In the ever-evolving world of interior design education, a groundbreaking study published in the *Journal of Asian Architecture and Building Engineering* (translated from Japanese as *Journal of Asian Architecture and Building Engineering*) is set to revolutionize how we assess and enhance internship programs. Led by Mazen Arabasi from the University of Petra, this research delves into the critical role of internships in shaping the future of interior design students in Jordan and Bahrain, offering a data-driven approach to predict internship success.
Internships have long been a cornerstone of interior design education, providing students with practical training and industry exposure. However, the challenge lies in assessing these programs effectively to ensure they meet educational and professional standards. Arabasi’s study addresses this gap by employing machine learning algorithms to identify key factors influencing internship success.
The research compares the professional skill development, software skills, and internship performance of students from Jordan and Bahrain. Using classification models such as Decision Tree, SVM, Random Forest, Logistic Regression, KNN, and Naïve Bayes, the study predicts internship success based on software skills, internship hours, and teamwork skills. Among these models, Random Forest emerged as the most accurate predictor, achieving a remarkable 95.1% success rate and an AUC of 0.92.
“Our findings highlight the importance of longer internships, teamwork training, and computer skills in predicting internship success,” Arabasi explains. “By leveraging machine learning, we can provide a more predictive and evidence-based assessment of internship programs, ultimately enhancing the quality of education and professional development for interior design students.”
The study’s regression analysis further underscores the significance of internship length, teamwork, and computer skills as key predictors of success. With longer internships, teamwork training, and computer training identified as critical components, the research offers valuable insights for curriculum development and program improvement.
The implications of this research extend beyond the academic realm, impacting the commercial sector as well. By providing a data-driven approach to internship assessment, the study offers a blueprint for industries to enhance their training programs, ensuring that future professionals are well-equipped with the skills and knowledge needed to excel in the field.
As the interior design industry continues to evolve, the integration of machine learning and predictive analytics in education and training programs will play a pivotal role in shaping the future of the profession. Arabasi’s research not only sets a new standard for internship assessment but also paves the way for innovative approaches to education and professional development in the interior design sector.
In a world where data-driven decisions are becoming increasingly important, this study serves as a testament to the power of machine learning in transforming education and training programs. As we look to the future, the insights gained from this research will undoubtedly shape the way we prepare the next generation of interior design professionals, ensuring they are ready to meet the challenges and opportunities that lie ahead.