Predictive Modeling and Analysis of Student Academic Performance in an Engineering Dynamics Course

Engineering dynamics is a fundamental sophomore-level course that is required for nearly all engineering students. As one of the most challenging courses for undergraduates, many students perform poorly or even fail because the dynamics course requires students to have not only solid mathematical skills but also a good understanding of fundamental concepts and principles in the field. A valid model for predicting student academic performance in engineering dynamics is helpful in designing and implementing pedagogical and instructional interventions to enhance teaching and learning in this critical course. The goal of this study was to develop a validated set of mathematical models to predict student academic performance in engineering dynamics. Data were collected from a total of 323 students enrolled in ENGR 2030 Engineering Dynamics at Utah State University for a period of four semesters. Six combinations of predictor variables that represent students’ prior achievement, prior domain knowledge, and learning progression were employed in modeling efforts. The predictor variables include X1 (cumulative GPA), X2~ X5 (three prerequisite courses), X6~ X8 (scores of three dynamics mid-term exams). Four mathematical modeling techniques, including multiple linear regression (MLR), multilayer perceptron (MLP) network, radial basis function (RBF) network, and support vector machine (SVM), were employed to develop 24 predictive models. The average prediction accuracy and the percentage of accurate predictions were employed as two criteria to evaluate and compare the prediction accuracy of the 24 models. The results from this study show that no matter which modeling techniques are used, those using X1 ~X6, X1 ~X7, and X1 ~X8 as predictor variables are always ranked as the top three best-performing models. However, the models using X1 ~X6 as predictor variables are the most useful because they not only yield accurate prediction accuracy, but also leave sufficient time for the instructor to implement educational interventions. The results from this study also show that RBF network models and support vector machine models have better generalizability than MLR models and MLP network models. The implications of the research findings, the limitation of this research, and the future work are discussed at the end of this dissertation

The e-revolution and post-compulsory education: using e-business models to deliver quality education

The best practices of e-business are revolutionising not just technology itself but the whole process through which services are provided; and from which important lessons can be learnt by post-compulsory educational institutions. This book aims to move debates about ICT and higher education beyond a simple focus on e-learning by considering the provision of post-compulsory education as a whole. It considers what we mean by e-business, why e-business approaches are relevant to universities and colleges and the key issues this raises for post-secondary education

Predicting and Improving Performance on Introductory Programming Courses (CS1)

This thesis describes a longitudinal study on factors which predict academic success in introductory programming at undergraduate level, including the development of these factors into a fully automated web based system (which predicts students who are at risk of not succeeding early in the introductory programming module) and interventions to address attrition rates on introductory programming courses (CS1). Numerous studies have developed models for predicting success in CS1, however there is little evidence on their ability to generalise or on their use beyond early investigations. In addition, they are seldom followed up with interventions, after struggling students have been identified. The approach overcomes this by providing a web-based real time system, with a prediction model at its core that has been longitudinally developed and revalidated, with recommendations for interventions which educators could implement to support struggling students that have been identified. This thesis makes five fundamental contributions. The first is a revalidation of a prediction model named PreSS. The second contribution is the development of a web-based, real time implementation of the PreSS model, named PreSS#. The third contribution is a large longitudinal, multi-variate, multi-institutional study identifying predictors of performance and analysing machine learning techniques (including deep learning and convolutional neural networks) to further develop the PreSS model. This resulted in a prediction model with approximately 71% accuracy, and over 80% sensitivity, using data from 11 institutions with a sample size of 692 students. The fourth contribution is a study on insights on gender differences in CS1; identifying psychological, background, and performance differences between male and female students to better inform the prediction model and the interventions. The final, fifth contribution, is the development of two interventions that can be implemented early in CS1, once identified by PreSS# to potentially improve student outcomes. The work described in this thesis builds substantially on earlier work, providing valid and reliable insights on gender differences, potential interventions to improve performance and an unsurpassed, generalizable prediction model, developed into a real time web-based system