This manuscript describes the use of real data applied to a fictional real-estate firm for teaching data visualization to university students. In the case study, students employ data analytic techniques in Tableau to clean, organize, and analyze real estate data. By creating visualizations, students address several questions about how selling…

The Standards Assessment Inventory (SAI) has provided relevant, educator-level data helping systems of all kinds -- states, districts, schools, provinces, and organizations -- gather and track data about the professional learning their educators experience. An online, confidential, valid, and reliable instrument administered to school-based…

Evaluation is a critical component of continuous improvement in education. Robust evaluations enable engaged parties to determine program and intervention impact on key outcomes, identify areas for improvement, and guide future actions. Additionally, as educational systems increasingly focus on data-driven decisionmaking, evaluation becomes even…

Despite the increasing interest in data science education in the world, its teaching is not included in the curricula (junior secondary) and there is little information about it. Google Trends is discussed as a tool and database in school data science. Also, in different subjects, students were able to create and interpret graphs using this tool.…

Research has shown that critical data literacies development for educators is seldom a core component of most campus conversations about datafication, even as extractive, datafied systems become pervasive throughout the higher education sector. This article outlines findings from an international, qualitative, Comparative Case Study (CCS) of…

Internet users are becoming ignorant with their data and the transparency of information due to the nature of high-speed internet today. Regrettably, internet users are deceived by engineering tactics performed by highly trained people, namely cybercriminals. Thus, in order to combat phishing attacks, internet users should be educated on security…

Underlying thematic analysis are a few fundamental human cognitive processes, such as categorizing, prototyping and metaphorical mapping. By unpacking these basic processes of human cognition, this paper hopes to provide a cognitive basis for thematic analysis as a foundational method in data analysis for qualitative research. In particular, it…

Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and…

Social sciences expose many cognitively complex, highly qualified, or fuzzy problems, whose resolution relies primarily on expert judgement rather than automated systems. One of such instances that we study in this work is a reflection analysis in the writings of student teachers. We share a hands-on experience on how these challenges can be…

School attendance and absenteeism are critical targets of educational policies and practices that often depend heavily on aggregated attendance/absenteeism data. School attendance/absenteeism data in aggregated form, in addition to having suspect quality and utility, minimizes individual student variation, distorts detailed and multilevel…

There is no single agreed-upon definition of data literacy because expectations of what it means to be data literate varies across contexts. The lack of agreement on a definition of data literacy across contexts is therefore necessary. However, definitions are important. Definitions embody our understanding of concepts and are the foundation for…

Despite increasing calls to build equitable data infrastructures, the education field has yet to have a shared guideline around equitable education data management and stewardship. To address this gap, we propose one framework from the data governance literature: the FAIR (Findable, Accessible, Interoperable, Reusable) data management principles…

This article discusses the key elements of the Data Science Technology course offered to postgraduate students enrolled in the Master of Data Science program. This course complements the existing curriculum by providing the skills to handle the Big Data platform and tools, in addition to data science activities. We tackle the discussion about this…

The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…

Practical work involving laboratory experiments is agreed upon to be an essential component of secondary science education. Practical work encompasses a broad range of activity types. The different forms of practical work are not equal in terms of cognitive demand and learning benefit. Inquiry-based investigations provide experience of cognitive…