Development of a No-Code Machine Learning Model Builder for Predictive Analytics in Education

Abstract

Machine learning (ML) has the potential to enhance educational predictive analytics, but its adoption is limited by the programming expertise required to develop models. Traditional ML tools require coding skills, which makes them inaccessible to educators and researchers without computational backgrounds. Existing no-code platforms lack affordability and accessibility. This study addresses this gap by developing and validating a no-code ML builder to enable non-programmers to build, evaluate, and deploy ML models. Design and development research approach was adopted in the study. It utilizes Python-based tools such as Streamlit and scikit-learn. The tool underwent expert validation and comparative performance testing against Google Colab using datasets from Kaggle, consisting of 5000 and 2392 student performance records. The results show that the no-code ML builder, which is accessible at nextml.streamlit.app achieved a predictive performance comparable to coded models. A minor performance gap was observed in some algorithms, with Logistic Regression achieving an accuracy of 63.88% compared to 73.28% in Google Colab. Experts in educational technology and computer science rated the tool highly for usability, with mean scores ranging from 4.33 to 4.57. 71% of evaluators found it suitable for educational datasets, and 56% endorsed its ability to handle students' data sets. The study concludes that the tool bridges the accessibility gap in the application of ML in education while maintaining competitive model performance. It recommends that Institutions adopt no-code tools. Future research should focus on incorporating more complex algorithms.