This study investigated the effectiveness of blended instruction in enhancing student success in an advanced undergraduate engineering course. The research used learning analytics captured from pre-recorded lecture videos, course grade data, and student surveys. Results revealed positive correlations between lecture video viewership and course…

Learning Analytics tools are employed to assess student engagement with the Virtual Campus in an undergraduate Fluid Mechanics course at university level in Spain. This is aimed at obtaining a diagnosis of the course problematics which include low attendance rates, poor performance on activity tests and exams and a high number of re-enrolments. A…

Contribution: A competency assessment framework that enables learning analytics for course monitoring and continuous improvement. Our work fills the gap in systematic methods for competency assessment in higher education. Background: Many institutions are shifting toward competency-based education (CBE), thus encouraging their educators to start…

As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI…

The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or…

First-Year Engineering (FYE) programs are a critical part of engineering education, yet they are quite complex settings. Given the importance and complexity of FYE programs, research to better understand student learning and inform design and assessment in FYE programs is imperative. Therefore, this dissertation showcases various uses of data…

"Blackboard" is an important Learning Management System (LMS) employed at most higher education institutions to engage and interact with students during their studies. Students within Material Science and Engineering (MSE) often use these LMS's to absorb mathematical derivations, scientific information and submit coursework tasks. In…

A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level.…

An association between students' learn-online engagement and academic performance was investigated for a third-year Water Resources Systems Design course at the University of South Australia in 2017. As the patterns of data were non-parametric, Mann-Whitney and Kruskal-Wallis tests were performed using SPSS. It was revealed from the test results…

Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the…

Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from…