Machine Learning (ML) is a rapidly growing field, therefore ensuring that students deeply understand such concepts is of key importance in order to certify that they are prepared for the challenges and opportunities of the future workforce. Despite this, literature on teaching ML and assessing students' understanding with regard to this field is scarce. Hence, this research aims to provide an extensive analysis of the best practice within the ML field, with the main focus of the study being the decision trees and random forests classifiers. An analysis of learning outcomes is conducted using Bloom's taxonomy, guidelines for creating assessments that reflect students' understanding levels are provided and a series of interviews and surveys are conducted in order to analyze the need for certain questions during the course examination. The results are then analyzed and key findings such as the need to structure the course such that decision trees are assessed as a prerequisite for learning random forests are further discussed. The research is concluded with a set of recommendations that could be integrated into future editions of the course in order to assess student progress in a more efficient manner.

GitHub is the home of hundreds of millions of Open Source Software(OSS) repositories where users collaborate on projects and find inspiration for new ideas. Some of these projects have certain build configurations set up to make building, testing, and deploying the software more time-efficient and less error-prone. However, setting up the correct configurations usually requires a lot of time and a high level of knowledge. This paper aims to analyze the current practices for setting up build configurations like the Maven files and GitHub actions while clustering some of these practices based on the scope of the project. Thus, we provide useful information in terms of discovering similar projects based on the build configurations and discuss the feasibility of build configuration analysis. In summary, we provide a comprehensive analysis of project similarity based on Maven build configurations and workflow files, shedding light on the importance of build configurations for identifying similar projects, and laying the groundwork for future exploration in the realm of build configuration analysis.