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Showing 1 to 15 of 219 results Save | Export
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Ebru Korkmaz; Selçuk Alkan – SAGE Open, 2023
Identifying the mistakes in student responses and being able to manage the solution process are among the fundamental skills that should be acquired by the pre-service teachers. This skill is important in probability, about which students have a great number of misconceptions. The aim of this study was to determine the misconceptions in the…
Descriptors: Preservice Teachers, Elementary School Mathematics, Probability, Thinking Skills
Lu, Jiannan; Ding, Peng; Dasgupta, Tirthankar – Journal of Educational and Behavioral Statistics, 2018
Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the…
Descriptors: Outcomes of Treatment, Mathematical Applications, Probability, Behavioral Science Research
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Orosi, Greg – International Journal of Mathematical Education in Science and Technology, 2017
In this paper, we derive the result of the classical gambler's ruin problem using elementary linear algebra. Moreover, the pedagogical advantage of the derivation is briefly discussed.
Descriptors: Algebra, Problem Solving, Elementary School Mathematics, Probability
Wu, Teng – ProQuest LLC, 2013
The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…
Descriptors: Employment, Probability, Systems Approach, Mathematical Applications
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Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze – Statistics Education Research Journal, 2016
Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…
Descriptors: Statistics, Probability, Teaching Methods, Foreign Countries
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Kazak, Sibel; Fujita, Taro; Wegerif, Rupert – Statistics Education Research Journal, 2016
The study explores the development of 11-year-old students' informal inference about random bunny hops through student talk and use of computer simulation tools. Our aim in this paper is to draw on dialogic theory to explain how students make shifts in perspective, from intuition-based reasoning to more powerful, formal ways of using probabilistic…
Descriptors: Inferences, Computer Simulation, Probability, Statistical Distributions
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Benakli, Nadia; Kostadinov, Boyan; Satyanarayana, Ashwin; Singh, Satyanand – International Journal of Mathematical Education in Science and Technology, 2017
The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data…
Descriptors: Calculus, Probability, Data Analysis, Computation
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Foster, Colin – Teaching Statistics: An International Journal for Teachers, 2012
This article advocates biased spinners as an engaging context for statistics students. Calculating the probability of a biased spinner landing on a particular side makes valuable connections between probability and other areas of mathematics. (Contains 2 figures and 1 table.)
Descriptors: Statistics, Probability, Statistical Bias, Mathematical Applications
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Jance, Marsha L.; Thomopoulos, Nick T. – American Journal of Business Education, 2011
The paper shows how to find the min and max extreme interval values for the exponential and triangular distributions from the min and max uniform extreme interval values. Tables are provided to show the min and max extreme interval values for the uniform, exponential, and triangular distributions for different probabilities and observation sizes.
Descriptors: Intervals, Probability, Observation, Statistical Distributions
Ghosh, Indranil – ProQuest LLC, 2011
Consider a discrete bivariate random variable (X, Y) with possible values x[subscript 1], x[subscript 2],..., x[subscript I] for X and y[subscript 1], y[subscript 2],..., y[subscript J] for Y. Further suppose that the corresponding families of conditional distributions, for X given values of Y and of Y for given values of X are available. We…
Descriptors: Information Theory, Models, Programming, Mathematical Applications
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Flores, Alfinio; Cauto, Kevin M. – Mathematics Teacher, 2012
This article will describe two activities in which students conduct experiments with random numbers so they can see that having at least one repeated birthday in a group of 40 is not unusual. The first empirical approach was conducted by author Cauto in a secondary school methods course. The second empirical approach was used by author Flores with…
Descriptors: Probability, Secondary School Students, Secondary School Mathematics, Mathematics Instruction
Thipwiwatpotjana, Phantipa – ProQuest LLC, 2010
Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…
Descriptors: Intervals, Programming, Mathematical Applications, Probability
Cai, Li; Monroe, Scott – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2014
We propose a new limited-information goodness of fit test statistic C[subscript 2] for ordinal IRT models. The construction of the new statistic lies formally between the M[subscript 2] statistic of Maydeu-Olivares and Joe (2006), which utilizes first and second order marginal probabilities, and the M*[subscript 2] statistic of Cai and Hansen…
Descriptors: Item Response Theory, Models, Goodness of Fit, Probability
Guan, Tay Eng – Australian Mathematics Teacher, 2011
The famous Birthday Problem is a staple in introductory probability texts and can be stated as follows: What is the probability that at least two persons from "n" randomly chosen persons have the same birthday? In this article, the author suggests an adaptation of the birthday problem which can be repeated in class until the full effect…
Descriptors: Probability, Class Activities, Computation, Problem Solving
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Voskoglou, Michael Gr. – International Journal of Mathematical Education in Science and Technology, 2010
We represent the main stages of the process of mathematical modelling as fuzzy sets in the set of the linguistic labels of negligible, low intermediate, high and complete success by students in each of these stages and we use the total possibilistic uncertainty as a measure of students' modelling capacities. A classroom experiment is also…
Descriptors: Mathematical Models, Experiments, Markov Processes, Matrices
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