This study helps improve the early prediction of student performance by RNN-FitNets, which applies knowledge distillation (KD) to the time series direction of the recurrent neural network (RNN) model. The RNN-FitNets replaces the teacher model in KD with "an RNN model with a long-term time-series in which the features during the entire course…

Unaddressed social, emotional, and behavioral (SEB) needs and academic challenges may lead to negative youth outcomes. Universal behavioral risk screeners, like the student self-report Social, Academic, and Emotional Behavior Risk Screener (SAEBRS-SRS), identify at-risk students. To improve screening tool use, research is needed to identify the…

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…

Predicting students' success is crucial in educational settings to improve academic performance and prevent dropouts. This study aimed to improve student performance prediction by combining advanced machine learning (ML) approaches. Convolutional Neural Networks (CNNs) and attention mechanisms were used for extracting relevant features from…

Even though individuals now have increased access to higher education, first-generation college students (FGCS) remain disadvantaged. Without prior college exposure, experience, or a college-going family tradition, FGCS face challenges navigating higher education institutions which can lead to decreased college retention and completion rates.…

Technological advances have significantly affected education, leading to the creation of online learning platforms such as virtual learning environments and massive open online courses. While these platforms offer a variety of features, none of them incorporates a module that accurately predicts students' academic performance and commitment.…

Historically, students' lack of academic persistence has been attributed to factors such as financial instability, a difficult homelife, trauma, and social marginalization. The purpose of this study was to examine the perceptions of students labeled as high-risk who were able to successfully persist towards their academic goals and explore how…

Student retention in higher education is a primary indicator of institutional reputation, impacting factors such as national rankings, admissions selectivity, and alumni support (Lee, Sanford, and Lee 2014). Programs designed to support student success in the first year and beyond generally intersect with the institutional mission. Utilizing…

Although the traditional 16-week schedule is widely used in higher education, time-compressed courses are increasingly being recognized as an approach to improve student success rates and expedite credential completion, thereby facilitating entry into the workforce. However, there is limited research to establish that the reported advantages of…

This study evaluated the comparative effects of presession and interspersed attention on the academic achievement of an at-risk student in an inclusive fourth-grade classroom. Data indicated an increase in academic achievement during both presession and interspersed attention conditions. Additionally, data on disruptive behavior displayed as an…

Predicting student performance in an academic institution is important for detecting at-risk students and administering early-intervention strategies. We propose a new grade prediction model that considers three factors: temporal dynamics of prior courses across previous semesters, short-term performance consistency, and relative performance…

Early identification of at-risk students for timely intervention is critical to prevent non-completion of study programs. This article proposes a flipped class framework to support the academic success of at-risk students in an undergraduate Calculus course. It comprises three main components of setting, conduct, and monitoring. A flipped support…

Understanding student engagement with the institution from the first day of classes to the end of the semester would help inform the institution of the potential risk that a student will drop out of a class or of the school. Learning Management Systems (LMS) record student interactions with the system and might be able to be used to identify…

The purpose of this study was to examine the ongoing issue of teachers with chronic absenteeism particularly as it relates to vulnerable student populations. For the purposes of this research, chronic absenteeism is defined as missing 10 or more instructional days. The problem is the effect that chronic teacher absenteeism has on vulnerable…