Across the international higher education sector, existing studies highlight that student evaluations of courses and teaching are biased and prejudiced towards academics and can cause mental distress. Yet student evaluation data is often used as part of faculty hiring, firing, promotion, award and grant decisions. That a data source known to be…

Pragmatically, machine learning techniques can improve educators' capacity to monitor students' learning progress when applied to quality data. For developing countries, the major obstacle has been the unavailability of quality data that fits the purpose. This is partly because the in-use information systems are either not properly managed or not…

Student privacy is a critical area of higher education that deserves greater focus, particularly as student data digitalization increases. Many colleges and universities use data literacy as a way to prepare students, sometimes from different disciplines, to work with others' data postgraduation. Data literacy can be an avenue for helping all…

Continuous improvement methods, widely used in fields such as medicine, can help educators identify and try out adjustments in practice, known as "change ideas," which contribute to better outcomes and more equitable systems for students. Since 2018, the Bill & Melinda Gates Foundation has invested in 24 organizations across the…

For the first time in 32 years, the national average scale score in mathematics dropped by seven points across the country in grades four and eight, according to the latest results of the National Assessment on Educational Progress (NAEP). The 2022 results in mathematics were staggering, in which "math performance fell for the first time in…

Accelerating advancements in learning analytics and artificial intelligence (AI) offers unprecedented opportunities for improving educational experiences. Without including students' perspectives, however, there is a potential for these advancements to inadvertently marginalize or harm the very individuals these technologies aim to support. This…

The state of California has the largest community college system in the United States with 1.8 million students from diverse backgrounds and 64% percent of students who are economically disadvantaged (California Community Colleges Chancellor's Office, 2022; Felix & Castro, 2018). California community colleges also experience inequitable…

U.S. middle school teachers have struggled to meet the instructional needs of students in the area of mathematics. Teachers' approaches to assessment and instructional strategies remain understudied; such knowledge could help educational leaders to devise strategies to boost student achievement. The purpose of this qualitative study was to examine…

What metrics matter the most to advancement professionals? Are the most strategic data being regularly collected, or are there new metrics that should be considered? To answer these questions, the Council for Advancement and Support of Education (CASE) and GiveCampus partnered on a pulse survey on this topic for advancement professionals at…

This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes…

Wichita State University (WSU) began the SEM process in 2015 with a steering committee co-chaired by the university registrar and the chief data officer (CDO). Work leading up to and undertaken throughout that process and the implementation of the SEM plan it produced helped to form an extraordinary partnership between these two professionals.…

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student…

Hiring academic staff into departments and supporting them remains the single costliest activity that most institutions of higher learning engage in and requires careful, long-term, data-driven planning. This study identifies widely available (but seldom actually used) variables needed for this process: available instructional workload units and…

While teachers' core responsibility is to provide high-quality instruction, they are also expected to engage in data-based decision-making (DBDM), e.g., to analyse and use data to improve instruction. We developed a relevance intervention to promote student teachers' self-determined motivation and application intentions for DBDM and implemented it…

Educational data mining is used to discover significant phenomena and resolve educational issues occurring in the context of teaching and learning. This study provides a systematic literature review of educational data mining in mathematics and science education. A total of 64 articles were reviewed in terms of the research topics and data mining…