Artificial intelligence to predict university master's program recommendations
DOI:
https://doi.org/10.35564/jmbe.2024.0002Keywords:
university master's program, student satisfaction, artificial intelligence, ai, machine learning, supervised learning, recommendationAbstract
The satisfaction of a student in a master's program can be influenced by factors such as program quality, learning opportunities, guidance and support received, infrastructure and resources available, outcomes, and employability. In this study, impressions of students from the Master's in Financial Counseling and Planning at Universidad Rey Juan Carlos were collected through a survey. These responses were used to train various artificial intelligence models with the aim of predicting whether the master's program would be recommended. The result of retrospective validation shows an accuracy of over 80% in all cases, leading us to conclude that artificial intelligence is a valid tool for this objective. This investigation contributes to understanding the efficacy of AI in predicting student recommendations for master's programs. It highlights the potential of AI models to inform program enhancements and optimize student experiences, while also emphasizing the need for robust research methodologies and considerations of student satisfaction factors
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