Sensors and internet of things (IoTs) are ubiquitous in our modern day-to-day living. The past decade has been marked by the rapid emergence and proliferation of a myriad of small devices. Applications range from smart home devices that control cooking ranges to mobile phones, wearable devices that serve as fitness trackers and personalized…

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…

Educational data mining has become an effective tool for exploring the hidden relationships in educational data and predicting students' academic achievements. This study proposes a new model based on machine learning algorithms to predict the final exam grades of undergraduate students, taking their midterm exam grades as the source data. The…

Scientific progress depends on the integrity of data and research findings. Intentionally distorting research data and findings constitutes scientific misconduct and introduces falsehoods into the scientific record. Unintentional distortions arising from questionable research practices (QRPs), such as unsystematically deleting outliers, pose…

Human resources training is considered an effective solution in empowering human resources. Organizations try to have effective educational planning for this precious resource by identifying shortcomings through a need assessment. This study provides a model based on organizational data analysis to achieve a unique and appropriate training…

Progress-monitoring data collection is an essential skill for teachers serving children for whom the general curriculum is insufficient. As the field of early childhood education moves toward tiered service provision models, the importance of routine data collection is heightened. Therefore, we evaluated the effects of a training package on…

This article documents a year-long collaborative self-study of three teacher educators engaged in a practicum innovation in a teacher education program. This study, which is part of a larger study examining practicum-based seminars called "Particulars of Practice" (POP), focuses on exploring our own practices and identities within this…

There are a variety of discrete facility location models that have practical relevance for operations management and management science courses. Integer linear programming (ILP) is the standard technique for solving such problems. An alternative approach that is often conceptually appealing to students is to pose the problem as one of finding the…

Preschool, understood as being both a physical and psychological environment, is, according to objectives in national curriculum, required to be inclusive for all those who attend it. Previous studies have identified the stereotypical norms of several everyday activities of preschools, including both interactions and premises. The aim of this…

Collaborative data inquiry can help schools serve their students better and improve student outcomes--but only if equity is prioritized. Researchers from Harvard's Data Wise Project discuss the importance of using an equity lens when engaging in collaborative data inquiry and what this can mean in terms of disrupting system inequities.

The SQL-Explore Learning Module detailed in this teaching tip provides an opportunity for students to apply database course knowledge beyond solving traditional pre-determined Structured Query Language (SQL) coding questions. In this unique constructivist activity using the apropos 5E Instructional Model, students explore tables to locate data…

This teaching case describes the functionality of a system employing IoT sensors to monitor part levels for a fictitious auto parts manufacturer. Data from the sensors are used to populate a centralized database and generate a dashboard for management. The system also generates tiered alerts to notify part runners and managers of pending part…

This article describes the use of literature to broaden students' skills in content comprehension, data analysis, modeling, and productive scientific discussion. The design builds on existing models to maximize student gains in ability, confidence, and postgraduate preparation. Course units are divided into 1-week modules consisting of three…

This paper interrogates the fundamental logic of data-driven decision-making (DDDM) as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault's understanding of this concept, I center Colin Koopman's recent analysis of the 'informational…