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Henry Markovits; Valerie A. Thompson – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
Mental model (Johnson-Laird, 2001) and probabilistic theories (Oaksford & Chater, 2009) claim to provide distinct explanations of human reasoning. However, the dual strategy model of reasoning suggests that this distinction corresponds to different reasoning strategies, termed "counterexample" and "statistical,"…
Descriptors: Abstract Reasoning, Thinking Skills, Learning Strategies, Logical Thinking
Berg, Arthur – Teaching Statistics: An International Journal for Teachers, 2021
The topic of Bayesian updating is explored using standard and non-standard dice as an intuitive and motivating model. Details of calculating posterior probabilities for a discrete distribution are provided, offering a different view to P-values. This article also includes the stars and bars counting technique, a powerful method of counting that is…
Descriptors: Bayesian Statistics, Teaching Methods, Statistics Education, Intuition
Czocher, Jennifer A.; Weber, Keith – Journal for Research in Mathematics Education, 2020
To design and improve instruction in mathematical proof, mathematics educators require an adequate definition of proof that is faithful to mathematical practice and relevant to pedagogical situations. In both mathematics education and the philosophy of mathematics, mathematical proof is typically defined as a type of justification that satisfies a…
Descriptors: Mathematics Instruction, Validity, Mathematical Logic, Definitions
Dvir, Michal; Ben-Zvi, Dani – Mathematical Thinking and Learning: An International Journal, 2023
Employing a statistical modeling inspired pedagogy is becoming a widespread practice in the statistics education community. Many have incorporated the practice of formulating conjectures in their modeling-enhanced educational designs and have reported on its benefits. We further elucidate the mechanism through which students' conjecturing may be…
Descriptors: Mathematics Instruction, Teaching Methods, Statistics Education, Instructional Design
Li, Xiao; Xu, Hanchen; Zhang, Jinming; Chang, Hua-hua – Journal of Educational and Behavioral Statistics, 2023
The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits.…
Descriptors: Learning Processes, Models, Algorithms, Individualized Instruction
Armiati; Fauzan, Ahmad; Harisman, Yulyanti; Sya'bani, Febrina – Journal on Mathematics Education, 2022
This study examines the development of learning designs based on Realistic Mathematics Education (RME) on the probability topic at the eighth-grade junior high school level. Probability abounds in everyday life, and the RME approach is believed to develop students' mathematical communication skills. This learning design development used the Plomp…
Descriptors: Instructional Design, Mathematics Instruction, Mathematics Education, Teaching Methods
Provost, Amanda; Lim, Su San; York, Toni; Panorkou, Nicole – North American Chapter of the International Group for the Psychology of Mathematics Education, 2022
The frequentist and classical models of probability provide students with different lenses through which they can view probability. Prior research showed that students may bridge these two lenses through instructional designs that begin with a clear connection between the two, such as coin tossing. Considering that this connection is not always…
Descriptors: Probability, Models, Mathematics Instruction, Teaching Methods
Rodriguez, Jon-Marc G.; Stricker, Avery R.; Becker, Nicole M. – Chemistry Education Research and Practice, 2020
Explanations of phenomena in chemistry are grounded in discussions of particulate-level behavior, but there are limitations to focusing on single particles, or as an extension, viewing a group of particles as displaying uniform behavior. More sophisticated models of physical processes evoke considerations related to the dynamic nature of bulk…
Descriptors: Science Instruction, Chemistry, Undergraduate Students, College Science
Godino, Juan D.; Rivas, Hernán; Burgos, María; Wilhelmi, Miguel R. – International Electronic Journal of Mathematics Education, 2019
There is currently a consensus in mathematics education that favors constructivist instructional models, which are based on the inquiry of knowledge by students. There are, however, different views that consider objectivist models, based on knowledge transmission (direct or explicit teaching) more effective in the teaching of scientific…
Descriptors: Mathematics Instruction, Teaching Methods, Constructivism (Learning), Probability
Jungck, John R. – PRIMUS, 2022
Finite Mathematics has become an enormously rich and productive area of contemporary mathematical biology. Fortunately, educators have developed educational modules based upon many of the models that have used Finite Mathematics in mathematical biology research. A sufficient variety of computer modules that employ graph theory (phylogenetic trees,…
Descriptors: Mathematics Instruction, Teaching Methods, Mathematical Models, Learning Modules
Coronado, Semei; Sandoval-Bravo, Salvador; Celso-Arellano, Pedro Luis; Torres-Mata, Ana – European Journal of Contemporary Education, 2018
The purpose of this paper is to analyze the test applied at the eighth Statistics II tournament to students from the University Center for Economic and Administrative Sciences of the University of Guadalajara, for the purpose of determining whether it promotes competitive learning among students. To achieve this, Item Response Theory (IRT) is…
Descriptors: Models, Competition, Item Response Theory, Student Motivation
Letkowski, Jerzy – Journal of Instructional Pedagogies, 2018
Single-period inventory models with uncertain demand are very well known in the business analytics community. Typically, such models are rule-based functions, or sets of functions, of one decision variable (order quantity) and one random variable (demand). In academics, the models are taught selectively and usually not completely. Students are…
Descriptors: Models, Data Analysis, Decision Making, Teaching Methods
Mao, Ye; Lin, Chen; Chi, Min – Journal of Educational Data Mining, 2018
Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…
Descriptors: Prediction, Pretests Posttests, Bayesian Statistics, Short Term Memory
Kuzniak, Alain; Nechache, Assia; Drouhard, J. P. – ZDM: The International Journal on Mathematics Education, 2016
According to our approach to mathematics education, the optimal aim of the teaching of mathematics is to assist students in achieving efficient mathematical work. But, what does efficient exactly mean in that case? And how can teachers reach this objective? The model of Mathematical Working Spaces with its three dimensions--semiotic, instrumental,…
Descriptors: Mathematics Education, Teaching Methods, Grade 9, Grade 10
Lee, Hollylynne S.; Doerr, Helen M.; Tran, Dung; Lovett, Jennifer N. – Statistics Education Research Journal, 2016
Repeated sampling approaches to inference that rely on simulations have recently gained prominence in statistics education, and probabilistic concepts are at the core of this approach. In this approach, learners need to develop a mapping among the problem situation, a physical enactment, computer representations, and the underlying randomization…
Descriptors: Probability, Inferences, Statistics, Teaching Methods