Abstract: This study aims to enhance deep learning and improve the quality of physical chemistry education by applying the DOK (Depth of Knowledge) theory to categorize teaching content. Using the teaching of “additional pressure and vapor pressure of curved liquid surfaces” in surface chemistry as a case, we design diverse classroom activities and group tasks that promote higher-order thinking. To address the challenging Kelvin equation, we incorporate performance tasks that connect theoretical concepts to real-life applications, sparking student interest. Through a progressive, discussion-based approach in group work, students explore the impact of surface tension on chemical potential and extend the Kelvin equation from single-component systems to multi-component solutions and reactions, illustrating thermodynamic concepts and fostering meaningful understanding. A blended teaching approach was adopted, combining pre-class SPOC (Small Private Online Course) tasks, flipped classroom discussions via Rain Classroom, and post-class expansion through public accounts. Teaching practice, supported by surveys and student outputs, demonstrates that DOK-based deep learning not only enhances higher-order cognitive skills but also significantly improves students' learning initiative and practical innovation abilities.

Key words: Deep learning, DOK, Physical chemistry, Performance task, Teaching practice