Practical programming assignments are critical parts of programming courses in Computer Science education. Students are expected to translate programming concepts learned from lectures into executable implementations that solve the tasks outlined in the assignments. These implementations are primarily assessed based on their functional correctness, ensuring that students’ code produces the expected output when provided with specific inputs.

However, functional correctness is not the only metric that evaluates the quality of a source code program. Runtime efficiency is a metric that is less frequently evaluated in programming courses, yet it holds significant importance in the context of professional software development. To investigate this gap and its potential ramifications, we conducted a large-scale empirical study on the time efficiency of 250 programming assignments that are evaluated solely on functional correctness. The results demonstrate that students’ programming assignments exhibit significant variance in terms of execution time, with much of this variance being reducible by leveraging automated tools such as PMD, IntelliJ IDEA and ChatGPT. We further identified 27 recurring inefficient code patterns and their fixes, providing actionable guidelines for educators to enhance the organization and integration of code performance topics throughout the programming course curriculum.