The automatic code generation is the process of generating source code snippets from a program, i.e., code for generating code. Its importance lies in facilitating software development, particularly important is helping in the implementation of software designs such as engineering diagrams, in such a case, automatic code generation copes with the…

This study leverages bibliometric analysis through the bibliometrix R-package to dissect the expansive influence of machine learning on education, a field where machine learning's adaptability and data-processing capabilities promise to revolutionize teaching and learning methods. Despite its potential, the integration of machine learning in…

Background and Context: We explore the potential for understanding the processes involved in students' programming based on studying their behaviour and dialogue with each other and "conversations" with their programs. Objective: Our aim is to explore how a perspective of inquiry can be used as a point of departure for insights into how…

Many organizations utilize some form of automation in the test assembly process; either fully algorithmic or heuristically constructed. However, one issue with heuristic models is that when the test assembly problem changes the entire model may need to be re-conceptualized and recoded. In contrast, mixed-integer programming (MIP) is a mathematical…

The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…

As vast amounts of data have become available in business in recent years, the demand for data scientists has been rising. The author of this article provides a tutorial on how one entry-level machine learning competition from Kaggle, an online community for data scientists, can be integrated into an undergraduate econometrics course as an…

In this research project we describe the legacy software landscape, its current state, and challenges associated with aging information systems and access to its data. We briefly describe the popularity of dynamic languages and how a specific dynamic programming language, Ruby on Rails (RoR or Rails), is used to create a system to extract data…

This study aimed to examine the potentials of the NLP approach in detecting discourse markers (DMs), namely okay, in transcribed spoken data. One hundred thirty-eight concordance lines were presented to human referees to judge the functions of okay in them as a DM or Non-DM. After that, the researchers used a Python script written according to the…

Quantitative literacy is necessary to keep pace with the exponentially increasing magnitude of biological data and the complexity of statistical tools. However, statistical programming can cause anxiety in new learners and educators alike. In order to produce graduates that are well-prepared for quantitative research, overcoming the initial…

Curricula in computer engineering, computer science, and other related fields include several courses about hardware design. Examples of these courses are digital logic design, computer architecture, microprocessors, computer interfacing, hardware design, embedded systems, switching theorem, and others. In order for the students to realize the…

One of the biggest hurdles of teaching data science and programming techniques to beginners is simply getting started with the technology. With multiple versions of the same coding language available (e.g., Python 2 and Python 3), various additional libraries and packages to install, as well as integrated development environments to navigate, the…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…

Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students…

Background: Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use…

This study describes the most relevant problems and solutions found in the literature on teaching and learning of object-oriented programming (OOP). The identification of the problem was based on tertiary studies from the IEEE Xplore, Scopus, ACM Digital Library and Science Direct repositories. The problems and solutions identified were ranked…