Australian early childhood education still labours with the achievement of universal access and the production of comprehensive and consistent data to underpin a national evidence base. In this article, we attend to the processes led by numbers whereby new practices of quantification, rationalization and reporting are introduced and mastered in a…

Various types of structured data collected by learning management systems such as Moodle have been used to improve student learning outcomes. Learning analytics refers to an assortment of data analysis methods used for this task. These methods typically do not consider unstructured data such as blogs, discussions, e-mail, and course messages.…

"Education data science" is an emerging methodological field which possesses the algorithm-driven technologies required to generate insights and knowledge from educational big data. This article consists of an analysis of the Lytics Lab, Stanford University's laboratory for research and development in learning analytics, and the Center…

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…

Career Technology Education (CTE) program evaluations have been mostly completed for compliance and monitoring purposes. Hence, they have limited use in establishing program effectiveness or for program improvement. Moreover, engaging in program evaluations can be time consuming and costly, especially for programs that have not been evaluated in…

Although millions of students have access to varieties of learning resources on Massive Open Online Courses (MOOCs), they are usually limited to receiving rapid feedback. Providing guidance for students, which enhances the interaction with students, is a promising way to improve learning experience. In this paper, we consider to show students the…

State tests lack the sensitivity and frequency to reflect ongoing academic improvement of students who are well below grade-level proficiency standards. As a result, some states are using screening and progress-monitoring data, collected as part of a multi-tiered system of supports (MTSS) model, to evaluate early student-level progress toward SiMR…

This publication provides a summary of data relating to students, programs, subjects and training providers in Australia's government-funded vocational education and training (VET) system (defined as all Commonwealth and state/territory government-funded training delivered by technical and further education [TAFE] institutes, other government…

In recent years and especially over the past 12 months, with the COVID-19 pandemic and the unrest after the death of George Floyd, community colleges have been making efforts to view and rethink their missions through an equity lens. These efforts have included turning this prism toward institutional operations like student services, human…

Q methodology is a method to systematically study subjective matters such as thoughts and beliefs on any given topic. Q methodology can be used for both theory building and theory testing. The purpose of this paper was to give a brief overview of Q methodology to readers with various backgrounds. This paper discussed several advantages of Q…

In this paper, we describe a framework for data analysis that can be embedded into a base application. Since it is important to analyze the data directly inside the application where the data is entered, a tool that allows the scientists to easily work with their data, supports and motivates the execution of further analysis of their data, which…

Classrooms are complex environments. Ethnography can help researchers understand these complexities as they gather information by watching and talking with people, reading available reports and records and mapping out the environment. This case adds to existing literature on ethnography as we share our own experiences of what we refer to as…

In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of…

Incidental findings in research with human participants may have implications for a person's present health or future health outcomes. Current guidelines focus on methods for handling and reporting incidental findings from biological test data but incidental findings might also arise from non-biological tests. This article presents three examples…

With the spread of the COVID-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students' performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must…