Data mining approaches have been widely used to estimate student performance in online education. Various Machine Learning (ML) based data mining techniques have been developed to evaluate student performance accurately. However, they face specific issues in implementation. Hence, a novel hybrid Elman Neural with Apriori Mining (ENAM) approach was…

An effective educational program warrants the inclusion of an innovative construction that enhances the higher education efficacy in such a way that accelerates the achievement of desired results and reduces the risk of failures. Educational decision support system has currently been a hot topic in educational systems, facilitating the pupil…

This essay critiques the emphasis on detecting artificial intelligence (AI) usage in student submissions and advocates for a shift towards the meaningful integration of AI in education. Citing data from Turnitin, it highlights the significant yet understated prevalence of AI in academic work. The discussion underscores the ideological, detection,…

Artificial intelligence technology has become widely used in many industries, including healthcare, agriculture, banking, social security, and home furnishings, due to the rise and development of this discipline. One of the newest areas of technology in the education industry is AI in Education, where extensive research supports instructional…

"Generating College Completion: Charting a Path to Institutional AI Adoption for Student Success in Higher Education" provides a comprehensive framework for institutional AI adoption in higher education, focusing on student success and completion. The report examines different approaches to AI adoption, ranging from building in-house…

Today, the main aim of educational institutes is to provide a high level of education to students, as career selection is one of the most important and quite difficult decisions for learners, so it is essential to examine students' capabilities and interests. Higher education institutions frequently face higher dropout rates, low academic…

Monitoring students' academic progress is vital for ensuring timely completion of their studies and supporting at-risk students. Educational Data Mining (EDM) utilizes machine learning and feature selection to gain insights into student performance. However, many feature selection algorithms lack performance forecasting systems, limiting their…

Recently, tracking student behavior has become a very important phase for constructing adaptive educational systems. Several researchers have developed various methods based on machine learning for better tracing students' knowledge. Most of these methods have shown an effective estimation of student features and an accurate prediction of future…

This study examines the impacts of AI-powered personalized education on student achievement and future perspectives. Personalized education refers to teaching methods tailored to the individual needs and learning paces of students, made more effective by the opportunities offered by AI. AI technologies transform educational processes such as…

Objective: This study examines how accurately a small set of short-term academic indicators can approximate long-term outcomes of community college students so that decision-makers can take informed actions based on those indicators to evaluate the current progress of large-scale reform efforts on long-term outcomes, which in practice will not be…

Assessment refers to a broad array of approaches for measuring or evaluating a person's (or group of persons') skills, behaviors, dispositions, or other attributes. Assessments range from standardized tests used in admissions, employee selection, licensure examinations, and domestic and international large-scale assessments of cognitive and…

This article attempts to address the workforce crisis with implications for economic competitiveness and national defense faced by America and the dichotomy of STEM needs and available employees. Businesses struggle to fill critical skilled roles in STEM occupations and thus suffer sluggish growth. In fact, some estimate up to 2.4 million STEM…

The paper outlines the potential for Artificial Intelligence (AI) to positively impact student success. This will be approached from a student life-cycle perspective, taking an integrated view of the student experience and identifying where AI can be most beneficial. Current usages of AI in education will be considered, in addition to those being…

In this paper we have discussed a novel method which has been developed for representation and retrieval of cases in case based reasoning (CBR) as a part of e-learning system which is based on various student features. In this approach we have integrated Artificial Neural Network (ANN) with Data mining (DM) and CBR. ANN is used to find the…

Musso et al. (2013) predict students' academic achievement with high accuracy one year in advance from cognitive and demographic variables, using artificial neural networks (ANNs). They conclude that ANNs have high potential for theoretical and practical improvements in learning sciences. ANNs are powerful statistical modelling tools but they can…