Engineering students enter a challenging sector in higher education and are potentially at risk of poor mental health and or mental wellbeing and less likely to seek help when experiencing poor mental health or wellbeing. We carried out a scoping review using Joanna Briggs Institute scoping review methodology. Ten databases were searched over a…

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…

Success in post-secondary engineering graphics courses in technology and engineering often relies on self-efficacy, academic success, and mental rotation abilities. Using a facilitative instructor model, the Improving Undergraduate STEM Education (IUSE) team applied active learning modules as supplemental material at two post-secondary…

The problematic persistence rates that many colleges and schools of engineering encounter has resulted in ongoing conversations about academic readiness, retention, and degree completion within engineering programs. Although a large research base exists about student preparedness in engineering, many studies report a wide variety of factors that…

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…

Many degree-seeking college students struggle academically and ultimately never graduate. Academic challenges and persistence within the major are especially salient issues for students who major in science, technology, engineering, and mathematics. Academic probation serves as a means for informing students that they are at risk of dismissal, and…

This study concerns students in an internationally accredited undergraduate mechanical engineering program who due to consistent poor grades are academically dismissed and, by existing policy, required to take a 1-year leave of absence. The purpose was to determine whether the policy could be improved to offer more proactive solutions to address…

Students identified as at-risk of non-academic continuation have a propensity toward lower academic self-efficacy than their peers (Lent, 2005). Within engineering, self-efficacy and confidence are major markers of university continuation and success (Lourens, 2014 Raelin, et al., 2014). This study explored academic learning self-efficacy specific…

Studies show that a significant fraction of students graduating from high schools in the U.S. is ill prepared for college and careers. Some problems include weak grounding in math and writing, lack of motivation, and insufficient conscientiousness. Academic institutions are under pressure to improve student retention and graduate rates, whereas…

Part of a more extensive National Science Foundation-funded study, this study presents the findings and analysis of the effect on three-dimensional modeling self-efficacy (3DSE) by the inclusion of online active learning modules (ALM). Using multiple datasets, we found that the use of ALM in an introductory engineering graphics course, closed a…

Student learning inventories are used by both researchers and educators as tools to identify "at risk" students. This article critically interrogates the results of one of these inventories, the 18-item Approaches to Learning and Studying Inventory. In-depth interviews were held with a purposive sample of 10 first-year engineering…

This study focuses on quantitative long-term effects of Supplemental Instruction (SI) in terms of graduation and dropout rates. One of the main aims of SI is to introduce students to effective study strategies and techniques. If SI is introduced at an early stage for new students in higher education, it should therefore be expected that this…

This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide…

Retention and graduation rates for engineering disciplines are significantly lower than desired, and research literature offers many possible causes. Engineering learning communities provide the opportunity to study relationships among specific causes and to develop and evaluate activities designed to lessen their impact. This paper details an…

Schools are raising standards to improve academic and technical achievement and intellectual growth for all groups of students, particularly at-risk students. This newsletter contains tools and strategies that school leaders and teachers are using successfully to help students meet higher expectations. It organizes the techniques into five…