Evaluating Project-Based Learning Success: Unveiling Insights through Supervised Machine Learning Assessment

Abstract

In physics education research, the success of implementing the project-based learning model is typically assessed based on students' learning outcomes. There are very few studies that specifically evaluate the success of implementing the project-based learning model in terms of the process. This is due to the difficulty of assessing the process itself. The aim of this research was to obtain information on the success profile of implementing the Project Based Learning (PBL) model assessed using Supervised Machine Learning. This research used a qualitative descriptive method. The subjects of the research were 60 undergraduate students Jambi University. Data was collected based on the students' success in each stage of PBL. The stages of PBL include 1) project team introduction and planning; 2) initial research phase for information gathering; 3) creation, development, initial evaluation, and prototyping; 4) second research phase; 5) final development stage; and 6) publication of the product or artefact. Every stage of project-based learning is structured using supervised machine learning. This aims to facilitate students' direct involvement in project-based learning in supervised machine learning, enabling teachers to rapidly evaluate and give feedback. The success of project-based learning depends mostly on the precision of the students' group in project design, as indicated by studies. If the planning process is executed well, including tool and material selection, project execution procedures, and good design, students will have no challenges during the product creation stage.