In today's digital world, higher education institutions collect and use more data than ever. However, institutional silos create barriers for stakeholders who need data for daily operations and strategy. This article presents a vision of a unified, collaborative future for data governance and actionable steps stakeholders can take.

Many research agencies are now requiring that data collected as part of funded projects be shared. However, the practice of data sharing in education sciences has lagged these funder requirements. We assert that this is likely because researchers generally have not been made aware of these requirements and of the benefits of data sharing.…

Graph Signal Processing (GSP) plays a crucial role in addressing the growing need for information processing across networks, especially in tasks like supervised classification. However, the success of GSP in such tasks hinges on accurately identifying the underlying relational structures, which are often not readily available and must be inferred…

"Self-regulated learning" (SRL) significantly impacts the process and outcome of "programming problem-solving." Studies on SRL behavioural patterns of programming students based on trace data are limited in number and lack of coverage. In this study, hence, the Hidden Markov Model (HMM) was employed to probabilistically mine…

The standards of educational information exchange are still firmly rooted in a Newtonian paradigm that emphasizes strict rules of information exchange. With the explosion of information since World War II, and especially its accessibility through the mechanism of the internet, this paradigm has become a barrier to effective exchanges of…

Multimedia information fusion technology is currently a popular technology for data processing. The advanced productivity it brings is dozens of times that of traditional data information processing. The collected and processed information is not only efficient and accurate, but also has strong scalability. With the continuous development of…

The aim of this investigation is to explore the preservice teachers' reasoning about variation (variability or spread) when they analyze data in situations that involve risk. In particular, in this communication the responses to two problems of a questionnaire administered to 96 preservice teachers are reported. The problems are of comparing…

The research urgency is caused by necessity to develop the informational support for management of development of higher education in conditions of high turbulence of external and internal environment. The purpose of the paper is the development of methodology for structuring and analyzing datasets of educational activities in order to reduce…

This chapter describes the background and process for collecting postgraduation outcomes data at a 4-year not-for-profit private college. The strategies, analyses, and reporting of this data-collection effort are highlighted with the use of a case study.

Secondary analyses of international large-scale assessments (ILSA) commonly characterize relationships between variables of interest using correlations. However, the accuracy of correlation estimates is impaired by artefacts such as measurement error and clustering. Despite advancements in methodology, conventional correlation estimates or…

Background: Deep versus surface knowledge is widely discussed by educational practitioners. A corresponding construct, levels of processing, has received extensive theoretical and empirical attention in learning science and psychology. In both arenas, lower levels of information and shallower levels of processing are predicted and generally…

In a wide range of fields, professional practice is being transformed by the increasing influence of digital analytics: the massive volumes of big data, and software algorithms that are collecting, comparing and calculating that data to make predictions and even decisions. Researchers in a number of social sciences have been calling attention to…

Recently organizations have begun to realize the potential value in the huge amounts of raw, constantly fluctuating data sets that they generate and, with the help of advances in storage and processing technologies, collect. This leads to the phenomenon of big data. This data may be stored in structured format in relational database systems, but…

Education, Big Data, and student privacy are a combustible mix. The improvement of education and the protection of student privacy are key societal values. Big Data and Learning Analytics offer the promise of unlocking insights to improving education through large-scale empirical analysis of data generated from student information and student…

Learning analytics (LA) are summaries, visualizations, and analyses of student data that could improve learning in multiple ways, for example by supporting teachers. However, not much research is available yet concerning how LA may support teachers to diagnose student progress and to intervene during student learning activities. There is evidence…