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Lazrig, Ibrahim; Humpherys, Sean L. – Information Systems Education Journal, 2022
Can sentiment analysis be used in an educational context to help teachers and researchers evaluate students' learning experiences? Are sentiment analyzing algorithms accurate enough to replace multiple human raters in educational research? A dataset of 333 students evaluating a learning experience was acquired with positive, negative, and neutral…
Descriptors: College Students, Learning Analytics, Educational Research, Learning Experience
Kaser, Tanja; Klingler, Severin; Schwing, Alexander G.; Gross, Markus – IEEE Transactions on Learning Technologies, 2017
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and…
Descriptors: Bayesian Statistics, Models, Intelligent Tutoring Systems, Networks
Martin-Fernandez, Manuel; Revuelta, Javier – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
This study compares the performance of two estimation algorithms of new usage, the Metropolis-Hastings Robins-Monro (MHRM) and the Hamiltonian MCMC (HMC), with two consolidated algorithms in the psychometric literature, the marginal likelihood via EM algorithm (MML-EM) and the Markov chain Monte Carlo (MCMC), in the estimation of multidimensional…
Descriptors: Bayesian Statistics, Item Response Theory, Models, Comparative Analysis
Martori, Francesc; Cuadros, Jordi; González-Sabaté, Lucinio – International Educational Data Mining Society, 2015
Student modeling can help guide the behavior of a cognitive tutor system and provide insight to researchers on understanding how students learn. In this context, Bayesian Knowledge Tracing (BKT) is one of the most popular knowledge inference models due to its predictive accuracy, interpretability and ability to infer student knowledge. However,…
Descriptors: Bayesian Statistics, Inferences, Prediction, Accuracy
Lang, Charles William McLeod – ProQuest LLC, 2015
Personalization, the idea that teaching can be tailored to each students' needs, has been a goal for the educational enterprise for at least 2,500 years (Regian, Shute, & Shute, 2013, p.2). Recently personalization has picked up speed with the advent of mobile computing, the Internet and increases in computer processing power. These changes…
Descriptors: Individualized Instruction, Electronic Learning, Mathematics, Bayesian Statistics
Zhang, Dake; Stecker, Pamela; Huckabee, Sloan; Miller, Rhonda – Journal of Learning Disabilities, 2016
Research has suggested that different strategies used when solving fraction problems are highly correlated with students' problem-solving accuracy. This study (a) utilized latent profile modeling to classify students into three different strategic developmental levels in solving fraction comparison problems and (b) accordingly provided…
Descriptors: Middle School Students, Fractions, Mathematics Achievement, Low Achievement
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries

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