While previous educational governance literature on datafication has paid attention to comparison across spatial entities like countries and schools, temporal comparison in terms of progression (including prediction) has received less attention. One of the material forms in which progression and prediction data are circulated is the visual form of…

Assessment data play a crucial role in facilitating informed decision-making. In the context of student affairs professionals aiming to empirically demonstrate the significance of connection, belonging, and wellness within a holistic campus learning environment, the need for formative data is becoming increasingly valuable. The article outlines…

Higher education leaders face growing pressure to demonstrate the return on investment (ROI) of not only their institution but also each academic program offering. With increasing accountability and limited time for meaningful reflection on student learning and development, now is the time to rethink how a quasi-annual academic program review…

Big Data offers opportunities and challenges in all aspects of human life. In relation to research ethics, Big Data represents a normative difference in degree rather than a difference in kind. Data are more messy, rapid, difficult to predict, and difficult to identify owners; but the principles of informed consent, confidentiality, and prevention…

The traditional system focuses excessively on physical skills and physical fitness assessment, with problems such as single indicator, static approach, subject limitation and inefficient data utilization, making it difficult to assess students in a comprehensive and fair manner. The rise of big data technology has brought about a turnaround, from…

The Australian Universities Accord Final Report offers a historic yet insufficient opportunity to advance Indigenous self-determination in higher education. Its goals will remain hollow without dismantling the entrenched colonial foundations embedded in universities' governance and data practices. This paper demands that Indigenous data…

This study demonstrates how to evaluate a university-wide online course designed to support student transition into university by using Propensity Score Matching (PSM) and Doubly Robust Estimation (DRE). Using data from seven academic years, from 2016/17 to 2022/23, with more than 28,000 students, we examine whether enrolment in this optional…

In the boxplot, the box always represents -- regardless of its area -- the middle half of the data and thus a measure of variability (interquartile range). However, when students first learn about boxplots, they are usual already familiar with other forms of statistical representations (e.g., bar or circle graphs) in which a larger area represents…

The concept of statistical sense provides an understanding of the goals of statistics education and helps to clarify the design of activities that promote the development of statistical literacy, reasoning and thinking. The new approaches to statistics in schools mean special attention must be paid to teacher training. This training should enable…

Because data-informed decision-making (DIDM) can help teachers meet diverse learners' needs (van Geel et al., 2016), educator preparation programs (EPPs) must ensure that preservice teachers (PSTs) develop the data literacy skills needed for effective data use. However, little is known about the ways in which EPPs work towards building PSTs' data…

States serve as the central authority in higher education oversight, playing a critical role in consumer protection and quality assurance within the regulatory triad and as an independent regulatory entity. However, there is a notable gap in understanding the components of renewal processes, how agencies implement them in practice, and the…

Student outcome data is essential for decision-making in higher education, informing choices at the student, institutional, and state levels. Within state authorization--the gatekeeping process for institutional entry, continued operation, and closure--these data support oversight, accountability, and consumer transparency. This brief summarizes…

Multimodal learning analysis emphasizes using diverse data from various sources and forms for precise examination of learning patterns. Despite recent rapid advancements in this field, conventional learning analysis remains predominantly cross-sectional and group-focused, which is insufficient for understanding continuous and personalized learning…

The integration of Internet technology and the collaborative development of smart classrooms is an essential step for colleges and universities to advance English instruction reform. This study utilized data mining (DM) technology to analyze the learning process in college English smart classrooms. The results indicate that the DM algorithm used…

Learning analytics are starting to become standardized in higher education as institutions use the techniques of Big Data analytics to make decisions to help them reach their goals. The widespread use of student information brings forth ethical concerns primarily in relation to privacy. While the overarching ethical issues related to learning…