The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and…

Industrial drug discovery teams encompass scientists from multiple specialties and require participants to communicate effectively across disciplinary boundaries. In this paper, we present an undergraduate or graduate classroom simulation of this environment. Over a series of five workshops, student teams of mixed scientific backgrounds perform…

The increasing use of massive data requires effective teaching strategies for business analytics. The discipline combines analytical skills, information technology, and business-domain knowledge, and is challenging to teach to business students. This research demonstrates that business undergraduate students can benefit from an experiential…

The COVID-19 pandemic is a historic global event that has extended to all parts of society and shaken the core of what we know and how we live. During this pandemic, the work of STEM professionals has taken center stage. Through our close observations of how the events of the pandemic have been unfolding across the globe, we propose an…

In all areas of human activity, decision-making based on data analysis is very important. As the availability of data grows, it becomes critical to educate not only traditional students but also those individuals who are now in the workforce, as many of them are expected to manage the complex data streams and to provide evidence and guidance for…

There is a growing body of research on physiological synchrony (PS) in Collaborative Problem Solving (CPS). However, the current literature presents inconclusive findings about the way in which PS is reflected in cognitive and affective group processes and performance. In light of this, this study investigates the relationship between PS and…

Current teacher preparation programs have received scrutiny for failing to sufficiently support teachers in valuing and developing their knowledge of data literacy. The current study explores a collaborative approach to increasing pre-service teachers' experiences with data literacy by including under-utilized data experts: school psychologists.…

A multiple case study was designed to examine the components of data literacy used by formal and informal leaders to support the work of Data Teams. Nine formal leaders and ten informal leaders constituted the participants. One overarching central question and seven subquestions guided the research. The answer to the central question, How do…

Single-period inventory models with uncertain demand are very well known in the business analytics community. Typically, such models are rule-based functions, or sets of functions, of one decision variable (order quantity) and one random variable (demand). In academics, the models are taught selectively and usually not completely. Students are…

Educational Data Mining is an emerging discipline which seeks to develop methods to explore large amounts of data from educational settings, in order to understand students' behavior, interests and results in a better way. In recent years there have been various works related to this specialty and multiple data mining techniques derived from this…

In this study, we examined how in-service middle-level mathematics teachers used statistics in their own classroom research. Using an embedded single-case design, we analyzed a purposefully selected sample of nine teachers' classroom research papers, identifying several themes within each phase of the statistical problem solving process to…

An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students' behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address…

In this paper, I share the results of a study of teachers' ideas about student decision-making at entry into a professional development program to integrate engineering into their instruction. The framework for the Engineering Design Process (EDP) was based on a Challenge-Based Learning (CBL) model. The EDP embedded within the CBL model suggests…

The development of a student's ability to make data-driven decisions has become a focus in higher education (Schield 1999; Stephenson and Caravello 2007). Data literacy, the ability to understand and use data to effectively inform decisions, is a fundamental component of information competence (Mandinach and Gummer 2013; Stephenson and Caravello,…

In this study, we applied decision trees (DT) to extract a compact set of pedagogical decision-making rules from an original "full" set of 3,702 Reinforcement Learning (RL)- induced rules, referred to as the DT-RL rules and Full-RL rules respectively. We then evaluated the effectiveness of the two rule sets against a baseline Random…