The "Building Your Research Data Management Toolkit" was developed to provide introductory research data management skills training to liaisons in academic libraries. This paper assesses the participants' perceived change in knowledge, behaviors and attitudes as a result of participation in the RoadShow program. Long term changes in…

The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers…

The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit with panel data. Two challenges arise with higher frequency data (e.g., monthly versus yearly): (1) achieving excellent pre-treatment fit is typically more challenging; and (2) overfitting to noise is more likely. Aggregating data…

Generative artificial intelligence (AI) has shown the potential to reshape the world and redefine daily workflows. One specific instance of generative AI, ChatGPT, specializes in understanding natural language and generating human-like conversational text. Its free access, user-friendly interface, and instant feedback have propelled its popularity…

As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the "what" and "how" of data science in pre-collegiate education have not yet reached consensus. This paper…

This teaching brief explores how and why location analytics should be taught in business schools using three objectives. First, an explanation is provided for the importance of including location analytics in the standard business school curriculum--especially in the field of supply chain management. Second, a lack of GIS-based location analytics…

Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication…

Qualitative studies in the domain of physics education research have become more common in the last several decades. Methodologically, this has been marked by an expansion of the types of data collected in physics education research (PER): namely, in the use of individual and group interviews, problem-solving sessions, and classroom…

With the 5th Edition of Data Analysis for Continuous School Improvement, best-selling Victoria Bernhardt has written the go-to-resource for data analysis in your school! By incorporating collaborative structures to implement, monitor, and evaluate the vision and continuous improvement plan, this book provides a framework to show learning…

In 2003, Holmes [The Statistician 52, Part 4, (2003), 439-474] reviewed 50 years of teaching statistics in English schools and drew out many lessons for more effective teaching. We consider the current relevance of several of these lessons and whether we have learned from them. Many national reports have been published about teaching statistics…

Purpose: Scholars are increasingly paying attention to the characteristics of effective citizenship education. The systematic use of data to maximise student learning, also called an output-driven approach, is often presented as a powerful predictor of student outcomes. However, its effectiveness has not been studied in citizenship education.…

We are living in the Big Data era, and yet we may have serious troubles when dealing with a handful of kinetic data if we are not properly instructed. The aim of this paper, related to enzyme kinetics, is to illustrate how to determine the K[subscript m] and V[subscript max] of a michaelian enzyme avoiding the pitfalls in which we often fall. To…

Analysis of symmetrically predicted endogenous subgroups (ASPES) is an approach to assessing heterogeneity in an ITT effect from a randomized experiment when an intermediate variable (one that is measured after random assignment and before outcomes) is hypothesized to be related to the ITT effect, but is only measured in one group. For example,…

Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In…

Data modeling practices are often invisible to students in introductory biology courses. However, developing a well-rounded understanding of these practices is critical for scientific literacy. Furthermore, introductory undergraduate science laboratory courses are often taught by graduate students or novice instructors with little autonomy,…