The principles of process-oriented guided inquiry learning (POGIL) are applied to the analysis of the emission spectrum of atomic hydrogen. Over the course of three learning cycles, students construct the hydrogen atom’s energy level diagram and assign quantum numbers using their measurements of the Balmer series plus additional information on the emission lines in the Lyman and Paschen series. This guided inquiry approach to the hydrogen emission spectrum, which was developed as part of the POGIL Physical Chemistry Laboratory Project, has been tested at several institutions with a variety of spectroscopic instruments. The analysis leading to the assignment of quantum numbers requires simple and inexpensive supplies: rulers, colored pens, and long strips of paper.

In the experiment "How can you measure a reaction enthalpy without going into the lab?" we have students use computational thermochemistry to explore the properties and reaction thermodynamics of hydrofluoropropanes. This guided inquiry lab was developed under the Process Oriented Guided Inquiry Lab Physical Chemistry Laboratory (POGIL-PCL) project. Students are asked to find the "best" replacement for the hydrofluoropropane CFC-227ea, which has been used in military fire suppression systems. The compound has been known to decompose at high temperatures to produce poisonous HF, resulting in some casualties. Students are asked to choose an alternative compound based upon properties predicted with computational chemistry. The number of possibilities is large enough that a class will have to pool data to make a selection. As part of their study, students are also asked to evaluate calculational methods for speed and accuracy and to cooperatively choose the "best" method for the class's analysis. The evaluation of methods requires them to compare computational results with experimental values. Finally, students must use their calculational data to rationalize a choice about the "best" fire suppressant molecule.

This chapter describes the author's experience developing computational narrative activities (CNAs). These interactive activities utilize current computing technologies (python and Jupyter notebooks), and use established assignment design strategies to help students learn physical chemistry concepts while developing skills in problem solving, writing, and applied computing. Design considerations and implementation details of CNAs are described in the context of an undergraduate physical chemistry course.