To address the seven guiding questions posed for authors of articles in this special issue, we begin by discussing the development (in the late 1970s-early 1980s) of Eccles' expectancy-value theory of achievement choice (EEVT), a theory developed to explain the cultural phenomenon of why girls were less likely to participate in STEM courses and…

Student dropout is a major concern in studies investigating retention strategies in higher education. This study identifies which variables are important to predict student dropout, using academic data from 3583 first-year students on the Business Administration (BA) degree at the University of Barcelona (Spain). The results indicate that two…

Individuals with autism increasingly enroll in universities, but little is known about predictors for their success. This study developed predictive models for the academic success of autistic bachelor students (N = 101) in comparison to students with other health conditions (N = 2465) and students with no health conditions (N = 25,077). We…

In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often…

We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…

Having a job immediately after graduation is the dream of every IT graduate. However, not everyone can achieve this outcome. The study's primary goal is to develop predictive models to forecast IT graduates' chances of finding a job based on factors such as academic performance, socioeconomic status, academic habits, and demographic data.…

Florida State University researchers spent the last year collecting and analyzing data on corequisite developmental education (DE) models in Texas as part of a four-year study that received a $1.5M grant from the U.S. Department of Education's Institute of Education Sciences. This study was proposed in response to Texas House Bill (HB) 2223, which…

During COVID, HyFlex gained popularity and became a "new normal" that educators need to consider as an effective instructional approach. Previous research offers conflicting findings related to the impact of HyFlex instruction on students' basic psychological needs and academic performance. Our investigation provides insight into a…

How important are learning strategies or personal attributes for learning outside of domain-specific knowledge or twenty-first-century transversal skills when predicting academic success in higher education? To address this question, we conducted a longitudinal study among 1,681 students at one of the leading universities in Hungary. Students took…

Predicting students' learning outcomes is one of the main topics of interest in the area of Educational Data Mining and Learning Analytics. To this end, a plethora of machine learning methods has been successfully applied for solving a variety of predictive problems. However, it is of utmost importance for both educators and data scientists to…

Objective: The purpose of this research study is to examine the use of the Andersen Behavioral Model of Health Services Use in predicting how health impacts the academic performance of college students through predisposing, enabling, and need factors. Participants: Data were collected from 428 college students attending a large university in the…

The aim of the study is to identify variables that explain students' academic performance, determine their relative importance, and consequently, develop an index to distinguish advantaged and disadvantaged schools in pursuit of educational equality. By using this index, we intend to build a model for evaluating schools' overall performance based…

Higher education institutions face heightened accountability for student success. As such, higher education relies heavily on big data to predict student outcomes. This process is problematic because predictive models are developed on historical data, are deficit based, and are focused on student factors, neglecting institutional factors. The…

Teacher absenteeism has high individual and societal costs and triggers a vicious cycle by increasing teacher absenteeism for the remaining teachers. Moreover, teachers' non-attendance disrupts the learning process and affects student motivation and achievement. Teacher absenteeism further exacerbates the increasing teacher shortage observed in…

Modeling and predicting student learning in computer-based environments often relies solely on sequences of accuracy data. Previous research suggests that it does not only matter what we learn, but also how we learn. The detection and analysis of learning behavior becomes especially important, when dealing with open-ended exploration environments,…