Visualizing Concepts in Organic Chemistry

As a new Assistant Professor teaching a large organic chemistry lecture (150+ learners), I do my best to integrate my research into my instruction. My doctoral research with Maria Oliver-Hoyo at NC State involved the development of instructional resources that utilize physical and augmented reality models to support learners in visualizing the concepts relevant to infrared (IR) and proton nuclear magnetic resonance (1H NMR) spectral elucidation. The physical model, used for IR spectroscopy, incorporated the harmonic oscillator model and Hooke’s Law to study dynamic vibrations between atoms and addressed concepts such as reduced mass, bond order, electronegativity, bond dipole, and bond polarity.1 The augmented reality models, for 1H NMR spectroscopy, incorporated ball and stick structures, electrostatic potential maps, and molecular orbitals to illustrate the principles of nuclei spin, proton equivalence, and electron distribution.2 The activities were designed using purposeful Contrasting Cases to demonstrate how changes at the structural level influence chemical phenomena.3,4 The activities have been thoroughly tested in organic chemistry laboratories. The results reveal the activities support learners in visualizing structures and developing sound assumptions and mental models about the interactions between energy and matter related to these spectroscopic techniques.5 Approaching spectral elucidation from a conceptual standpoint allows learners to use conceptual reasoning to incorporate the why in their problem-solving. This process is not easy, but it supports learners in making connections across the chemistry curriculum and developing interconnected knowledge. Though I am unable to implement the entire activity in the lecture, I use components of each and expect learners to not only assign peaks to hydrogen atoms or functional groups but to use their chemical knowledge to justify why they correspond. I believe incorporating these fundamental concepts throughout the curriculum and guiding learners to find patterns supports learners in constructing meaningful, long-term chemical knowledge and prevents a fragmented understanding that is pervasive in organic chemistry.

Within my post-doctoral research with Maia Popova at UNCG, I investigated learner competence with representations of molecular structure (skeletal structures, dash-wedge diagrams, chair conformations, etc.). Our investigation revealed that learner competence and reasoning with representations varied depending on the skill (i.e., the ability to interpret, translate, generate, or use representations), the representation, and whether learners attend to the mode of the representation or the conceptual information embedded in the representation.6 Often, their answers and reasoning were misaligned through rule-based patterns that, at best, conditionally applied to the tasks at hand. This investigation led to the ORCA (the Organic chemistry Representational Competence Assessment), which measures learner competence in interpreting, translating, and using representations of molecular structure. A publication detailing the assessment development is under review, but the findings illuminate that as an instructor, I cannot assume my learners are competent with representations of molecular structure after initial instruction. It is important to provide learners with prolonged practice to continuously build their competence. As a result of my research, I formatively assess student representational competence with molecular structure, even with fundamental skills (e.g., interpreting and translating) throughout the semester. Furthermore, I incorporate learner answers and reasoning into the TopHat (clicker style) questions I use in lecture, to target the why behind student reasoning.

Chemists use representations as tools;7 they leverage them for a particular purpose. Whether it is spectra or representations of molecular structure, I try to teach my learners how and when to leverage representations by focusing on their affordances and the conceptual information embedded within them. Part of my current research program as an Assistant Professor at Indiana University Indianapolis investigates how learners leverage digital resources (YouTube, Augmented Reality, generative AI) and how these resources should be designed or scaffolded to become effective tools that give learners agency in navigating technology and support the learning of organic chemistry. 

 

References

(1)         Wright, L.; Oliver-Hoyo, M. T. Supporting the Teaching of Infrared Spectroscopy Concepts Using a Physical Model. J Chem Educ 2019, 96 (5), 1015–1021. https://doi.org/10.1021/acs.jchemed.8b00805.

(2)         Wright, L.; Oliver-Hoyo, M. Development and Evaluation of the H NMR MoleculAR Application. J Chem Educ 2021, 98 (2), 478–488. https://doi.org/10.1021/acs.jchemed.0c01068.

(3)         Graulich, N.; Schween, M. Concept-Oriented Task Design: Making Purposeful Case Comparisons in Organic Chemistry. J Chem Educ 2018, 95 (3), 376–383. https://doi.org/10.1021/acs.jchemed.7b00672.

(4)         Alfieri, L.; Nokes-Malach, T. J.; Schunn, C. D. Learning Through Case Comparisons: A Meta-Analytic Review. Educ Psychol 2013, 48 (2), 87–113. https://doi.org/10.1080/00461520.2013.775712.

(5)         Wright, L.; Oliver-Hoyo, M. T. Student Assumptions and Mental Models Encountered in IR Spectroscopy Instruction. Chemistry Education Research and Practice 2020, 21 (1), 426–437. https://doi.org/10.1039/c9rp00113a.

(6)         Ward, L. W.; Rotich, F.; Hoang, J.; Popova, M. Representational Competence Under the Magnifying Glass—The Interplay Between Student Reasoning Skills, Conceptual Understanding, and the Nature of Representations. In Student Reasoning in Organic Chemistry: Research Advances and Evidence-based Instructional Practices; Graulich, N., Shultz, G., Eds.; Advances in Chemistry Education Series; Royal Society of Chemistry: Cambridge, 2022; pp 36–55. https://doi.org/10.1039/9781839167782.

(7)         Kozma, R.; Chin, E.; Russell, J.; Marx, N. The Roles of Representations and Tools in the Chemistry Laboratory and Their Implications for Chemistry Learning. Journal of the Learning Sciences 2000, 9 (2), 105–143. https://doi.org/10.1207/s15327809jls0902_1.

 

 

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