In general, our research group is focused on expanding our understanding of how theories of learning and cognitive development can be applied to the unique learning environments encountered in chemistry. Specifically, we are currently pursuing projects that seek to better understand:
✦ factors that influence meaningful/rote learning in chemistry;
✦ factors that influence epistemological development in chemistry;
✦ the development of representational competence in chemistry;
✦ and, the use of technology in teaching chemistry.
Epistemological Development in Chemistry - Much research in other science, technology, engineering, and mathematics (STEM) disciplines has documented the importance that students’ epistemological views, i.e., students’ beliefs about the nature and scope of learning, have in influencing success in a particular course. Little of this research, however, has been performed in the context of chemistry. Our prior research resulted in the development of a valid and reliable instrument, the CHEMX Survey, to measure these epistemological views and was used to document how such views changed as chemistry students progressed through their study of the subject. Through the utilization of a long-term, longitudinal study, our current work is focused on better understanding how both curricular and extracurricular factors influence the evolution of these views.
The Development of Mechanistic Competence - The use of the curved-arrow notation to convey electron flow during mechanistic processes is ubiquitous in the organic chemistry classroom. Prior research, however, has documented the difficulties that students face in utilizing this representational modality. Our research relies innovative technology - a Tablet PC program called OrganicPad - to track how students’ mechanism use evolves over time. We are also interested in designing and evaluating novel instructional approaches for teaching mechanisms and the use of the curved-arrow notation.
iRespond: iPad/iPhone/iPod touch as Personal Response System - Most currently available personal response systems allow instructors to probe students’ understanding using multiple choice questions or questions that require short alphanumeric responses. While it is possible to create sophisticated multiple choice questions that measure students’ understanding in many areas, in a discipline such as chemistry that requires the development of a wide range of drawing, graphing, calculation, and structure creation and manipulation skills, being required to rely upon only two question-types can be quite confining. We are currently developing iRespond, a suite of tools that will convert Apple’s iPhone, iPod touch, and iPad into an interactive personal response system for use in the chemistry classroom and a series of approximately 200 iRespond questions that can be used in the typical two-semester general and organic chemistry courses. This is supported by the National Science Foundation. See http://besocratic.chemistry.msu.edu/urespond/index.html for more details.
Cognitive Load and Chemical Representations - Representations of chemical structure and process are ubiquitous in the chemistry curriculum. From Lewis structures, to Newman Projections, to the use of curved arrows to convey electron flow during mechanistic processes, novice chemistry students are expected to construct, utilize, and manipulate these representations. Unfortunately, this process is often quite difficult and often hindered by cognitive load issues. This research seeks to develop a reliable and valid method of measuring cognitive load in real time using physiological metrics such as heat flow and heart rate, to use these techniques to research the interplay between students’ construction and use of chemical representations and cognitive load, and to use the research results collected to develop a series of best practice guidelines and suggestions for the use of representations in introductory chemistry courses and a set of activities designed to support general chemistry and organic chemistry students as they learn how to construct and use these representations.