another.  ... 
doi:10.1201/b10274-32 fatcat:brwzkxxyazerve3kxed7sy2tte

A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers

D. Hooshyar, R.B. Ahmad, M. Yousefi, F.D. Yusop, S.-J. Horng
2015 Journal of Computer Assisted Learning  
In this paper, a novel Flowchart-based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem-solving activities  ...  development with the aim of improving their problem-solving skills.  ...  Finally, in order to show the learning outcome, post-test scores were obtained.  ... 
doi:10.1111/jcal.12099 fatcat:yh5btrsa2vc5hontjlnneryyci

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  ...  Intelligent tutoring systems that utilize Bayesian Knowledge Tracing have achieved the ability to accurately predict student performance not only within the intelligent tutoring system, but on paper post-tests  ...  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

Computational Psychometrics for the Measurement of Collaborative Problem Solving Skills

Stephen T. Polyak, Alina A. von Davier, Kurt Peterschmidt
2017 Frontiers in Psychology  
In our post-game analysis, our goal was to discover unique gameplay profiles by performing a cluster analysis of user's sub-skill performance scores based on their patterns of selected dialog responses  ...  In the real-time analysis, our aim was to start with limited knowledge of skill mastery, and then demonstrate a form of continuous Bayesian evidence tracing that updates sub-skill level probabilities as  ...  and can effectively be scored for a participant based on their sub-skill association and level identification.  ... 
doi:10.3389/fpsyg.2017.02029 pmid:29238314 pmcid:PMC5712874 fatcat:4f7jbruc5zddxbszqisghaxyuy
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