Big Data can radically transform education by enabling personalized learning, deep student modeling, and true longitudinal studies that compare changes across classrooms, regions, and years. With these promises, however, come risks to individual privacy and educational validity, along with deep policy and ethical issues. Education is largely a…

Many researchers who are interested in studying students' online self-regulated learning (SRL) have heavily relied on self-reported surveys. Data mining is an alternative technique that can be used to discover students' SRL patterns from large data logs saved on a course management system. The purpose of this study was to identify students' online…

Data use is becoming a prominent strategy for educational innovation and improvement across countries. However, the fragmentation of data collection often hinders the capacity of policymakers, researchers and practitioners to access and analyse the wealth of data routinely generated in educational institutions. A critical step towards realising…

With digitisation and the rise of e-learning have come a range of computational tools and approaches that have allowed educators to better support the learners' experience in schools, colleges and universities. The move away from traditional paper-based course materials, registration, admissions and support services to the mobile, always-on and…

Open book assessment is not a new idea, but it does not seem to have gained ground in higher education. In particular, not much literature is available on open book examinations in mathematics and statistics in higher education. The objective of this paper is to investigate the appropriateness of open book assessments in a first-year business…

We investigate whether commonly used value-added estimation strategies produce accurate estimates of teacher effects under a variety of scenarios. We estimate teacher effects in simulated student achievement data sets that mimic plausible types of student grouping and teacher assignment scenarios. We find that no one method accurately captures…

Researchers use many different metrics for evaluation of performance of student models. The aim of this paper is to provide an overview of commonly used metrics, to discuss properties, advantages, and disadvantages of different metrics, to summarize current practice in educational data mining, and to provide guidance for evaluation of student…

Charlotte-Mecklenburg Schools' (CMS) Parent University is an innovative, collaborative initiative designed to engage parents in their children's education. Working with community partners, Parent University offers unique courses and workshops such as Parenting Awareness, Helping Your Child Learn in the 21st Century, Health and Wellness, and…

This study was conducted to systematically track and benchmark upper primary school students' ESL reading comprehension ability and subsequently generate data at the micro and macro levels according to individual achievement, school location, gender and ethnicity at the school, district, state and national levels. The main intention of this…

Learning analytics and digital badges are emerging research fields in educational science. They both show promise for enhancing student retention in higher education, where withdrawals prior to degree completion remain at about 30% in Organisation for Economic Cooperation and Development member countries. This integrative review provides an…

This study applies a diagnostic errors framework to identify and classify mistakes that were made in a psychoeducational assessment of a bilingual student who was misidentified as a person with autism. Findings of diagnostic errors were categorized under four domains--faulty knowledge, faulty data gathering, faulty data processing, and faulty…

This article reports on findings based on analyses of a unique dataset collected by ACT that includes information on student achievement in a variety of subjects at the high-school level. The authors examine the relationship between teacher effect estimates derived from value-added model (VAM) specifications employing different student learning…

How many students repeat a grade each year? How do retention rates vary across states and over time? Despite extensive research on the predictors and consequences of grade retention, there is no systematic way to quantify state-level retention rates; even national estimates rely on imperfect proxy measures. We present a conceptually simple…

In order to assess how completely a Small Learning Community (SLC) and/or Career Academy is implemented in any given site, and to connect the degree of implementation with the amount of improvement in student performance, two kinds of information are needed. The first is information on the quality of implementation, which can be collected by…

As standards-based instruction and the focus on accountability have grown over the last decade, technology's role has grown in importance. Research surrounding high-performing schools shows that there are four important uses of technology in these schools: (1) A student information system to store and manage data connected to students; (2) An…