Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's…

In today's university era, reforming the English teaching model has become a major research topic for researchers. Based on this, this paper adopts a hierarchical and progressive model construction method to further explore the reform model of university English teaching in the context of educational ecology. First, this paper discusses the…

This study explores learner engagement with Advanced Computational Thinking (ACT) in the New Zealand digital curriculum. "Advanced" in ACT refers to an expansive, transdisciplinary, and future-looking understanding of computational thinking (CT). ACT promotes CT beyond narrow modes of problem-solving (abstraction, algorithmic thinking,…

In the era of artificial intelligence (AI), big data (BD), and digital transformation (DT), analytics students should gain the ability to solve business problems by integrating various methods. This teaching brief illustrates how two such methods--Bayesian analysis and Markov chains--can be combined to enhance student learning using the Analytics…

In the fall of 2009 and in the spring of 2012, supported by the National Institute of General Medical Sciences (NIGMS) in the National Institutes of Health (NIH), we designed a course "Phylogenetic Analysis and Molecular Evolution" (PAME), the first cross-listed course across three different colleges (College of Arts and Sciences,…

Workforce planning is prevalent and recognized as a good strategic practice in many organizations. However, business students may have little experience with workforce planning or workforce analytics. The purpose of this article is to present a workforce planning exercise for use in a face-to-face or online classroom setting. In this exercise,…

The tremendous growth in electronic educational data creates the need to have meaningful information extracted from it. Educational Data Mining (EDM) is an exciting research area that can reveal valuable knowledge from educational databases. This knowledge can be used for many purposes, including identifying dropouts or weak students who need…

Background and Aims: The fastest growing group of students with disabilities are those with Autism Spectrum Disorder (ASD). States annually report on post-high school outcomes (PSO) of exited students. This study sought to fill two gaps in the literature related to PSO for exited high-school students with ASD and the use of state data and…

Background and Context: Assessing team members' indivdiual contributions to software development projects poses a key problem for computing instructors. While instructors typically rely on subjective assessments, objective assessments could provide a more robust picture. To explore this possibility, In a 2020 paper, Buffardi presented a…

The purpose of this research is to study the gap between industry expectations and the current competencies of bachelor's degree industrial engineering graduates in Khon Kaen University in Thailand 4.0 era so that the research results of this study may be used as the guideline for improvement of the existing Bachelor of Engineering Degree program…

This article describes experiences with teaching process data analytics and machine learning, including in: (1) a joint undergraduate/graduate course for students in chemical and mechanical engineering and engineering management; and (2) an undergraduate chemical engineering concentration in process data analytics. The article also describes…

In 2019, the National Center for Education Statistics (NCES) began exploratory data collection for the School Pension Survey (SPS). The SPS is a new data collection of elementary/secondary school teacher pension data collected at the school district level. The SPS was developed primarily in response to public demand for data on teacher and other…

Student surveys are widely used to evaluate university teaching and increasingly adopted at the K-12 level, although there remains considerable debate about what they measure. Much disagreement focuses on the well-documented correlation between student grades and their evaluations of instructors. Using individual-level data from 19,000 evaluations…

Scientific Data Analysis Toolkit (SDAT) is a rigorous, versatile, and user-friendly data analysis add-in application for Microsoft Excel for Windows (PC). SDAT uses the familiar Excel environment to carry out most of the analytical tasks used in data analysis. It has been designed for student use in manipulating and analyzing data encountered in…

The purpose of this article is to provide an overview of a structured, rigorous approach to collaborative qualitative analysis while attending to challenges associated with working in team environments. The method is rooted in qualitative data analysis literature related to thematic analysis, as well as the constant comparative method. It seeks to…