Good learning management so that learning has a positive impact on its recipients (students), or in other words, results in active learning. Active learning is a learning approach where students strive to acquire various knowledge during class learning, this is reflected in the increasing understanding and experience of the students, also allowing students to develop their thinking, for example, such as analytical skills. Another factor why active learning is necessary is because the times continue to develop and each person's characteristics are different, one of which is currently the millennial generation where they generally prefer to collaborate on assignments because they enjoy communicating with their friends.Research objective: To analyze students' experiences in participating in project-based learning, including aspects of planning, implementation, evaluation, and completion of project assignments . The method used in this research is a mixed methods method. This research is a research step by combining two forms of research that have previously existed, namely qualitative research and quantitative research. According to Creswell, mixed research is a research approach that combines qualitative research with quantitative research. Correlation shows a linear relationship between the variables in this study. Correlation between variables is calculated using the Pearson coefficient whose value ranges from -1 to +1. Positive values indicate a unidirectional relationship (the higher one variable, the higher the other variable), while values close to zero indicate a weak relationship or no linear relationship. The Quality variable, which represents overall quality, shows a strong positive correlation with several other key variables, specifically: Allianc (r = 0.955), indicating a very strong relationship, Imp2 (r = 0.844), Respond (r = 0.823). Model Explanatory Power The R-squared value shows that the PjBL variable has moderate explanatory power (17.1%), while the Response variable has very high explanatory power (100%), which means the model is able to explain the variation in respondent responses comprehensively. This emphasizes that the model built is suitable and relevant in the research context
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