Speaker
Description
In undergraduate engineering education, foundational courses in engineering mechanics pose considerable challenges for students due to the abstract and analytical nature of the subject matter. To enhance learning outcomes and provide immediate, formative feedback, automated STACK assignments incorporating the Meclib library for parameterized graphics have been implemented in Bachelor-level courses on statics and dynamics. This paper presents an experience report focusing on the effectiveness of STACK-based assessments and the specific topics and problem types where students encountered significant learning obstacles.
The analysis reveals common challenges related to fundamental topics, including equilibrium analysis, free-body diagrams, and the application of Newton’s laws. Additionally, trends in student errors and misconceptions, particularly related to numerical calculations, correct handling of physical units, and problem interpretation, are examined. Unlike typical symbolic problems in lectures and exercises, the STACK assignments require students to compute numerical results, introducing additional challenges such as rounding errors and error propagation. The assignments are also structured to support the targeted practice of specific mathematical skills essential in mechanics, such as vector decomposition and the application of differentiation rules—including fundamental concepts like the product, quotient, and chain rules. It is discussed how automated, feedback-driven STACK assignments not only help students develop these core problem-solving skills but also motivate them to engage consistently throughout the semester, while enabling instructors to detect and address both conceptual misunderstandings and gaps in essential mathematical skills early in the learning process.
Through this study, it is aimed to provide insights into the design of more effective didactic strategies in mechanics and advocate for broader adoption of automated assessment tools in technical education. The findings underscore the importance of targeted feedback and adaptable problem sets in fostering a deeper understanding of mechanics fundamentals among engineering students.
