Multilingual learners (MLL) are a large and growing group of disadvantaged students in the United States public education system who come from refugee and immigrant backgrounds and require linguistic instruction, in addition to content instruction. MLLs are entitled to language instruction, and school districts receive additional funding for these…

Globally, physical education curricula are progressively integrating intelligent physical education systems, a breakthrough in physical technology. These systems utilise advanced data analytic and sensing technologies, significantly enhancing the interactivity and personalisation of physical activity, thus improving students' athletic performance…

We describe a semester-long project for an introductory statistics class that studies the broken windows theory of policing and the related issues of race, policing, and criminal justice. The most impactful feature of the project is the data-collection phase, in which students attend and observe a public arraignment court session. This "Court…

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…

From its earliest days, practitioners of computer-assisted language learning (CALL) have collected data from computer-mediated learning environments. Indeed, that has been a central aspect of the field from the beginning. Usage logs provided valuable insights into how systems were used and how effective they were for language learning. That…

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model…

Data collected from online learning and tutoring systems for individual students showed strong autocorrelation or dependence because of content connection, knowledge-based dependency, or persistence of learning behavior. When the response data show little dependence or negative autocorrelations for individual students, it is suspected that…

Supplemental Instruction (SI) is a voluntary, non-remedial, peer-facilitated, course-specific intervention that has been widely demonstrated to increase student success, yet concerns persist regarding the biasing effects of disproportionate participation by already higher-performing students. With a focus on maintaining access for all students, a…

We describe a method for analyzing student data from online programming exercises. Our approach uses contingency tables that combine whether or not a student answered an online exercise correctly with the number of attempts that the student made on that exercise. We use this method to explore the relationship between student performance on online…

A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

Analysis of student-level data to inform policy and promote student success is a core function of executive higher education agencies. Postsecondary data systems have expanded their collection of data elements for use by policymakers, institutional staff and the general public. State coordinating and governing boards use these data systems for…

The Integrated Postsecondary Education Data System (IPEDS) collects institution-level data from postsecondary institutions in the United States (50 states and the District of Columbia) and other U.S. jurisdictions (see appendix A for a list of other U.S. jurisdictions). This "First Look" presents findings from the provisional data for…

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…