The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical…

This study examines modelling with machine learning. In the context of a yearlong data science course, the study explores how upper secondary students apply machine learning with Jupyter Notebooks and document the modelling process as a computational essay incorporating the different steps of the CRISP-DM cycle. The students' work is based on a…

Addressing societal issues in civil and environmental engineering increasingly requires skills in data science and programming. To date, there is not much known about the extent students are learning these skills in current civil and environmental engineering curricula. We conducted a survey of accredited civil and environmental engineering…

A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and ScikitLearn. The tools are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history,…

In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different…

This study aims to examine the effects of mixture item response theory (IRT) models on item parameter estimation and classification accuracy under different conditions. The manipulated variables of the simulation study are set as mixture IRT models (Rasch, 2PL, 3PL); sample size (600, 1000); the number of items (10, 30); the number of latent…

Teaching students statistical programming languages while simultaneously teaching them how to debug erroneous code is challenging. The traditional programming course focuses on error-free learning in class while students' experiences outside of class typically involve error-full learning. While error-free teaching consists of focused lectures…

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…

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…

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…

In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new students, the road map for teaching data science remains elusive. In 2019, 69 college and university faculty teaching data science courses and…

Electronic sports (e-sports) can be defined as digital games played competitively for an audience (Hodge et al., 2017). With a global consumer base of roughly 450 million people and projected 2019 revenues of US$1.1 billion, the e-sports industry continues to grow (Pannekeet, 2019). Behind this growth is a thriving ecosystem which includes…

In the era of big data, many business organizations consider data analytics skills as important criteria in the acquisition of qualified applicants. As numerous managerial decisions in the field of marketing are becoming evidence-based, business schools have integrated case studies about different stages of data analytics such as problem…