Data science programming presents many challenges for programmers entering the field. Roughly, data science programming can be broken up into several activities: data wrangling, analysis, modeling, or visualization. Data wrangling is an important first step that involves cleaning and shaping tabular data--or dataframes--into a form amenable for…

Programming languages have been used and developed in the field of education. Python programming was employed in this study. The goal of this study is to see whether there is an effect of using Pydroid in mathematics learning on students' problem-solving abilities and to see if this software satisfies or does not match the criteria for mathematics…

We report an educational tool for the upper level undergraduate quantum chemistry or quantum physics course that uses a symbolic approach via the PySyComp Python library. The tool covers both time-independent and time-dependent quantum chemistry, with the latter rarely considered in the foundations course due to topic complexity. We use quantized…

Physics, even though it is guided by simple principles, tends for many topics to be obscured in the mathematics redundancy. MATLAB®, as interactive software for computer algebra, has already had an important impact on the way physics is taught by educators. It has also had a substantial impact on the way research is performed by students to…

Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to "approaches taken by the students," we compare source code on a more abstract, semantic level. Even if novice student's solutions follow the same idea,…

Spark, one of the products offered by MyQ (formerly Plethora), is a game-based platform meticulously designed to introduce students to the foundational concepts of computer science. By navigating through logical challenges, users delve into topics like abstraction, loops, and graph patterns. Setting itself apart from its counterparts, Spark boasts…

This study explores the effects of worked-out examples and metacognitive scaffolding on novice learners' knowledge performance, cognitive loads, and self-regulation skills in problem-solving programming. 126 undergraduate students in a computer programming fundamentals course were randomly assigned to one of four groups: (1) task performance with…

Programming is an interdisciplinary practice with applications in both mathematics and computer science. Mathematics concerns rigor, abstraction, and generalization. Computer science predominantly concerns efficiency, concreteness, and physicality. This makes programming a medium for problem solving that mediates between mathematics and computer…

Block-based programing languages (BBPL) provide effective scaffolding for K-12 students to learn computational thinking. However, the output-based assessment in BBPL learning is insufficient as we can not understand how students learn and what mistakes they have had. This study aims to propose a data-driven method that provides insight into…

Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever-growing field of business analytics, including implementing real-life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the…

This study used the ARCS approach to investigate the effects of university students' motivation, including attention, relevance, confidence, and satisfaction, to use the Programming Teaching Assistant (PTA) on their Programming Problem-Solving Skills (PPSS). Previous studies have shown that PTA features enhance learners' programming performance,…

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…

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…

When we "think like a computer scientist," we are able to systematically solve problems in different fields, create software applications that support various needs, and design artefacts that model complex systems. Abstraction is a soft skill embedded in all those endeavours, being a main cornerstone of computational thinking. Our…

Concurrency is often perceived as difficult by students. One reason for this may be due to the fact that abstractions used in concurrent programs leave more situations undefined compared to sequential programs (e.g., in what order statements are executed), which makes it harder to create a proper mental model of the execution environment. Students…