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Zhenchang Xia; Nan Dong; Jia Wu; Chuanguo Ma – IEEE Transactions on Learning Technologies, 2024
As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…
Descriptors: Graphs, Artificial Intelligence, Multivariate Analysis, Prediction
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Peterson, Anna D.; Ziegler, Laura – Journal of Statistics and Data Science Education, 2021
We present an innovative activity that uses data about LEGO sets to help students self-discover multiple linear regressions. Students are guided to predict the price of a LEGO set posted on Amazon.com (Amazon price) using LEGO characteristics such as the number of pieces, the theme (i.e., product line), and the general size of the pieces. By…
Descriptors: Toys, Statistics Education, Teaching Methods, Regression (Statistics)
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Powers, Daniel A. – New Directions for Institutional Research, 2012
The methods and models for categorical data analysis cover considerable ground, ranging from regression-type models for binary and binomial data, count data, to ordered and unordered polytomous variables, as well as regression models that mix qualitative and continuous data. This article focuses on methods for binary or binomial data, which are…
Descriptors: Institutional Research, Educational Research, Data Analysis, Research Methodology
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
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Yetkiner, Zeynep Ebrar – Middle Grades Research Journal, 2009
Commonality analysis is a method of partitioning variance to determine the predictive ability unique to each predictor (or predictor set) and common to two or more of the predictors (or predictor sets). The purposes of the present paper are to (a) explain commonality analysis in a multiple regression context as an alternative for middle grades…
Descriptors: Multivariate Analysis, Correlation, Regression (Statistics), Prediction
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De Corte, Wilfried – Educational and Psychological Measurement, 2000
Shows how a theorem proven by H. Brogden (1951, 1959) can be used to estimate the allocation average (a predictor based classification of a test battery) assuming that the predictor intercorrelations and validities are known and that the predictor variables have a joint multivariate normal distribution. (SLD)
Descriptors: Classification, Correlation, Estimation (Mathematics), Multivariate Analysis
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Timm, Neil H. – Journal of Educational and Behavioral Statistics, 2002
Shows how to test the hypothesis that a nonnested model fits a set of predictors when modeling multiple effect sizes in meta-analysis. Illustrates the procedure using data from previous studies of the effectiveness of coaching on performance on the Scholastic Aptitude Test. (SLD)
Descriptors: Effect Size, Meta Analysis, Models, Multivariate Analysis
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Hoeksma, Jan B.; Knol, Dirk L. – Multivariate Behavioral Research, 2001
Makes the case that hierarchical linear models or longitudinal multilevel models are a better alternative than standard regression models for empirical tests of predictive developmental hypotheses. Describes a multivariate longitudinal model linking developmental data to a criterion and presents an example from a study of the prediction of infant…
Descriptors: Behavior Patterns, Case Studies, Development, Hypothesis Testing
Tobias, Sigmund; Everson, Howard – College Entrance Examination Board, 1996
This report describes 12 studies dealing with the knowledge monitoring component of metacognition. It is assumed that knowledge monitoring is basic to other metacognitive activities, such as evaluating learning, selecting appropriate strategies, or planning, because distinguishing between what students know and do not know ought to be a…
Descriptors: Metacognition, Learning, Knowledge Level, Multiple Choice Tests