Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one's imputation model must be congenial to (appropriate for) one's intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner…

Dual enrollment is a complex policy issue and is increasingly of interest to state policymakers. Adequate data and data systems are critical to informing dual enrollment policy, yet the data at the federal level and many state data systems suffer from common challenges and lack the capacity to address dual enrollment policy. We review the…

Most literature related to Benford's Law discusses what the law can be used for and how it works but fails to address effective methods and procedures for teaching the law to students. This article examines existing information resources to determine the most effective methods and procedures used to explain this Law to those who have no experience…

The challenge in data analysis often lies in accounting for the multidimensionality and complexity of the data while simultaneously discovering patterns. Integrating and consolidating different types of data during analysis can broaden the perspective and permit obtaining complementary views. This methodological research study on data collection…

This editorial introduces a special section of the "Journal of Learning Analytics," for which Neil Selwyn's keynote address to LAK '18 has been written up as an article, "What's the problem with learning analytics?" His claims and arguments are engaged in commentaries from Alfred Essa, Rebecca Ferguson, Paul Prinsloo, and…

Analysis of keystroke logging data is of increasing interest, as evident from a substantial amount of recent research on the topic. Some of the research on keystroke logging data has focused on the prediction of essay scores from keystroke logging features, but linear regression is the only prediction method that has been used in this research.…

Activity Theory (AT) is increasingly employed as a lens to guide data analysis in information systems (IS) studies. The theory is also applied to assess and evaluate information systems and technologies (IS/IT) in organisations. Even though its popularity continues to increase in both business and academic domains, there is no formal or assessment…

Scores estimated from multidimensional item response theory (IRT) models are not necessarily comparable across dimensions. In this article, the concept of aligned dimensions is formalized in the context of Rasch models, and two methods are described--delta dimensional alignment (DDA) and logistic regression alignment (LRA)--to transform estimated…

Mathematics classrooms are increasingly becoming sites for investigating social (in)justice, but research on teaching mathematics for social justice remains limited to individual case studies. This article reports on a metasynthesis of 35 qualitative reports of social justice mathematics enactments in diverse classroom contexts. Critical race…

With Governor Newsom's signature on the state budget in June 2019, California is finally on the path to joining the majority of other states in the country who have robust data systems. Having this administration provide funding to begin to build and maintain a statewide longitudinal data system (SLDS) is an important win. Advocates have been…

This issue of Research Library Issues looks at the critical role and participation of libraries and librarians in supporting the data science revolution. The articles contained in this issue are: (1) Data Literacy as a Pathway to Data Science at Georgia Tech (Catherine Murray-Rust, with Ameet Doshi, Jay Forrest, Ximin Mi, and Alison Valk); (2) New…

This popular text, now in its fifth edition, provides step-by-step guidance for new and experienced researchers who want to use interviewing as a research method. This user-friendly guide explains the rationale for interviewing and the complexity of selecting interview participants, important interviewing techniques, and how to work with the…

One of the essential problems, in educational data mining, is to predict students' performance on future learning materials, such as problems, assignments, and quizzes. Pioneer algorithms for predicting student performance mostly rely on two sources of information: students' past performance, and learning materials' domain knowledge model. The…

Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate problems through analysis of massive, non-standard,…

As a requirement of our era, digital competence and e-learning have become basic competencies. The importance of these concepts has become clearer in medical education, especially with the COVID-19 pandemic. This period also triggered the transformation in education with technology. This study aimed to examine the relationship between medical…