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Previous research has demonstrated a link between prior knowledge and student success in engineering courses. However, while course-to-course relations exist, researchers have paid insufficient attention to internal course performance development. This study aims to address this gap—designed to quantify and thus extract meaningful insights—by examining a fundamental engineering course, Statics, from three perspectives: (1) progressive learning reflected in performance retention throughout the course; (2) critical topics and their influence on students’ performance progression; and (3) student active participation as a surrogate measure of progressive learning. By analyzing data collected from 222 students over five semesters, this study draws insights on student in-course progressive learning. The results show that early learning had significant implications in building a foundation in progressive learning throughout the semester. Additionally, insufficient knowledge on certain topics can hinder student learning progression more than others, which eventually leads to course failure. Finally, student participation is a pathway to enhance learning and achieve excellent course performance. The presented analysis approach provides educators with a mechanism for diagnosing and devising strategies to address conceptual lapses for STEM (science, technology, engineering, and mathematics) courses, especially where progressive learning is essential. 

In engineering, students’ completion of prerequisites indicates an understanding of fundamental knowledge. Recent studies have shown a significant relationship between student performance and prior knowledge. Weak knowledge retention from prerequisite coursework can present challenges in progressive learning. This study investigates the relationship between prior knowledge and students’ performance over a few courses of Statics. Statistics has been considered as the subject of interest since it is the introductory engineering course upon which many subsequent engineering courses rely, including many engineering analysis and design courses. The prior knowledge was determined based on the quantitative and qualitative preparedness. A quiz set was designed to assess quantitative preparedness. The qualitative preparedness was assessed using a survey asking students’ subjective opinions about their preparedness at the beginning of the semester. Student performance was later quantified through final course grades. Each set of data were assigned three categories for grouping purposes to reflect preparedness: 1) high preparedness: 85% or higher score, 2) medium preparedness: between 60% and 85%, and 3) weak preparedness: 60% or lower. Pearson correlation coefficient and T-test was conducted on 129 students for linear regression and differences in means. The analysis revealed a non-significant correlation between the qualitative preparedness and final scores (p-value = 0.29). The data revealed that students underestimated their understanding of the prerequisites for the class, since the quantitative preparedness scores were relatively higher than the qualitative preparedness scores. This can be partially understood by the time gap between when prerequisites were taken and when the course under investigation was taken. Students may have felt less confident at first but were able to pick up the required knowledge quickly. A moderately significant correlation between students’ quantitative preparedness and course performance was observed (p -value < 0.05). Students with high preparedness showed >80% final scores, with a few exceptions; students with weak preparedness also showed relatively high final scores. However, most of the less prepared students made significant efforts to overcome their weaknesses through continuous communication and follow-up with the instructor. Despite these efforts, these students could not obtain higher than 90% as final scores, which indicates that level of preparedness reflects academic excellence. Overall, this study highlights the role of prior knowledge in achieving academic excellence for engineering. The study is useful to Civil Engineering instructors to understand the role of students’ previous knowledge in their understanding of difficult engineering concepts. 

Las Vegas valley has undergone significant development, thus increasing urban flooding. This study analyzes the impacts of urban development on urban flooding in the Flamingo watershed by using a watershed model. The input data includes precipitation, soil characteristics, elevation, and land cover. Urban development is incorporated through increasing percent impervious. Sub-watersheds and streamlines were delineated in ArcGIS using digital elevation model (DEM) dataset. Natural Resources Conservation Service (NRCS) curve-number method was used for the calculation of runoff. The Hydrologic Engineering Center-Hydrologic Management System (HEC-HMS) was used to estimate the discharge hydrograph. The model was calibrated through changing the curve number of the sub-basins. Two urbanization scenarios created with a 5% and 10% increase in impervious surfaces were generated. The results showed that peak discharge occurred earlier due to increase in impervious surfaces. Moreover, the total discharge volume and peak discharge for a given storm event were increasing due to increased imperviousness from urbanization. This study provides useful insight into a hydrological response to urban development that can be helpful in flood remediation.