2025 ASEE Annual Conference & Exposition

This study explores the integration of Machine Learning (ML) concepts into the curriculum for 6th to 12th-grade students thus, addressing the growing importance of computational skills in the STEM workforce. Our hypothesis is that connecting abstract computing principles to practical ML applications in solving real-world problems through research-based exploratory learning will attract students to ML-related courses and STEM fields at large. Hence, we utilized a real-world example of predicting Alzheimer’s Disease severity based on data drawn from smart home sensors.
This project is the outcome of a Research Experience for Teachers summer program. The curriculum integration was implemented across Mathematics, Algebra, and Statistics subjects. Key components of the integration included exposure to computing fundamentals using Scratch, building basic ML pipelines with explainability in ORANGE, and conducting Automated ML using Aliro. The study also introduced students to Python programming concepts using Google Colab. Each of these software enabled an increasingly level of ML exposure while utilizing similar prediction model framework. Orange is an open source for ML and data analysis through Python scripting and visual programming. It is very user friendly for non-programmers. Aliro is an open-source software package designed to automate ML analysis through a clean web interface. It includes a pre-trained ML recommendation system that assists the students to automate the selection of ML algorithms and its hyperparameters and provides visualization of the evaluated model and data. The smart home data used to train and validate the prediction models is drawn from the CASAS Kyoto dataset.
Despite facing challenges such as time constraints and technological limitations due to school district policies, the project successfully incorporated ML concepts into existing curricula. The integration emphasized the iterative nature of research, model performance evaluation, and the importance of balancing model fit with generalizability. Students were able to learn ML concepts by repetition and reinforcement using the three different software suites (ORANGE, Aliro, and Google Colab). This study contributes to the growing body of literature on STEM education by demonstrating practical approaches to introducing complex ML concepts to secondary school students. It highlights the potential for interdisciplinary learning and the development of critical thinking skills essential for future STEM professionals.