Empowering Thermodynamic Model Understanding in Chemical Engineering Undergraduate Education: A Classroom Intervention Study With Aspen Plus

ABSTRACT Understanding how students conceptualize and apply thermodynamic models remains a challenge in chemical engineering education. This study addresses how a regression‐based activity influences conceptual understanding, develops model‐based reasoning, and shapes student engagement. The study was conducted in a third‐year undergraduate course ( n = 38) using Aspen Plus. A four‐stage inquiry‐based activity guided students through parameter regression using literature‐reported liquid–liquid equilibrium data, model validation, and reflection. Learning was evaluated using questionnaires, performance‐based tasks, and thematic analysis of student work. Results show that 97.4% of students reported the ability to interpret binary interaction parameters, 92.1% understood model assumptions, 89.5% could evaluate predictive accuracy, and 100% demonstrated competence in parameter regression. Qualitative findings indicate active engagement in comparing predictions and identifying model limitations. These results suggest that data‐driven modeling activities can support conceptual understanding and model‐based reasoning, while further validation across contexts is needed.