Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…

As institutions cope with the difficult task of managing scarce resources to support student learning, college writing centers, like other student services, need to be able to articulate and, at times, quantify the benefits they offer the populations they serve. This study examined outcomes associated with visiting the writing center at one…

Using data from 11 institutions, the authors investigate enrollments in intermediate microeconomics to determine characteristics of successful and unsuccessful students and follow the retake behavior of unsuccessful students. Successful students are significantly different from unsuccessful ones, and unsuccessful students differ by type…

Choosing a major field of study is important, and so is the selection of a specialized concentration that is aligned with an individual's career aspirations. In this paper, we explore a relatively newer concentration in the area of business, namely business analytics. The field of analytics has seen a rapid growth in recent years and enrollment in…

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…

This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…

This paper analyzes the dataset collected from students participating in the Boy With A Ball (BWAB) program, a faith-based community outreach group, through the Hemingway Measure of Adult Connectedness©, a questionnaire measuring the social connectedness of adolescents. This paper first approaches the data in the conventional method provided by…

Graduation from high school is a commonly accepted measurement of academic achievement within the PK-12 education system. Failure to meet this academic milestone is well noted to result in unfavorable outcomes in the transition to, and throughout, adulthood; negatively impacting individuals' health and employment opportunities, while increasing…

Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student…

Higher education institutions have prioritized supporting undecided students with their major and career decisions for decades. This study used a U.S. public research-focused university's large-scale institutional data set and undecided student's retention and graduation rate predictors to demonstrate how to couple student and institutional data…

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…

Although studies have investigated educational attainment of groups of students professing low and high educational self-expectations, groups of noncommittal students, rather than being studied as a discrete group, have been treated as missing and ignored. This study investigated the differences between students of noncommittal, low, and high…

Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on…

Improvement in undergraduate retention and progression is a priority at many US postsecondary institutions. A number of institutions address this issue by identifying gateway courses (foundational courses in which a large number of students fail or withdraw) and concentrating on "fixing" them. This paper argues that may not be the best…