Background: The creation of Intelligent Supervision Platforms in universities leverages Big Data for robust monitoring and decision-making, which significantly enhances overall efficiency and adaptability in educational environments. Objectives: This research focuses on evaluating how Big Data-driven Intelligent Supervision Platforms in…

The use of information and communication technology-based data systems to support teachers in data-driven decision-making (DDDM) remains limited. Despite the growing number of data systems available, their uptake remains limited, and there is a limited understanding of what data system characteristics increase and factors that influence teacher…

In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such…

Libraries hold large amounts of data, which can contribute to improvements in the quality of library services. Data resources of modern library have the characteristics of big-data, where library can use big-data methods to achieve reform and innovation, including resource transferring, resource utilisation, social identity, thinking innovation.…

The digital evaluation field is a new area that arises in the core of education and studies highlight the importance of editing data as well as using ICT to drive internal school improvement. Data- Driven Decision Making (DDDM in advance) executes relatively simple models on carefully targeted data extracted through target questionnaires. This…

Educational analytics tend toward aggregation, asking what a "normative" learner does. In "The Left Hand of Data," educational researchers Matthew Berland and Antero Garcia start from a different assumption--that outliers are, and must be treated as, valued individuals. Berland and Garcia argue that the aim of analytics should…

This study explored the core competencies, technological skills, functional proficiencies, and professional experiences of data scientists at higher education institutions. The specific population of interest was higher education administrators and staff professionals identified as data scientists. This study was informed by the following guiding…

This research was a mixed-methods study investigating how teachers in northeastern West Virginia use student data to drive their instructional decisions. This study also investigated teachers' perceptions of the data use on their instruction, their students, and themselves. Data collection for this research included both a cross-sectional survey…

Purpose: This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational…

Higher education institutions in the United States have collected and analyzed data for decades. From mandatory reporting for state and federal compliance to ad hoc reporting for internal and external stakeholders, there are myriad business purposes for which administrators, staff, and faculty routinely gather data. As more colleges and…