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Tim Erickson – Australian Mathematics Education Journal, 2024
This is the third in a series of articles describing CODAP and where it might be used to address content in the "Australian Curriculum: Mathematics" v9.0 (ACARA, 2022). We've talked before about model-ling and about statistics; this time, we'll talk about exploring probability using CODAP. As before, we have also prepared online pages…
Descriptors: Statistics Education, Data Analysis, Mathematical Concepts, Mathematics Curriculum
Kopanidis, Foula Zografina; Shaw, Michael John – Education & Training, 2017
Purpose: Educational institutions are caught between increasing their offer rates and attracting and retaining those prospective students who are most suited to course completion. The purpose of this paper is to demonstrate the influence of demographic and psychological constructs on students' preferences when choosing to study in a particular…
Descriptors: Student Attitudes, Course Selection (Students), Preferences, Models
Watson, Jane; English, Lyn – Australian Primary Mathematics Classroom, 2013
Jane Watson and Lyn English use a chance activity exploring expectation and variation with coin tossing to highlight the importance of understanding the part-whole relationship embodied in percentage and its power to measure and compare for different wholes, in this case different sample sizes. The purpose of this article is to raise awareness of…
Descriptors: Numbers, Probability, Mathematical Concepts, Mathematics Instruction
Watson, Jane; English, Lyn – Australian Mathematics Teacher, 2015
By the time students reach the middle years they have experienced many chance activities based on dice. Common among these are rolling one die to explore the relationship of frequency and theoretical probability, and rolling two dice and summing the outcomes to consider their probabilities. Although dice may be considered overused by some, the…
Descriptors: Mathematics Instruction, Manipulative Materials, Simulation, Technology Uses in Education
Stewart, Wayne; Stewart, Sepideh – PRIMUS, 2014
For many scientists, researchers and students Markov chain Monte Carlo (MCMC) simulation is an important and necessary tool to perform Bayesian analyses. The simulation is often presented as a mathematical algorithm and then translated into an appropriate computer program. However, this can result in overlooking the fundamental and deeper…
Descriptors: Markov Processes, Monte Carlo Methods, College Mathematics, Mathematics Instruction