Developing effective assessments is a critical component of quality instruction. Assessments are effective when they are well-aligned with the learning outcomes, can confirm that all intended learning outcomes are attained, and their obtained grades are accurately reflecting the level of student achievement. Developing effective assessments is not…

Like in many Global South countries, the vocational education and training system in Kazakhstan has some weaknesses, including low-competent educators poorly applying digital technologies in their instructional repertoire, which highlights the need for motivating teachers towards incorporating technologies representing students' everyday life in…

Revisiting the seminal 2010 Nolan and Temple Lang article on the role of computing in the statistics curricula, we discuss several trends that have emerged over the last ten years. The rise of data science has coincided with a broadening audience for learning statistics and using computational packages and tools. It has also increased the need for…

This explanatory case study investigated why educators (teachers, administrators and support teachers) in a northeast charter school found it difficult to implement a data-driven decision-making (D3M) process. To comprehend where the breakdown in the process occurred, the researcher examined the cultural, technological and political barriers that…

Credit recovery in public education has become an intervention used widely in the United States to address the rising number of students who are not meeting with success in the completion of required courses in the traditional education setting. With the passing of the Every Student Succeeds Act in 2015, public schools sought a method to motivate…

A prominent goal of colleges and universities today is to enact data-driven teaching and learning. Faculty clearly play a key role, and yet they tend to have limited time, a lack of training in assessment or education research, and few incentives for engaging in this work. We describe a framework designed to address the practical and cultural…

The growing use of machine learning for the data-driven study of social issues and the implementation of data-driven decision processes has required researchers to re-examine the often implicit assumption that datadriven models are neutral and free of biases. The careful examination of machine-learned models has identified examples of how existing…

A driving factor in designing interactive museum exhibits to support simultaneous users is that visitors learn from one another via observation and conversation. Researchers typically analyze such collaborative interactions among museumgoers through manual coding of live- or video-recorded exhibit use. We sought to determine how log data from an…

According to a continuously increasing amount of information in all aspects whether the sources are retrieved from an internal or external organization, a platform should be provided for the automation of whole processes in the collection, storage, and processing of Big Data. The tool for creating Big Data is a Big Data challenge. Furthermore, the…

Members of the Data Science Program at George Washington University (GWU) designed and implemented a tuition-free two-week summer camp at GWU for high-school students from the Washington Metro Area. The United States Department of Agriculture (USDA) Office of the Chief Information Officer and his staff were our main partners in the project. The…

Peter Dunn and Margaret Marshman present the second of their data files articles in which they discuss the statistical investigation cycle which describes the whole process of conducting a statistical research study. [For "The Data Files: A Series of Articles to Support Mathematics Teachers to Teach Statistics," see EJ1259108.]

The use of open textbooks in universities is according to some organisations changing the higher education landscape and is promising for the mainstream adoption of OER. The aim of this paper is to analyse authors' views of agency and empowerment when they plan, create and reflect on their open textbooks, their teaching and students' learning.…

The Canvas Learning Management System (LMS) is used in thousands of universities across the United States and internationally, with a strong and growing presence in K-12 and higher education markets. Analyzing the development of the Canvas LMS, we examine 1) 'frictionless' data transitions that bridge K12, higher education, and workforce data 2)…

We perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e. data available at all universities without the need of specific collection. Therefore, we propose a methodical approach…

Large-scale assessment programs, such as the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS), employ item response theory (IRT) and marginal estimation methods to estimate student proficiency in specific subjects such as mathematics, science, or reading. Each of these…