Mind the Gap: A Review of Machine Learning Tools in Educational Settings

Abstract

This review examines a wide range of machine learning (ML) tools used in educational settings, focusing on their effectiveness in teaching both ML concepts and Python programming. The analysis covers commonly used platforms such as Google Colab, TensorFlow, Keras, Scikit-learn, Teachable Machine, Create ML, WEKA, KNIME, and Open Mind, assessing their educational value and limitations. The study finds that while low-code and no-code tools lower the barrier to entry for ML education, they often lack the flexibility and depth needed to support the development of advanced analytical and programming skills. In contrast, tools like Python libraries provide robust learning experiences but demand a higher level of prior knowledge. The research identifies a gap between accessible tools and those capable of fostering Python proficiency, emphasizing the need for integrated solutions that balance usability and technical rigor. This work contributes to the discourse on enhancing ML education through strategic tool selection.