This study aims to employ the supervised machine learning algorithms to examine factors that negatively impacted academic performance among college students on probation (underperforming students). We used the Knowledge Discovery in Databases (KDD) methodology on a sample of N = 6514 college students spanning 11 years (from 2009 to 2019) provided…

The study investigated the joint contribution of the self-regulated learning (SRL) and individual differences approaches to the prediction of university students' grade point average (GPA) obtained at three separate time points throughout their degree (3 years). We assessed cognitive (i.e., previous academic performance, cognitive ability, and…

The aim of the study was to examine self-motivation and study ethics as predictors of academic achievement among undergraduates in a Nigerian University. The study employed the correlation research design in the quantitative approach. Purposive sampling technique was used to draw a sample of 320 students out of which 228 students consented and…

Academic dropout remains a significant challenge for education systems, necessitating rigorous analysis and targeted interventions. This study employs machine learning techniques, specifically random forest (RF) and feature tokenizer transformer (FTT), to predict academic attrition. Utilizing a comprehensive dataset of over 40 000 students from an…

In recent times, Educational Data Mining and Learning Analytics have been abundantly used to model decision-making to improve teaching/learning ecosystems. However, the adaptation of student models in different domains/courses needs a balance between the generalization and context specificity to reduce the redundancy in creating domain-specific…

Given the rapid growth of K-12 online learning, research is needed in the effective identification of at-risk students so that administrators and teachers can develop appropriate supports and interventions. The purpose of this research was to determine if student success in an online course could be predicted for English Learners (EL) using…

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.…

The gap in supply (i.e., shortage) and demand of the STEM workforce have prompted extensive research on identifying factors that predict STEM outcomes and retention of students. Few studies, however, have examined the relationships between STEM outcomes and predictors in an integrated model, taking into account measurement errors in the…

Teacher retention is a foundational issue and schools have historically struggled to retain teachers. Previous research indicates that teachers leave the profession at high rates before year five of teaching expertise. Lack of teacher retention contributes to instability and lack of trust in school districts. This secondary data study explores…

Graduate admission has become critical for quality assurance. An innovative solution is needed to achieve efficiency and consistency in the admission process at institutions. However, the existing research is lack of a simple practical method for informed decision-making for admissions. We develop a multi-criteria decision-making (MCDM) model to…

Doing assignments is a very important part of learning. Students' assignment submission time provides valuable information on study attitudes and habits which strongly correlate with academic performance. However, the number of assignments and their submission deadlines vary among university courses, making it hard to use assignment submission…

Purpose: This study examined predictors of cumulative grade point average (GPA) from entrance scores and successive performance during students' academic work in university engineering programs. Design/methodology/approach: Scores from high school coursework, the General Ability Test and the Achievement Test were examined to determine if these…

This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to…

The self-regulation and self-efficacy beliefs have a major effect on English language learning achievement. This study examines if self-regulatory learning strategies and English self-efficacy beliefs significantly predicted learning achievement in learning English. 542 ninth grade students studying at ten different Vocational and Technical…

Structural equation modelling was used to investigate the predictive associations between kindergarten developmental, socio-behavioural, and biotic influences on third grade achievement among a nationally representative sample of low-SES children. The findings validate the understanding that developmental and learning trajectories start early in…