Contribution: The main contribution is to provide practitioners and researchers with an insight in efficiently and effectively employing multi-attribute decision making (MADM) methods in e-learning website selection problems. Background: Advances in information systems and the Internet have resulted in e-learning websites becoming an important…

Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review…

The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC, however, is not suitable for evaluating models with order constraints on the parameters of interest. This article explores two extensions of the BIC for evaluating order-constrained models, one where a…

This study aimed to visualize self-regulated learning (SRL) behaviors performed by users from an online teacher professional development platform called the EdHub Library using the pm4py algorithm in Python to parse event data during the first 30 days of the school year and the first 90 days of the COVID-19 pandemic in March 2020. Process mining…

Rough set theory (RST) was proposed by Zdzistaw Pawlak (Pawlak,1982) as a methodology for data analysis using the notion of discernibility of objects based on their attribute values. The main advantage of using RST approach is that it does not need additional assumptions--like data distribution in statistical analysis. Besides, it provides…

Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science…

In this work three examples of textbook circuits (resistor-capacitor, resistor-inductor and resistor-inductor-capacitor) have been modeled by employing the Euler method for the approximate solution of differential equations using algorithms implemented in the "Python" programming language. The aim of this work was to demonstrate how…

Computational notebooks are an increasingly common tool used to support student learning in a variety of contexts where computer programming can be applied. These notebooks provide an easily distributable method of displaying text and images, as well as sections of computer code that can be manipulated and run in real-time. This format allows…

In recent years, there has been a renewed interest in the introduction of programming in teacher education and professional development, highlighting its importance for the development of so-called computational thinking. This study explored primary education teachers' participation in programming practices. By focusing on their views of creating…

Programming and coding skills are in high demand, and can provide access to employment in growing fields. But a high percentage of undergraduates who enroll in relevant programs do not persist until they achieve competency in the subject and employment in the field. Revel for "Introduction to Java Programming" aims to give students an…

Programming and coding skills are in high demand, and can provide access to employment in growing fields. But a high percentage of undergraduates who enroll in relevant programs do not persist until they achieve competency in the subject and employment in the field. Revel is an interactive learning environment intended to help students prepare for…

In this study, the effects of Arduino IDE and C++ programming languages were investigated on the computational thinking skills of preservice teachers. The Computational Thinking Skills Scale was administered to preservice teachers. Firstly, a basic programming training was given and then it was asked to create group projects on a voluntary basis.…

Controlling complexity through the use of abstractions is a critical part of problem solving in programming. Thus, becoming proficient with procedural and data abstraction through the use of user-defined functions is important. Properly using functions for abstraction involves a number of other core concepts, such as parameter passing, scope and…

Biology is a data-driven discipline facilitated greatly by computer programming skills. This article describes an introductory experiential programming activity that can be integrated into distance learning environments. Students are asked to develop their own Python programs to identify the nature of alleles linked to disease. This activity…

This study investigated the scalability of a cognitive multiple-choice test through the Mokken package in the R programming language for statistical computing. A 2019 mathematics West African Examinations Council (WAEC) instrument was used to gather data from randomly drawn K-12 participants (N = 2866; Male = 1232; Female = 1634; Mean age = 16.5…