In regression analysis courses, there are many settings in which the response variable under study is continuous, strictly positive, and right skew. This type of response variable does not adhere to the normality assumptions underlying the traditional linear regression model, and accordingly may be analyzed using a generalized linear model…

Through the lenses of statistical investigations and cognitive demands, we examined bivariate data tasks offered in US high school mathematics textbook series--a popular representative of three curriculum types: traditional, integrated, and hybrid. We developed a framework grounded in literature of association topics for the inclusion and…

The purpose is to improve insights and educational results by applying analytic methods. The focus is on the mathematics applied to learn from the kind of data available to most classes such as final examination marks or homework grades. The sample is 249 students learning introductory college statistics. The result is a predictive model for…

Statistical reasoning focuses on properties that belong not to individual data values but to the entire aggregate. We analyze students' statements from three different sources to explore possible building blocks of the idea of data as aggregate and speculate on how young students go about putting these ideas together. We identify four general…

In the identification process, there may be gifted students who may be unnoticed or students who are misdiagnosed and are disappointed. In this context, this study is a step that may solve these two problems about the identification of mathematically gifted students with the help of data mining, which is data analysis methodology that has been…

The aim of the present study was to examine the mathematical modelling studies done between 2004 and 2015 in Turkey and to reveal their tendencies. Forty-nine studies were selected using purposeful sampling based on the term, "mathematical modelling" with Higher Education Academic Search Engine. They were analyzed with content analysis.…

Research Findings: Quality early science education is important for addressing the low science achievement, compared to international peers, of elementary students in the United States. Teachers' beliefs about their skills in a content area, that is, their content self-efficacy is important because it has implications for teaching practice and…

Instead of reserving the study of probability and statistics for special fourth-year high school courses, the Common Core State Standards for Mathematics (CCSSM) takes a "statistics for all" approach. The standards recommend that students in grades 6-8 learn to summarize and describe data distributions, understand probability, draw…

This research traced changes in choices of technological tools and attitudes toward technology use among novice mathematics teachers at three stages of their professional development: as pre-service teachers, a year later, and in their work as novice teachers. At each stage, the participants were required to evaluate the benefits of technology use…

Blended learning combines online digital resources with traditional classroom activities and enables students to attain higher learning performance through well-defined interactive strategies involving online and traditional learning activities. Learning analytics is a conceptual framework and is a part of our Precision education used to analyze…

Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained…

As the use of educational technology becomes more ubiquitous, an enormous amount of learning process data is being produced. Educational data mining seeks to analyze and model these data, with the ultimate goal of improving learning outcomes. The most firmly grounded and rigorous evaluation of an educational data mining discovery is whether it…

As a consequence of the increased use of data in workplace environments, there is a need to understand the demands that are placed on users to make sense of such data. In education, teachers are being increasingly expected to interpret and apply complex data about student and school performance, and, yet it is not clear that they always have the…

The term "data snooping" refers to the practice of choosing which statistical analyses to apply to a set of data after having first looked at those data. Data snooping contradicts a fundamental precept of applied statistics, that the scheme of analysis is to be planned in advance. In this column, the authors shall elucidate the…

Numeracy in schools is becoming an increasingly important part of mathematics learning and teaching. This is because educators want students to engage with mathematical concepts more deeply, use mathematics to make sense of their environment and make decisions that are based on the analysis of mathematical information. In order to be numerate,…