Policy researchers have difficulty understanding stratification in enrollment in US higher education when race and ethnicity data are plagued by missing values. Students who decline to ethnoracially self-identify become part of a "race unknown" reporting category. In undergraduate enrollment, "race unknown" students are not…

Computer-Assisted Qualitative Data Analysis (CAQDAS) is becoming increasingly prevalent in the social and health sciences and an expected skill for many researchers. CAQDAS is much less structured than the use of quantitative analysis software. However, actual CAQDAS practices and challenges have been insufficiently studied, especially among…

Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education…

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students' retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students' failure,…

The Student Evaluation of Teaching (SET) instrument provides insight for instructors and administrators alike, often touting high response-rates to endorse their validity and reliability. However, response-rate alone omits consideration for "adequate quantity of 'observational sampling opportunity' (OSO) data points" (e.g., high student…

In this article, we explore the use of Facebook targeted advertisements for the collection of survey data. We illustrate the potential of survey sampling and recruitment on Facebook through the example of building a large employee--employer linked data set as part of The Shift Project. We describe the workflow process of targeting, creating, and…

Missing data are exceedingly common across a variety of disciplines, such as educational, social, and behavioral science areas. Missing not at random (MNAR) mechanism where missingness is related to unobserved data is widespread in real data and has detrimental consequence. However, the existing MNAR-based methods have potential problems such as…

It is timely to take stock of the wellbeing of educational research in Aotearoa New Zealand. The major Education Work Programme 2021 includes a Research, Evaluation and Development Strategy. Te Ara Paerangi--Future Pathways Green Paper has launched a major review of the design of public funding for research institutions and for research taking…

Purpose: The general goal of this paper is to help educators understand the importance of MOOC training to school teachers and their hypothetical value for predicting the use of teaching strategies in the face-to face-classroom teaching. With this purpose, the study is guided by two research questions: (1) Are there different patterns of…

Purpose: Overall quantitative research aims to observe certain fundamental principles of logic and scientific frame of reasoning. There continues to be challenges on how quantitative research is conducted in the field of information systems. Design/methodology/approach: Structured equation modelling (SEM) research identifies concerns about the…

The goal of the university professional development course BINE is to encourage participants to address education for sustainable development (ESD) in a reflective way. The main aims are for the participants to learn about action research and to reflect and implement ESD in their work and institutions. This paper briefly describes the concepts of…

Student dropout is a significant concern for university administrators, students and other stakeholders. Dropout is recognised as highly complex due to its multi-causality, which is expressed in the existing relationship in its explanatory variables associated with students, their socio-economic and academic conditions, and the characteristics of…

The higher education efficiency evaluation model using the data envelopment analysis method has interested many researchers. This paper uses bibliometric analysis on publications extracted from the Scopus database to provide a comprehensive overview of research publications on the measurement of higher education efficiency based on data…

Data-driven decision making (DDDM) involves using information gathered from and about students to make ongoing decisions about their instruction. Many teachers struggle with implementation of DDDM practices to optimize instruction, underscoring the importance of teacher education vis-à-vis DDDM. The present study secondarily analyzed existing data…

It is unclear whether the data visualization skills possessed by data-centric practitioners reflect recent advances in computing systems and visual design principles, and their role in generating relevant and insightful output to support decision-making. In this study, the author presents a curriculum designed for teaching basic and advanced…