Data First: Building Scientific Reasoning in AP Chemistry via the

Jul 31, 2014 - (2-8) By placing more focus on the concepts themselves and less on applications ..... Analysis of High School Chemistry Teachers' Perfo...
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Data First: Building Scientific Reasoning in AP Chemistry via the Concept Development Study Approach Carolyn A. Nichol,†,‡ Amber J. Szymczyk,‡ and John S. Hutchinson*,† †

Department of Chemistry, Rice University, Houston, Texas 77005, United States School Science and Technology Program, Rice University, Houston, Texas 77005, United States

‡

ABSTRACT: This article introduces the “Data First” approach and shows how the observation and analysis of scientific data can be used as a scaffold to build conceptual understanding in chemistry through inductive reasoning. The “Data First” approach emulates the scientific process by changing the order by which we introduce data. Rather than using data to solve problems after the concept is taught, the “Data First” approach allows students to analyze trends, evaluate discrepant events, and construct knowledge by developing a strong conceptual foundation. Not only is this approach aligned to the AP Chemistry Curriculum Framework, it also is based on research about how people learn. Included are examples showing how this approach can be used to teach a range of chemistry topics. These include using photoelectron spectroscopy data to understand electron configuration, using heats of vaporization to understand intermolecular forces, and using vapor pressure to understand dynamic equilibrium. The “Data First” approach is a resource to support the shift to more conceptual and inquiry-based teaching and learning in AP chemistry. This contribution is part of a special issue on teaching introductory chemistry in the context of the advanced placement (AP) chemistry course redesign. KEYWORDS: High School/Introductory Chemistry, First-Year Undergraduate/General, Curriculum, Inquiry-Based/Discovery Learning, Atomic Properties/Structure, Constructivism, Equilibrium, Molecular Properties/Structure, Professional Development

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INTRODUCTION The new Advanced Placement (AP) Chemistry Curriculum Framework places greater emphasis on concept development and understanding rather than the traditional rote quantitative calculation.1 The Introduction to the AP Curriculum Framework notes that students are expected to “spend less time on factual recall and more time on inquiry-based learning of essential concepts.”1 This is a needed change and is supported by a great volume of research demonstrating that students can perform such rote calculations while fundamentally misunderstanding the conceptual basis for those calculations.2−8 By placing more focus on the concepts themselves and less on applications of those concepts, which require only rote memorization, the new curriculum and exam will push students to a deeper understanding of chemistry. As noted in the Curriculum Framework, this new focus will help students “develop the reasoning skills necessary to engage in the science practices.” This shift will, however, generate significant challenges for AP teachers as they revamp their lesson plans and approaches to facilitate their students’ deeper understanding. Teachers commonly teach the way they were themselves taught,9 so helping them move to a new approach will require new professional development along with new lesson plans that approach the material in a different way. In this article, we address this challenge with an adaptation of the “Concept Development Study” (CDS) approach, which has © 2014 American Chemical Society and Division of Chemical Education, Inc.

been developed, applied, and analyzed in the General Chemistry curriculum at Rice for over two decades.10,11 This new “Data First” approach has been presented and applied in our teacher professional development course at Rice for the past three years. “Data First” means that physical observations and measurements (e.g., gas pressure, solution concentration, ionization energy, valence, bond strength) are treated as data from which chemical concepts, models, and theories are developed rather than predictions to be made from those concepts, models, and theories. This fundamentally reverses the traditional order of presentation and introduces inductive reasoning as the primary method of scientific reasoning. The Data First approach offers several significant advantages in assisting teachers to focus on concept development. First, the data are familiar to them as end results of calculations or deductive reasoning, although not as the foundation for the fundamental concepts. Second, this approach need not take any longer than the conventional approach because the content is approximately the same but the order has been reversed. Third, this approach stresses scientific reasoning, which is deeply embedded in the new AP curriculum. In particular, this approach is consistent with the Curriculum Framework’s goal to “enable students to establish lines of evidence and use them Special Issue: Advanced Placement (AP) Chemistry Published: July 31, 2014 1318

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possible and valuable to use historically relevant experiments and data in these developments. To implement the CDS philosophy, we have written and assembled a set of modules called “Concept Development Studies in Chemistry.” Each CDS builds a single significant concept by pulling together two to four sets of experimental data. There are currently over two dozen such modules available freely online.11 A list of these modules is given in Table 1. Each of these modules has been tested for more than a decade at our university. Our studies have shown that students learn quite effectively from this approach, even in comparing students from stronger backgrounds with those with less preparation.10,12,13

to develop and refine testable explanations and predictions of natural phenomena.”1 In this paper, we will describe the Data First approach in detail and then illustrate it in several examples. We will then discuss our own experiences in introducing this approach to high school teachers.

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DESCRIPTION OF THE APPROACH

Concept Development Studies in Chemistry

Because Data First is based on the CDS approach, we begin with a description of the latter. As the name reveals, in the CDS approach, each major concept in Chemistry is developed from experimental observations and inductive reasoning, rather than simply being presented fait accompli. New observations build on previous ones in succession, leading to iterative refinement of the concept or model. This structure is illustrated in Figure 1, which shows how a conceptual model is built on experimental observations and reasoning.

Data First Approach

The Data First approach is an implementation of the CDS philosophy that we have found to be valuable in helping high school teachers shift to a more conceptual and inquiry-based approach. The goal of Data First is to correct what we view as a backward approach to teaching science content. In many traditional chemistry courses (and indeed, in most science courses in general) content is delivered in a deductive manner. That is, scientific models and theories are usually presented as established facts in a teacher-directed lecture, followed by guided practice illustrating how these models are applied to specific problems which are usually calculation-based.14 A recent survey of science teachers across grade levels conducted by Horizon Research reports that roughly 40% of science teachers believe they should explain concepts to students first before having students consider evidence for those ideas.15 This deductive presentation runs counter to scientific practice. Science is conducted via inductive reasoning, the process by which observations made of specific situations are translated into general models or theories, as shown in Figure 1. The science occurs in the construction of the model, the completion of which is like the climactic final chapter of a well-written mystery novel. However, this construction is typically omitted in textbooks and disregarded by teachers. To illustrate, arguably

Figure 1. Structure and progression of a Concept Development Studies module.

Of considerable importance is that the process illustrated in Figure 1 is reflective of the actual process by which scientific concepts, models, and theories are developed. As such, it is

Table 1. Concept Development Studies in Chemistry 2013 Modules Aligned to AP Chemistry Curriculum Framework Essential Knowledge Statements Concept Development Studies in Chemistry 2013a Module Atomic Molecular Theory Atomic Masses and Molecular Formulas Structure of an Atom Electron Shell Model of an Atom Quantum Electron Energy Levels in an Atom

AP Essential Knowledge 1.A.1, 1.A.2, 1.B.1 1.B.1, 1.B.2,

1.D.1, 1.E.1 1.A.3, 1.E.1, 1.E.2 1.B.2, 1.C.1 1.C.2, 1.D.1, 1.D.3

Electron Orbitals and Electron 1.B.1, 1.B.2, 1.C.2, 1.D.1 Configurations in Atoms Covalent Bonding and Electron Pair Sharing 2.C.1 Molecular Structures 2.C.4 Energy and Polarity of Covalent Chemical Bonds Bonding in Metals and Metal Non-Metal Salts Molecular Geometry and Electron Domain Theory Measuring Energy Changes in Chemical Reactions a

Module

AP Essential Knowledge

Reaction Energy and Bond Energy Physical Properties of Gases The Kinetic Molecular Theory Phase Transitions and phase Equilibrium Phase Equilibrium and Intermolecular Forces Phase Equilibrium in Solutions

5.C.1, 5.C.2 2.A.2 2.A.2, 5.A.1 6.A.1, 6.B.1, 6.B.2 2.A.1, 2.A.2, 2.B.1, 2.B.2, 2.B.3

Reaction Rate Laws Reaction Kinetics

6.A.1, 6.B.1, 6.B.2

2.B.2, 2.C.4

Reaction Equilibrium in the Gas phase

4.A.1, 4.A.2, 4.A.3 4.A.1, 4.B.1, 4.B.2, 4.B.3, 4.C.1, 4.C.2, 4.C.3, 4.D.1 6.A.1, 6.A.2, 6.A.3, 6.A.4, 6.B.1, 6.B.2

2.A.1, 2.C.2, 2.C.3, 2.D.1, 2.D.2, 2.D.3 2.C.4

Acid Ionization Equilibrium

6.C.1, 6.C.2

Entropy and the Second Law of Thermodynamics Free Energy and Thermodynamic Equilibrium

5.E.1, 5.E.2, 5.E.3

3.C.2, 5.A.2, 5.B.1, 5.B.2, 5.B.3, 5.B.4

5.E.3, 6.D.1

Modules available for free online. See ref 11. 1319

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the element is located in on the periodic table. This causes misconceptions as it is actually the opposite that is true: the location of an element in the periodic table is determined by its electron configuration, not vice versa. They are also given an order by which orbitals are “filled” by following an increase in energy sequence but without any justification for why this is the case or how this order was determined.20−22 A typical lesson may include a mnemonic device, card sort or some hands-on activity that helps students learn the “rules” for “writing” the electron configuration for an element.23−25 Students can memorize this pattern but it does not provide a mechanism for students to gain a conceptual understanding of what these subshells are and why there is a limit on the number of electrons in each shell. In the Data First approach, the students are not told about electron configuration but rather students explore the PES data in Table 2 and identify trends by scanning the data line-by-line

the most revolutionary development in science, all matter is composed of atoms, is presented as a foregone conclusion on page two of a popular AP chemistry textbook.16 As students are “left out” of the reasoning behind the development of this fundamental model in chemistry, they are consequently quite often “left out” of understanding it as well. Teachers recognize this disconnect when they make comments to their students such as, “I know it doesn’t make much sense now, but hang in there. I think it will make more sense after I show you an example.” In fact, student understanding is often not significantly improved even after the working of such example problems and “drill and kill” exercises.2−8 However, if students feel reasonably confident that they can reproduce the teacher’s work on similar problems in a test situation, they often settle for this form of rote memorization and call it “understanding.” Ironically, teachers often use traditional lecture approaches in the interest of time, yet this approach commonly necessitates significant time investment in “drill and kill” exercise and review in order to produce positive student outcomes.17,18 The Data First approach repurposes both that time and often the data and examples used as application calculations for use in the initial stages of instruction instead (thus “Data First”). This model provides fodder for class discussion, debate, and analysis and creates a platform for the collective construction of a scientific model robust enough to explain the data, thereby developing many of the AP Chemistry Curriculum Framework’s identified Science Practices, such as using representations and models to communicate scientific phenomena and solve scientific problems (Science Practice 1), performing data analysis and evaluation of evidence (Science Practice 5), and working with scientific explanations and theories (Science Practice 6). We have found that when students are personally engaged in the reasoning used to construct scientific models, they are much more capable of applying the model to problemsolving and are much more flexible in extending the model to novel situations.10,12,13 Thus, the need for “drill and kill” is significantly reduced and the investment of time and energy in the beginning of the lesson cycle is recouped. The Data First approach can be implemented in almost any chemistry topic and thereby brings inquiry learning into every concept and lesson. To illustrate this, we provide specific examples of applications of the Data First model to the instruction of three key topics in the AP Chemistry curriculum. Of these three, one illustrates the atomic scale, one the macroscopic scale, and one the dynamic nature of chemistry.

Table 2. b Ionization Energy Thresholds of Selected Elementsa Element H He Li Be B C N O F Ne Na Mg Al Si P S Cl Ar K Ca b

Ionization Energy Thresholds (MJ/mol) 1.31 2.37 6.26 11.5 19.3 28.6 39.6 52.6 67.2 84.0 104 126 151 179 208 239 273 309 347 390

0.52 0.90 1.36 1.72 2.45 3.12 3.88 4.68 6.84 9.07 12.1 15.1 18.7 22.7 26.8 31.5 37.1 42.7

Reprinted from ref 19.

a

0.80 1.09 1.40 1.31 1.68 2.08 3.67 5.31 7.79 10.3 13.5 16.5 20.2 24.1 29.1 34.0

0.50 0.74 1.09 1.46 1.95 2.05 2.44 2.82 3.93 4.65

0.58 0.79 1.01 1.00 1.25 1.52 2.38 2.90

0.42 0.59

In MJ/mol.

and looking for patterns. The PES data used are all possible f irst ionization energies of gaseous atoms. We can ask students what they observe for hydrogen and helium. How many thresholds are there? It is clear to the students why there is only one for hydrogen. But why is there also only one for helium, when we know it has two electrons? The students can conclude from the data that both electrons must require an identical amount of energy to be removed from the atom. Therefore, they must have identical energies. Why are there two energies for lithium when it has three electrons? Two electrons must have identical energies while one has a different energy. Because the two electrons in helium were also allowed to have identical energies, perhaps this initial energy “level” can only accommodate two electrons? If so, is the two-electron maximum true for all energy levels? Beryllium, boron, and carbon seem to support this idea, but nitrogen has only three distinct energies even though it contains seven electrons, indicating that at least three electrons must occupy the same energy level. In fact, an additional energy level is not gained until after neon, indicating that six electrons

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DATA FIRST EXAMPLE 1: ANALYZING PHOTOELECTRON SPECTROSCOPY DATA TO DEVELOP THE CONCEPT OF ELECTRON CONFIGURATION This example focuses on AP Chemistry Curriculum Framework Learning Objectives 1.5−1.8 and highlights how photoelectron spectroscopy (PES) data can be used to build a model of atomic subshells by exploring the energies of all of the electrons in an atom. More detail about how to use this approach in an AP class can be found in Concept Development Studies in Chemistry 2013.19 In a typical lesson on atomic structure, students are introduced to electron shells and the patterns of electron configuration for elements by being told that electron subshells correspond to orbitals of specific shapes and that these shapes are named s, p, d, and f. They are often then told that subshells and orbitals depend on what period and group 1320

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Figure 2. Photoelectron spectrum of argon illustrating the six ionization energy thresholds of argon and the relative abundance of electrons for each.

students in the construction of a model of intermolecular interaction based on observable heats of vaporization. A popular chemistry textbook26 states that (ref 26, p. 34) The physical properties of a compound are determined by the attractive forces between the individual molecules, called intermolecular forces. It is often difficult to use the molecular structure alone to predict a precise melting point or boiling point for a compound. However, a few simple trends will allow us to compare compounds to each other in a relative way, for example, to predict which compound will boil at a higher temperature. Many other textbooks take a similar approach, defining dipoles, hydrogen bonding, and dispersion forces and then using data, such as boiling points or heats of vaporization, to support the definition. In the Data First approach, students are allowed to explore the heats of vaporization of simple molecules (shown in Table 3) prior to a discussion of intermolecular forces so that they can begin to build their own conceptual understanding of the strength of intermolecular attractions and the type of intermolecular bonding present. This approach is an adaptation of a previously published lesson on inquiry approaches to

are accommodated at this particular level. Analysis of the photoelectron spectrum of argon provided in Figure 2 strengthens this interpretation and helps make yet another pattern in the table of data more obvious. First, the peak height of the various ionization energy thresholds provides a relative measure of the number of electrons associated with each energy. We see that there are exactly three times as many electrons of energies 1.52 and 24.1 MJ/mol as electrons of energies 2.82, 31.5, and 309 MJ/mol. This supports the notion that there are two electrons of identical energy at each of the latter energies, whereas there are six electrons at each of the former energies. Second, the clustering of the peaks reveals a sublevel structure within the atom. For example, the distance separating the 1.52 and 2.82 MJ/mol peaks is relatively small compared to the distances separating them from the remaining peaks. Therefore, it stands to reason that these electrons are more accurately occupying energy sublevels within one shared primary energy level, and likewise for the 24.1 and 31.5 MJ/mol peaks. From these simple observations, and under the guidance of the instructor, students can start to construct knowledge about the nature of the energies of electrons and discover the subshells. The instructor can ask guiding questions, such as “Why do you suppose that lithium atoms have three electrons but only two ionization energies?” or “Do you see any patterns in magnitudes of the ionization energies for sodium atom?” As they evaluate and discuss the data with the instructor and fellow students and follow the readings in the text, students build a conceptual understanding of atomic structure that goes well beyond the memorization of rules or mnemonics typically used in the teaching of electron orbital theory. ̈ ideas held by It is important to note two common naive chemistry students when working with this data. The first is to incorrectly interpret the table as successive ionization energies rather than multiple first ionization energies. The second is that ̈ idea elements have only one first ionization energy. This naive is generated due to the fact that chemists largely focus on only the minimum amount of energy required to remove an electron from an atom, ignoring the other more costly first ionizations possible. Successful analysis of the PES data requires first ̈ ideas. addressing these naive

Table 3. Heats of Vaporization and Total Number of Electrons for Hydrides of Selected Elements in Groups IV− VII Hydrides CH4 SiH4 GeH4 SnH4 NH3 PH3 AsH3 SbH3 H2O H2S H2Se H2Te

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DATA FIRST EXAMPLE 2: ANALYZING HEAT OF VAPORIZATION DATA TO UNDERSTAND INTERMOLECULAR FORCES This example targets AP Chemistry Curriculum Framework Learning Objectives 2.11, 2.13, 1.16, 5.6, and 5.9 by guiding

HF HCl HBr HI 1321

# Electrons Group IV 10 18 36 54 Group V 10 18 36 54 Group VI 10 18 36 54 Group VII 10 18 36 54

Hvap (kJ/mol) 8.2 12 14.1 18.5 23.4 14.6 16.7 21.3 40.7 18.7 19.6 23.7 28.6 16.2 17.6 19.6

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teaching intermolecular forces using boiling point data.27 It is important to note that discussion of heats of vaporization should occur prior to this discussion of intermolecular forces in the course sequence. However, if heats of vaporization have not yet been covered, a similar approach can be used with boiling point data instead. When students graph this data, trends are illuminated. First, students are only given the data for the Group IV hydrides. When they graph the data for the Group IV hydrides against total number of electrons in the molecule, they will find a trend as shown in Figure 3.

and the number of lone pairs available for coordination on their highly electronegative atoms. This line of questioning leads students to an explanation of the relative strength of the hydrogen bonding between NH3, H2O, and HF. Because so much of chemistry is based on developing particulate models that can explain macroscopic observations, it is important that students are provided with an opportunity such as this to use macroscopically observable properties of materials to gain insight into the invisible world of molecular behavior. In addition, the Data First approach allows students to make predictions about the intermolecular forces and properties of new materials that they may encounter.

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DATA FIRST EXAMPLE 3: ANALYZING VAPOR PRESSURE DATA TO ILLUMINATE DYNAMIC EQUILIBRIUM Our final example highlights AP Chemistry Curriculum Framework Learning Objectives 6.1 and 6.3. Equilibrium is one of the most fundamental and pervasive concepts of chemistry and, although students often master the calculations utilizing equilibrium constants, they often struggle to grasp the conceptual understanding of the dynamic nature of chemical and phase equilibria.28−32 The particulate nature of chemistry makes it challenging to “see” that molecules are always in motion and to understand that the constant macroscopic properties of a system at equilibrium are not the result of stationary molecules or static processes in the system. The Data First Approach can be used to help students construct a deeper understanding of chemical processes and the particulate nature of chemistry while also clarifying these and other misconceptions about equilibrium. The two laboratory experiments previously published by the authors33,34 show how students can delve into the concept of equilibrium by generating their own data, observing discrepant events, and using reasoning to reach a deeper understanding of the concept. In one of these experiments,33 students can collect their own data and compare a water liquid−vapor system with a system that contains only air. This exploration of phase equilibrium and vapor pressure only requires the use of a small Erlenmeyer flask, a two-hole rubber stopper fitted with a Luer connection, a pressure gauge, a disposable syringe, wooden dowels of various lengths, and water. Described in detail in the previous paper,33 the water liquid− vapor system is generated by boiling a sample of water in a small Erlenmeyer flask for an amount of time sufficient to drive off the air in the flask, sealing the flask, and then cooling it to room temperature. Pulling back the plunger of the attached syringe in both the liquid−vapor system and in a similar setup containing only air allows students to investigate how changing volume affects the pressure of both systems. An example of typical data collected, shown in Table 4, reveals that the pressure in the liquid−vapor system is relatively independent of the syringe volume, whereas the pressure in the air-only system is highly dependent on the syringe volume or applied pressure. Prior to the experiment, students typically predict that both systems will follow Boyle’s law and, therefore, behave the same. After conducting the experiments, students observe that only the air system obeyed Boyle’s law, whereas the water−vapor system remained surprisingly unaffected by changes in volume. This provides an opportunity for students to discuss the difference between the two systems, questioning why a twophase system should behave any differently from a single-phase system. How can the pressure of a gas not decrease by

Figure 3. Heats of vaporization of Group IV hydrides versus total number of electrons in the molecule.

By analyzing the relationship between number of electrons in the molecule and heat of vaporization, students can establish a relationship between electron count and attractions between molecules. Students can expand on this by using their knowledge of electronegativities, molecular geometries, and molecular polarity developed previously using the Data First approach to explore the differences in polarity between molecules and predict how this might affect intermolecular forces, thus developing a dipole interaction model. They can sketch their predicted graphs for Groups IV through VII hydrides based on their developed dipole interaction model and then compare their predictions to actual supplied data, as in Figure 4.

Figure 4. Heats of vaporization of Groups IV−VII hydrides versus total number of electrons in the molecule.

The resulting graph will likely closely agree with student predictions, thus supporting their dispersion and dipole models, with three very obvious deviations: NH3, H2O, and HF. These anomalies necessitate a revision of the dipole model, and through a teacher-guided discussion, hydrogen bonding can be incorporated to account for NH3, H2O, and HF’s abnormally high heats of vaporization. Furthermore, we can ask students to evaluate the number of hydrogen bonds possible in these three molecules given the number of hydrogens they each contain 1322

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Table 4. Equilibrium Pressure versus Syringe Volume for Two Systems, One Containing a Mixture of Liquid Water and Vapor and One Containing Only Aira Equilibrium Pressure (torr) Time (s)

Syringe Volume (mL)

Water

Air

0 15 30 45 60 75 90 105 120 135

0 14 35 58 35 14 0 58 0 0

25.4 25.0 24.2 23.8 24.3 25.1 25.6 23.9 25.6 25.6

773 704 622 561 622 706 774 562 774 774

a

Reprinted with permission from ref 33. Copyright 2011, American Chemical Society.

increasing the volume while holding temperature constant? Moles of gas must increase. Where does the additional gas come from? What affects the rate of evaporation? Because we did not change the temperature of the liquid and therefore did not affect the rate of evaporation, how can we see an increase in the amount of gas? In this way, students develop a model that illustrates two competing processes (evaporation and condensation) occurring at equal rates. Students can use kinetic molecular theory to explain the phenomena at a particulate level and then extend this knowledge to explain why a substance with weaker intermolecular forces has a greater vapor pressure than one with stronger intermolecular forces.

Figure 5. Response of 39 teachers in Rice’s PD program to the question “I increased my understanding of the following topics”.

Table 5. Analysis of High School Chemistry Teachers’ Performance on Content Pre- and Post-Tests

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USE OF THE DATA FIRST APPROACH IN CHEMISTRY PROFESSIONAL DEVELOPMENT Rice University has been providing professional development in chemistry using the Concept Development approach since 1998 and the Data First approach more recently. Over 300 chemistry teachers have participated in these PD programs, and they consistently report that the programs have helped them increase their understanding of chemistry concepts, as shown in Figure 5 for one cohort of 39 teachers. They also state that the Concept Development Studies book deepened their understanding of chemistry; 95% of our teachers report that the “CDS helped deepen my understanding of Chemistry,” 35% agreeing and 60% strongly agreeing with this statement. This is confirmed by pre- and postconcept tests that have been taken by teachers annually in the program (2012−2013 results are shown). The Chemistry Concept Reasoning Test (CCRT) that was created and validated by Cloonan and Hutchison13 for measuring conceptual understanding and critical scientific thinking of chemistry models and theories was administered to teacher participants before and after the PD. Results show significant difference and improvement from pre- to post-test. As shown in Table 5, participants posted a 21% change from the pretest with p < 0.05. Seventy-four percent of the teachers in the sample showed improvement in the post-test. Another chemistry concept test was administered (Mole Concept test developed in-house) and similarly showed a significant increase of 12% from the pre- to post-test with p < 0.05. Fifty-one percent of the teachers achieved an improvement on their posttest score. All pre- and post-test scores were analyzed using the nonparametric Wilcoxon signed ranks test.

Instrument

N

Pretest Mean (SD)

Post-test Mean (SD)

P

Chemistry Concept and Reasoning test Chemistry Mole Concept test

27

52% (18)

63% (17)

0.001

35

57% (23)

64% (22)

0.30. These results are also summarized in Table 6. Teachers in Rice’s PD programs also report that they are implementing the Data First approach in their upper level chemistry classes on a variety of topics and that they believe that it improves their students’ understanding. In our 2013 end of course survey, 96% of the chemistry teachers (n = 29) in our PD program agreed or strongly agreed that their “participation in the program and use of the lessons has increased the level of science inquiry in my classroom”. Quotes from the end of course anonymous survey include the following: I have used the original data to explain molecular geometries and atomic theory. I have used the ionization energy to explain trends on the periodic table. This helped some of my students make sense of the periodic table. I have tried to introduce new units by first introducing the data. It’s great and challenges critical thinking and keeps the students on task.

ACKNOWLEDGMENTS

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REFERENCES

The authors would like to acknowledge the National Science Foundation (award number EEC-0908968), The Texas Education Agency (award number 136944047110001), and the Houston Independent School District for their support of this work. We would also like to thank Dr. Alice Chow for her technical assistance on this paper.

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CONCLUSIONS It is clear from our years of experience providing chemistry teacher professional development that the Concept Development and Data First approaches help teachers build their own conceptual understanding of chemistry. The Data First approach is also a resource for AP Chemistry teachers because it aligns with the 2013−2014 AP Chemistry Curriculum Framework’s deep focus on conceptual understanding, critical reasoning skills, and scientific practices. Rather than using data to solve problems in chemistry through deductive reasoning after the concept has been taught to students, the Data First approach provides students with a platform on which they can use data from experiments and observations to construct chemical concepts using inductive reasoning strategies. This repurposing of the data traditionally used in instruction often only requires teachers to reorder rather than completely overhaul their instruction and, thus, provides a reasonable starting point for the busy teacher into the reform practices of inquiry teaching. Not only does this approach align with the AP framework but it also emulates the way that science is conducted. Further studies will evaluate how students whose teachers use the Data First approach perform on the AP chemistry exam.

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AUTHOR INFORMATION

Corresponding Author

*J. S. Hutchinson. E-mail: [email protected]. Notes

The authors declare no competing financial interest. 1324

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