Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these…

Forecasting the enrollments of new students in bachelor's systems became an urgent desire in the majority of higher education institutions. It represents an important stage in the process of making strategic decisions for new course's accreditation and optimization of resources. To gain a deep view of the educational forecasting context, the most…

This book presents different data collection and representation techniques: elementary descriptive statistics, confirmatory statistics, multivariate approaches and statistical modeling. It exposes the possibility of giving more robustness to the classical methodologies of education sciences by adding a quantitative approach. The fundamentals of…

This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science--such as visualization, data manipulation, and database usage--that instructors at a wide-range of institutions can incorporate into existing statistics…

The traditional "leaky pipeline" plots are widely used to inform gender equality policy and practice. Herein, we demonstrate how a statistical phenomenon known as Simpson's paradox can obscure trends in gender "leaky pipeline" plots. Our approach has been to use Excel spreadsheets to generate hypothetical "leaky…

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…

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional…

The article is devoted to elaboration and construction of a static model of macroeconomics in which economics is considered as an unstructured holistic unit, the input of which receives the resources, and the output is the result of the functioning of economics in the form of gross domestic product or gross regional product. Resources are…

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

School nurses collect data to report to their school district and state agencies. However, there is no national requirement or standard to collect specific data, and each state determines its own set of questions. This study resulted from a joint resolution between the National Association of State School Nurse Consultants and the National…

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…

The goal of this research which was carried out in reputable dedicated call centres within the Turkish telecommunication sector aims is to evaluate competence-based curriculums designed by means of internal funding through Stufflebeam's context, input, process, product (CIPP) model. In the research, a general scanning pattern in the scope of…

This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and…

This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting,…