Measures of student disadvantage--or risk--are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new…

The increasing use of learning management systems (LMSs) generates vast amounts of clickstream data, opening new avenues for predicting learner performance. Traditionally, LMS predictive analytics have relied on either supervised machine learning or Markov models to classify learners based on predicted learning outcomes. Machine learning excels at…

Background: School absenteeism and health have a close bidirectional link: children with medical conditions miss more school, and chronic absenteeism is tied to poor health outcomes. Despite rising absenteeism, tools to assess school attendance in healthcare settings remain underexplored. Brief, standardized attendance screening may help address…

The implementation of prereferral models was being discussed in educational literature as early as 1979. Over the past decade, schools in the United States have begun to adopt prereferral models to meet multiple goals, such as reducing inappropriate referrals to special education, supporting students who face academic and behavioral challenges,…

Although using machine learning for predicting which students are at risk of failing a course is indeed valuable, how can we identify which characteristics of individual students contribute to their being At-Risk? By characterising individual At-Risk students we could potentially advise on specific interventions or ways to reduce their probability…

Early warning systems (EWS) using analytical tools that have been trained against prior years' data, can reliably predict dropout risk in individual students so that educators may intervene early to help avert this from happening. Risk profiles for dropouts aren't always useful since students often do not conform to the profiles. Researchers with…

Danish schools offering "preparatory basic education and training" (FGU schools) have students that are characterized by having different academic, social, or personal problems. In addition, many FGU students are at high risk of suicidal behavior. Many young people with suicide behavior do not seek help and early identification is…

Virtual Reality (VR) has been found useful to improve engagement and retention level of students, for some topics, compared to traditional learning tools such as books, and videos. However, a student could still get distracted and disengaged due to a variety of factors including stress, mind-wandering, unwanted noise, external alerts, and internal…

Colleges are increasingly turning to predictive analytics to identify "at-risk" students in order to target additional supports. While recent research demonstrates that the types of prediction models in use are reasonably accurate at identifying students who will eventually succeed or not, there are several other considerations for the…

Introduction: Student retention is a critical issue in higher education. Universities have responded by implementing supports like early alert systems. Objective: We investigated students' knowledge of and experiences with an early alert system designed to enhance academic persistence. Method: We surveyed (N = 356) undergraduates at a large public…

Background: Formative assessments build a positive learning environment and provide feedback to enhance learning. This study examined the impact of online formative and low-stake summative assessments on medical students' learning outcomes in the Clinically Oriented Anatomy course from 2016 to 2020. We aimed to demonstrate that formative…

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

Teaching Institutions could benefit from Early Warning Systems to identify at-risk students before learning difficulties affect the quality of their acquired knowledge. An Early Warning System can help preemptively identify learners at risk of dropping out by monitoring them and analyzing their traces to promptly react to them so they can continue…

Recent data from statewide assessments, scores on the National Assessment of Educational Progress (NAEP), and college remediation needs show that an increasing number of K-12 students are not performing at grade level. As schools look to support these students' learning, some districts are turning to a proven strategy for identifying the students…

Background: Much confusion exists about the underachievement of gifted students due to significant variations in how the phenomenon has been identified. From a review of the literature, five methods were found to be commonly used to identify gifted underachievement. Aims: The purpose of the study was to assess the equivalence of the commonly used…