Predicting learner performance with precision is critical within educational systems, offering a basis for tailored interventions and instruction. The advent of big data analytics presents an opportunity to employ Machine Learning (ML) techniques to this end. Real-world data availability is often hampered by privacy concerns, prompting a shift…

Laypeople's estimates of carbon footprints have repeatedly shown to be deficient, which may hinder targeted behavior change to reduce CO2 emissions. In an online study (N = 127), a vast underestimation of carbon footprints for 60 food items was observed in an on average highly educated convenience sample, confirming a lack of carbon footprints…

Course dropout is a concern in online higher education, mainly in first-year courses when different factors negatively influence the learners' engagement leading to an unsuccessful outcome or even dropping out from the university. The early identification of such potential at-risk learners is the key to intervening and trying to help them before…

Inefficient targeting of students at risk of dropping out might explain why dropout-reducing efforts often have no or mixed effects. In this study, we present a new method which uses a series of machine learning algorithms to efficiently identify students at risk and makes the sensitivity/precision trade-off inherent in targeting students for…

Nowadays, Massive Open Online Courses (MOOC) has been gradually accepted by the public as a new type of education and teaching method. However, due to the lack of timely intervention and guidance from educators, learners' performance is not as effective as it could be. To address this problem, predicting MOOC learners' performance and providing…

Accurate knowledge monitoring is critical to the learning process, as it allows one to regulate studying and test preparation. Thus, a number of investigations have attempted to improve metacognition in the classroom, with the ultimate goal of improving student exam performance. However, such interventions have had inconsistent success using…

Language disorder is characterized by difficulty with the comprehension and production of different aspects of language. School-aged children with developmental language disorder (DLD) and school-aged children with Down syndrome (DS) demonstrate similar deficits in the area of morphosyntax, which often creates barriers during social interactions…

Predicting students' performance to identify which students are at risk of receiving a D/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among…

Metamotivational knowledge is a burgeoning area of study. It refers to people's knowledge about motivation, and it has been shown to contribute to motivation and behavioral outcomes. The current study bridges metamotivational knowledge with self-determination theory (SDT), one of the most prominent theories of academic motivation. SDT proposes…

With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…

Evidence-based policy at the local level requires predicting the impact of an intervention to inform whether it should be adopted. Increasingly, local policymakers have access to published research evaluating the effectiveness of policy interventions from national research clearinghouses that review and disseminate evidence from program…

In some computerized educational systems, there is evidence of students "wheel-spinning," where a student tries and repeatedly fails at an educational task for learning a skill. This may be particularly concerning in low resource settings. Prior research has focused on predicting and modeling wheel-spinning, but there has been little…

Student success and drop-out predictions have gained increased attention in recent years, connected to the hope that by identifying struggling students, it is possible to intervene and provide early help and design programs based on patterns discovered by the models. Though by now many models exist achieving remarkable accuracy-values, models…

Background/Context: Chronic absenteeism is a serious problem that has been linked to lower academic achievement, diminished socioemotional skills, and an increased likelihood of high school dropout (Allensworth et al., 2021; Gottfried, 2014). As a result, many schools have begun to embrace early warning systems (EWS) as a tool to identify and flag…

The accuracy of decision rules for progress monitoring data is influenced by multiple factors. This study examined the accuracy of decision rule recommendations with over 4,500 second-and third-grade students receiving a tier II reading intervention program. The sensitivity and specificity of three decision rule recommendations for predicting…