In the Classroom
Curricular Change Digests
The Data-Driven Classroom
edited by
Baird W. Lloyd Miami University Middletown Middletown, OH 45042
S. R. Bondeson, J. G. Brummer, and S. M. Wright* Department of Chemistry, University of Wisconsin–Stevens Point, Stevens Point, WI 54481; *
[email protected] Hanson and Bergman have described the use of electron diffraction data to help students distinguish between the molecular geometries of simple molecules (1). They call their method data-driven chemistry because they first present data and then students attempt to determine geometries. A recently published textbook also utilizes a data-driven approach to introduce some topics (2). The authors state that the approach “supports the unifying theme of the process of science by demonstrating to students how experimental data can be used to construct models” (2, page v). In this communication we describe a data-driven approach for teaching freshman chemistry in which experimental data are used to drive the development of models. By participating in this process students develop a deeper and more lasting understanding of chemistry. The Classroom Dynamic The dynamic that is fostered in the data-driven classroom (DDC) is summarized in the following progression: Data ➞ Data Analysis ➞ Model ➞ Applications In the context of the DDC, a model is a description of nature that must explain the data. Models can be pictorial, numerical, or narrative descriptions. The DDC can be contrasted with the widely used approach in which concepts and applications are presented via didactic lectures and subsequently illustrated in the laboratory. Data can be generated by students in the laboratory or by the instructor via the literature, Internet, or observations of a classroom demonstration. Often data are taken directly from a textbook. Students then begin the hard work of organizing and presenting the data in a meaningful way. This may involve some of the following tasks: making a table, graphing, computing an average, or developing a formula. Formatting helps students scrutinize the reliability of the data, discover trends in the data, and summarize the data into a generalized statement. Students have considerable difficulty organizing data and extracting trends, and we must allot a generous amount of time and guidance for these tasks. The difficult and creative work of explaining the data now begins. Students are forced to wrestle with questions of “Why does nature behave this way?” They need careful guidance from a patient, well-prepared instructor and often find that talking through ideas helps crystallize and refine their understanding. The goal is to have students explain and defend the details of their models based on the observed data trends. Their models need to be predictive, and predictions must be tested. The ideal progression as the course unfolds would be to develop one model fully and then present students with data that require the model to be modified or abandoned in favor of a more correct one.
56
Once a model is provisionally accepted it is applied to traditional chemistry problems. For example, students could be asked to classify simple chemical equations (e.g., combination, decomposition) on the basis of their Dalton-like model of matter. This simple model could then lead into an initial discussion of stoichiometry. In this way, stoichiometry and reaction types become an application of the students’ Dalton-like model of matter. A student-centered, collaborative approach is a beneficial outcome of the DDC. Presenting principles as scientific dogma is kept to a minimum. Some textbooks and lecture styles encourage the sense that chemistry is a static collection of truths, and the notion that science is dynamically progressing is foreign to most students’ thinking. The classic work of Kuhn demonstrates that science conceptions grow by argument and debate, by critical analysis and revision, as well as by experimentation (3). This is the essence of the DDC: students critically analyze data and develop explanations. They expand their understanding of chemistry concepts through discussion with their peers. Thus interactive lecture and collaborative exercises become important tools in the process, because students need to develop, discuss, and defend their models relative to the data. Students become the center of classroom activity and gain ownership of their ideas by verbalizing and defending them. The laboratory is also transformed under the data-toprinciples paradigm. Laboratory remains a venue for practicing techniques, but exercises are designed that allow students to use their laboratory data to develop concepts rather than to verify them. Laboratory exercises focus on collecting data and discovering trends. In order to see trends, students may need more data than they alone are able to generate, so some experiments may require pooling of lab data. Once trends are established, students apply, extend, or invent models to explain those trends. Sometimes data are taken from the laboratory into lecture in order to discuss them more thoroughly, thereby establishing an active link between the two settings. The Data-Driven Approach in Action We have utilized the DDC format in our freshman chemistry courses and have observed that students are generally more animated in the classroom; they ask more questions and discuss chemistry more frequently among themselves. Instruction is more student centered, and students are tracking ideas—that is, the concepts of chemistry. Below are several other observations concerning the data-driven approach. First, it is time intensive. Therefore, not as many topics can be covered. Our contention is that material covered is not necessarily material learned. Although less content may be addressed, students retain more knowledge and understanding because they have been active participants in the process.
Journal of Chemical Education • Vol. 78 No. 1 January 2001 • JChemEd.chem.wisc.edu
In the Classroom
The payback will be in future courses where less time will be devoted to reviewing. Second, less classroom time is spent on algorithmic problem solving because the focus is on data, ideas, and concepts. Instructors in future courses rightly expect proficiency in these kinds of calculations (e.g., stoichiometry, pH, buffer, and thermochemistry calculations). One solution to this dilemma is to assign guided problem worksheets for students to perform outside of class. Our observation is that students graduating from the DDC have problem-solving skills comparable to those of students emerging from our principlesdriven classrooms. Third, some topics, especially those involving conventions and techniques, cannot be developed using the data-driven approach and must be presented via a more didactic style. The DDC is challenging for both students and instructors. For instructors (i.e., us), it is much easier to deliver lectures than to design and implement student-centered, collaborative, model-building exercises. Even the best planned exercises sometimes progress in directions that cannot be foreseen, particularly during the initial application of that exercise. Often alterations must be made during class and discussions directed and summarized appropriately. For students, it is a new
experience to actively participate in the scientific process of critically evaluating data and formulating and defending explanations. Additionally, extra demands are placed on students outside the classroom (problem assignments and worksheets). The payoff of the DDC is that it encourages habits of critical thinking while fostering a deeper understanding of chemistry concepts. These benefits give reason to consider incorporating this approach throughout the entire chemistry curriculum. A complete description of the DDC, including sample exercises and assessment, is available online at http:// www.uwsp.edu/chemistry/chemproj/fipse. The role of the DDC in promoting critical thinking and conceptual development is also described. Literature Cited 1. Hanson, R. M.; Bergman, S. A. J. Chem. Educ. 1994, 71, 150. 2. Spencer, J. N.; Bodner, G. M.; Rickard, L. H. Chemistry: Structure and Dynamics; Wiley: New York, 1999. 3. Kuhn, T. S. The Structure of Scientific Revolution; The University of Chicago Press: Chicago, 1970.
JChemEd.chem.wisc.edu • Vol. 78 No. 1 January 2001 • Journal of Chemical Education
57
