Background: Although most engineering graduate students are funded and usually complete their degrees faster than other disciplines, attrition remains a problem in engineering. Existing research has explored the psychological and sociological factors contributing to attrition but not the structural factors impacting attrition. Purpose/Hypothesis:…

Cooperative education programs are usually based on a partnership between companies and universities. Dropouts have a particular impact here, for example the loss of junior staff in the companies. Most dropouts in cooperative education occur in the first academic year. In this multicausal dropout process, the influence of the cooperation partner…

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…

Engineering design plays an important role in education. However, due to its open nature and complexity, providing timely support to students has been challenging using the traditional assessment methods. This study takes an initial step to employ learning analytics to build performance prediction models to help struggling students. It allows…

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…

Contribution: The present research provides new evidence on the predictors of foreign-born (FB) students' dropout and the situations of discrimination that emerge on engineering campuses. Background: Ethnic minority students remain underrepresented in engineering degrees and have higher dropout rates than their White peers. Previous literature…

Early warning systems (EWSs) have proven to be useful in identifying students at risk of failing both online and conventional courses. Although some general systems have reported acceptable ability to work in modules with different characteristics, those designed from a course-specific perspective have recently provided better outcomes. Hence, the…

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…

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…

Creating a culturally responsive (Ladson-Billings 1995) or culturally sustaining (Paris 2012) learning environment requires teachers making connections to students' experiences outside of school, allowing them to draw on what González, Moll, and Amanti (2006) refer to as students' "funds of knowledge," or the values, interests, and…

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…

Contribution: Screening rates for engineering students for several major and moderate mental health issues are reported, including unspecified psychological distress as captured by the Kessler 6 screening instrument; screening rates for depressive, anxiety, and eating disorders as measured by the patient health questionnaire (PHQ); and screening…

We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not…

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…