The COVID-19 pandemic constrained higher education institutions to switch to online teaching, which led to major changes in students' learning behavior, affecting their overall performance. Thus, students' academic performance needs to be meticulously monitored to help institutions identify students at risk of academic failure, preventing them…

Purpose: Professional development interventions (PDIs) are crucial for equipping teachers to use data effectively. Relying on previous studies reporting on such interventions, this paper aims to identify and synthesise the goals, dimensions and conditions of PDIs for data use. This paper also examines the evidence of the effect of such…

Biochemistry is a data-heavy discipline, yet teaching students to work with large datasets is absent from many undergraduate Biochemistry programs. Ensuring that future generations of students are confident in tackling problems using big data first requires that educators become comfortable teaching big data skills. The activity described herein…

Bounded continuous data are encountered in many applications of item response theory, including the measurement of mood, personality, and response times and in the analyses of summed item scores. Although different item response theory models exist to analyze such bounded continuous data, most models assume the data to be in an open interval and…

Research in the UK is increasingly regulated by ethics review committees (RECs) which require researchers to seek ethics approval before commencing research. These RECs routinely expect researchers to anonymise data as part of standard ethical research practice. However, the anonymisation of data may sit in tension with participatory approaches to…

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the…

This paper showcases how a reflexive practice, that includes arts-based activities, deepened understandings experienced by a doctoral student of psychology while completing the data analysis section of a metasynthesis. The metasynthesis focused on qualitative studies, examining the mental and spiritual care of persons living with diabetic foot…

This article presents a project that uses a business context to introduce students to a multiphase approach to analytics while relying primarily on an introductory Excel course for prerequisite knowledge. A range of analytics techniques--including Excel Pivot tables and charts, regression trend lines, and linear programming--are combined into an…

The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four…

Educational institutions are creating a considerable amount of data regarding students, faculty and related organs. This data is an essential asset for academic institutions as it has valuable insights, knowledge and intelligence for the policymakers. Students are the fundamental entities and primary source of data creation in any educational…

Due to the unprecedented growth in available data collected by e-learning platforms, including platforms used by massive open online course (MOOC) providers, important opportunities arise to structurally use these data for decision making and improvement of the educational offering. Student retention is a strategic task that can be supported by…

"Beyond Good Intentions" provides a guide for states to craft equity-driven policies that are specific, actionable, measurable and well funded. It outlines four major steps states should take when considering and creating new policy: (1) Examine the Data to Set the Goal for the Policy: Specific types of data to consider when identifying…

Digitally-based assessments create opportunities for collecting moment to moment information about how students are responding to assessment items. This information, called log or process data, has long been regarded as a vast and valuable source of data about student performance. Despite repeated assurances of its vastness and value, process data…

With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…

Higher education institutions in the United States face an urgent need to use evidence of their students' outcomes to inform improvements and increase educational attainment. Data about student outcomes are abundant and professionals who interpret and report the data are commonplace today. Yet, the higher education landscape continues to be…