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Chapter 8
Designing an Active Learning Physical Chemistry Course Using Best Practices Jodye I. Selco* Center for Excellence in Mathematics and Science Teaching (CEMaST), Cal Poly Pomona, 3801 W. Temple Ave., Pomona, California 91768, United States *E-mail:
[email protected] This chapter describes both the best practices for curriculum design and how a Physical Chemistry course was designed on those principles to maximize student engagement and learning. The resulting course inquiry based modules are data heavy and constructed to improve student abilities to analyze data, think quantitatively, work in groups, and be metacognitive about their learning. Using “How do we know that?” as the course theme, students discover the relationships presented in texts instead of being told about these relationships. The computer based course activities are modeled upon POGIL activities, but are more heavily focused on data analysis than equation derivation. Student response has been overwhelmingly positive, and students report that the course has achieved the stated goals the course was designed to accomplish.
You know you want a more active learning environment in your Physical Chemistry course, but you don’t know where to begin. There are a few examples in the literature of active learning models for physical chemistry topics (1–6), some in engineering that focus on thermodynamics (7–9), and some that detail active learning pedagogical ideas (10–17). An excellent article by Bressette (18) describes a journey from a good but unsatisfied organic chemistry teacher, to one who convinces himself and others that engaged students learn more using Process Oriented Guided Inquiry Learning (POGIL). Bressette’s changes were accompanied by all the “right” moves for a © 2018 American Chemical Society Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
researcher - read the literature, attend sessions at conferences to learn from those intellectually engaged in the research, and then deciding to skeptically try a new teaching method (POGIL in this case) while examining the evidence for student achievement. There are a variety of student centered or active learning strategies; the review article by Prince and Felder (11) provides descriptions of these strategies. Included are Inquiry (guided to open), Discovery Learning, Problem Based Learning, Project Based Learning, Case Based, and Just in Time Teaching. Dissatisfied by student learning in Physical Chemistry, I have used a variety of these active learning strategies over the years (19), but have not used any one consistently for an entire course. Over the years, teaching Physical Chemistry as mathematical models developed to explain how the world around us worked has become second nature (19). There have been many student questions about physical chemistry, but one that seemed important (and almost unanswerable - at least by examining a textbook) was “How do we know that?” not just the typical “What do we know?” This seemed like an important idea to focus on and use as a theme for a course at some future date. Having been assigned to teach a one term (10 weeks) physical chemistry course for non-majors whose only prerequisite was the full year of General Chemistry (there is no mathematics prerequisite) it was clear that something besides a “standard” Physical Chemistry course would be needed if student learning was to be maximized. At Cal Poly Pomona, the Department of Chemistry and Biochemistry requires that students take at least one course in each subfield of chemistry in order to earn a minor (which is not required). A variety of Science, Technology, Engineering, and Mathematics (STEM) majors (primarily pre-health, biotechnology, and chemical engineers) take the course since these majors require many chemistry courses, and this course allows them to complete a chemistry minor. The idea of redesigning an entire course as an active learning experience was daunting! After multiple conversations with colleagues about the content required in the course and/or the pedagogies used, it became clear that should this course be redesigned, there would be freedom to use the best practices of teaching and learning as the guide without any repercussions from colleagues or the department. The course had to contain physical chemistry content, but the explicit content was undefined. Conversations with STEM colleagues indicated that if the course could improve data analysis skills - preferably assisted by a computer - the course would be quite useful to the students (and my colleagues). The most effective instruction is intentionally designed (Understanding by Design (20)) to 1) have students think about what they already know (activate prior learning), 2) expose students to new ideas or discrepant events and have them integrate new information into what they already know (integrate new information into existing schema), and 3) then have students think about what they learned and how they learned it (be metacognitive about their learning) (20–22). To accomplish this, there are four planning stages: Stage 1: decide what the students should come out of the class being able to do (the learning goals of the course); Stage 2: identify any constraints that need to be accounted for or addressed; Stage 3: identify the assessment evidence (what will be used to assess 116 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
student learning); and Stage 4: plan the instruction (how will students gather and examine evidence) so that students can succeed on your assessment.
Designing a Physical Chemistry Course Using Understanding by Design Principles So, what might this curriculum design process look like for a Physical Chemistry course? First came the realization that these students would never be “functioning physical chemists”; even undergraduate chemistry majors who take a year-long sequence of Physical Chemistry do not graduate as functioning physical chemists. However, one goal was that the students would gain an appreciation of what physical chemists did, how they did this, and some of the ways they think that are different from other chemists. It is clear that you become an expert at something by immersing yourself deeply (23), doing physical chemistry, thinking about physical chemistry, and talking about/teaching others (14, 24) physical chemistry. Although the students would not be able to fully immerse themselves in the subject, was there an aspect of the course into which we could take a deep dive? If so, how might this satisfy all of the learning and pedagogical objectives?
Stage 1: Identify Desired Results • • • • •
•
•
Students will understand what physical chemistry is, what physical chemists do, and how they think about solving problems Improve data analysis skills, particularly with a computer Improve student ability to think quantitatively and mathematically Improve students’ ability to connect mathematical models (equations) to the described behavior Try to examine topics in thermodynamics, kinetics, and quantum chemistry/spectroscopy (Could an entire quantum/spectroscopy unit be taught with “toys”?) Examine a few Physical Chemistry content topics deeply, acknowledging the chemistry background of students in the course (did these students need to know about topics not seen in General Chemistry, or was using “new eyes” on old topics sufficient?) (25) Active learning in groups to improve critical thinking, communication, and problem solving skills (14, 21, 22, 24)
Stage 2: Identify Constraints • •
•
10 week course No college level mathematics prerequisite, but three years of college preparatory mathematics is required for admission (e.g. Mathematics 3 or Algebra II) Students may have had no chemistry beyond General Chemistry 117 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
• •
Use Excel, not Mathematica or Maple, because Excel (or an equivalent) is available on essentially all computers Focus on most appropriate content for the audience with familiar contexts
To build on prior knowledge, a decision was made to focus most of the course on topics already explored in General Chemistry. This is not difficult, but to be an authentic Physical Chemistry course of some sort, these topics needed to be examined with “new eyes” - at a deeper level than is done in a General Chemistry course. The topics included gases, heat capacity, heat, enthalpy, entropy, Gibbs energy, chemical equilibria, colligative properties, kinetics (rates and temperature dependence), and quantum numbers of electrons in atoms and perhaps light and matter interactions. All of these topics are presented in General Chemistry with a surface level of understanding, but in a Physical Chemistry course the depth of understanding is deeper and more mathematical. Since one of the major goals was to design instruction to maximize learning, the POGIL activities in physical chemistry were considered for implementation (26). While intriguing, the level of mathematics they contained was above the level required of the students in the non-majors course. None of the other active learning pedagogies previously published seemed to fit all of the course goals either. If the philosophy of POGIL activities (2, 21) was to be used, it was clear that new ones needed to be designed. These newly designed activities should be data heavy and mathematics light, something that the existing POGIL activities were not (26). Students tend to solve problems algorithmically (look for the “right” equation to use) without understanding why that particular equation does or does not model the correct phenomenon (22). For anyone that has taught a course that includes mathematical equations, this is a very familiar student behavior. Could there be a way to deepen student understanding about how these equations arose, why they are the way they are, and why they only describe specific situations/phenomena? Could students come to understand that the field of physical chemistry grew out of a need to model the behavior of chemical systems in the world around us, presumably to optimize the desired results? Perhaps “How do we know that?” could be used as the course’s theme. The use of themes keeps the students concentrated on the “big picture”, helps them organize new information into a given schema, and motivates them to assimilate what they are learning into a larger framework (27).
Stage 3: Identify Assessment Evidence How will students demonstrate that they can apply what they have learned to solving new problems? The construction of the course is aligned with the three-dimensional learning set out in the Next Generation Science Standards (28) and A Framework for K-12 Science Education (29). In particular, the course focuses on the Science and Engineering Practices (how to do science) of Developing and Using Models; Using Mathematical and Computational Thinking; Analyzing and Interpreting Data; and Engaging in Argument from Evidence. The primary Crosscutting Concepts (ways of organizing thinking 118 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
about science) are Scale, Proportion, and Quantity; Systems and System Models; and Energy and Matter: Flows, Cycles, and Conservation. These practices and concepts are used to examine the data presented in the course and to have the students explain what the data means. If the course focuses on having students learn using three-dimensional learning, then the assessments should align with this way of learning as well (30, 31). Although multiple choice questions on assessments are easier to grade, these types of exams do not necessarily allow students to demonstrate everything they have learned – even when the questions are constructed in an appropriate way (32). Since the goal of the course is to have students “doing” physical chemistry, then this is what they should be demonstrating they can do on the assessments. This does not mean that every question in each assessment has to involve data analysis or look exactly like one of the learning modules, but students should be using the skills and knowledge learned in the course to demonstrate what they know. As a result, the exams for this course are data heavy; the questions ask students to build and use mathematical models, analyze and interpret the data, and explain what the results might mean. Since these types of questions require a computer and thinking time, the exams are untimed take-home exams that ask questions related to, but not identical to ones they have already encountered. One example might be to ask them to evaluate real gas equations as models for provided data after they have examined gas data at high pressures. Another example might be that students are asked to decide how often medication needs to be taken to keep the blood concentration of a drug above a specified level after they have already built a computer model of how medicine gets into and eliminated from the blood stream during a prior assignment. For each topic taught, the assessment question, task, or data set needs to be decided upon first so that instruction leads the students to be successful on the assessment task. Although you may not use every assessment question/task in each course, students need to be taught all of the skills prior to being assessed.
Stage 4: Plan Learning Experiences and Instruction Practicing physical chemists are taught to look for patterns and relationships between and amongst data. Lots of practice improves our expertise in doing this. Could this idea be a way to accomplish the course goals if the course and assignments were designed correctly? Then a realization hit - most of the fundamental thermodynamic relationships could be explored in this way, and the students could answer the thematic question of “How do we know that?” If the constant pressure heat capacity at different temperatures was known, how the heat capacity changed with temperature could be modeled. How heat, enthalpy, and entropy change with temperature could be determined from the temperature dependent heat capacity. From the enthalpy and entropy at a variety of temperatures, the molecular Gibbs energies, the Gibbs energy change for a chemical reaction, and the equilibrium constant of a reaction at each temperature could be determined. To produce the data sets for this extended thermodynamics unit, all that would be needed was a data set of heat capacities (preferably as a 119 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
function of temperature or at different temperatures - this is a physical chemistry course after all) from which data sets could be synthesized. Fortunately, this information is available from National Institutes of Standards and Technology (NIST) through its Chemistry WebBook (33). Physical chemists like to examine gas phase phenomena because they are simpler to model. However, the focus in this course should not be entirely on gas phase phenomena since most of these students work almost exclusively with condensed phases. With the course lasting only 10 weeks, many of the typical Physical Chemistry topics (34) cannot be covered. Therefore, the topics focused on should be the basics so they could be examined in depth for understanding. This led to a realization that the focus could be solely on constant pressure thermodynamic phenomena that might be encountered by the student population. (The course includes a disclaimer that there are other variables that could be held constant, and that this changes the relationships.) See Table 1 for a list of course topics and discoveries.
Course Topics and Content Using the NIST (33) equations for how the heat capacity changes with temperature, values of the constant pressure molar heat capacity within the range specified by NIST were generated. Interestingly, NIST has two sets of coefficients for the same equation within two different temperature ranges (with an overlapping temperature), a strategy that was used in the course to model the data. This allowed the values of CP at different temperatures to be synthesized for gas phase CH4, CO2, O2, and H2O over the range of temperatures modeled by NIST. Of course, this also allowed the data for q, ∆H, ∆S, ∆G, and Keq to be synthesized. Out of all of the active learning strategies I’ve used over the years, inquiry or discovery learning (11) is preferred from personal experience due to the high level of learning by students. Could scaffolded exercises be designed that had students arrive at a mathematical model of the behavior of the constant pressure heat capacity data and then figure out how the heat was related to that? If so, then the course exercises could be designed to have students answer “How do we know this?”. Modeling the heat capacity data as a function of temperature should be straight forward using Excel - despite the data being non-linear. Figure 1 shows that the graph of CP as a function of temperature is not linear. The equation of best fit (in this case) is a sixth order polynomial; the students are asked to convert this “generic” equation into one that describes how the constant pressure heat capacity depends on temperature. Translating the “Excel” equation into a model of the heat capacity yields
with R² = 0.9996, where T is the temperature in Kelvin, and CP is in J/mol·K,in this case. Writing this equation with CP as a function of T makes clearer to students that the constant pressure heat capacity changes with temperature, and not in a linear way. 120 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
Table 1. Topics presented in the course and the main learning objective of the module. Topic
Discovery (Learning Objectives)
Real Gases
High temperature, low pressure gases behave most ideally
Heat Capacity
Heat capacity changes with temperature and can be modeled
Heat
How heat and heat capacity are related
Enthalpy
How enthalpy, heat, and heat capacity are related
Entropy
How entropy and heat capacity are related
Gibbs Energy
How enthalpy and entropy are related to the Gibbs Energy
Vapor Pressure
How the vapor pressure changes with temperature (Clausius-Claperyon Equation)
Colligative Properties
Why Vapor Pressure Lowering and Raoult’s Law are expressed in terms of mole fractions; why Henry’s Law is expressed in terms of the pressure of the gas; why Boiling Point Elevation, Freezing Point Depression, and Osmotic Pressure are expressed in terms of a solution concentration
Chemical Equilibrium
How an equilibrium constant and the Gibbs energy are related
Kinetics
How the order of a reaction changes the time dependent behavior; how temperature affects reaction rates and the rate constant; and modeling time dependent behavior of species involved in consecutive reactions
Light and Matter Interactions
Observed colors are reflected light; how color and chemical structure are related (particle in a 1-D box); and what happens during fluorescence, phosphorescence, photochromism, and thermochromism
In modeling the data, students are asked to apply Occam’s Razor (35) to the equations they discover to find the simplest description of the relationship by considering both the value of R2 and how well the curve fits the data points themselves. For the equation above, it might be that a lower order polynomial equation might fit the data as well or almost as well as the sixth order equation above; students must evaluate both the value of R2 as well as whether the fitted curve line best models the points (and the visually interpolated ones between the data points) to decide upon the best fit. There are two major challenges for this course: the students were not required to have taken calculus (or any college level mathematics) prior to taking this course, and they may not have had any chemistry beyond General Chemistry. The lack of additional chemistry can be overcome by structuring the introductions to topics so that they present the students with a review of the content at the General Chemistry level. The lack of mathematics preparation is more problematic. Since Physical Chemistry is calculus based, the necessary mathematics required should be as simple as possible, and presented to the students in the simplest way possible. 121 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
Figure 1. The molar heat capacity at constant pressure for methane changes non-linearly as the temperature changes over a wide range. The Excel trendline equation for the best fit function is a sixth order polynomial, which is used to determine how heat capacity an other properties (e.g. heat) are related. To successfully model how the correct function of CP is related to heat, enthalpy, and entropy, students need to integrate the mathematical function of heat capacity in their model. This exercise needed to be structured in such a way as to make it accessible to all of the students, regardless of their mathematics background. The explanation presented is that integration is a “mathematical function” that often reveals relationships between quantities (e.g. a curve and the area under that curve). Since the constant pressure heat capacity trendlines of best fit are all polynomials, the integration of the four forms shown in Equation 1 was done for the students (sum of individually integrated polynomial terms), and the students only would need to program in a sum of integrated polynomial terms of the heat capacity. This enables them to determine relationships between the constant pressure heat capacity and heat, enthalpy, and entropy. For instance, once students have an adequate polynomial equation to model the heat capacity data as a function of temperature, they calculate four integrated functional forms of the heat capacity equation:
(Not all of the initial temperatures were 298 K; it is used here as an example.) Students were then provided with synthesized data for q, ∆H, and ∆S and asked (one at a time) to determine the relationship between CP and the new quantity. This approach turned out to have many advantages for data analysis, quantitative thinking, and converting mathematical equations into mathematical 122 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
models of how something behaves. In examining Figure 1, it is clear that the data is not linear, but it might be over a small temperature range. This led to a rich discussion about when to use
In Figure 2, the heat capacity data for O2 gas is presented. It is clear that no single polynomial function - even a sixth order one - is sufficient to model all of the details in the data, especially at low temperatures. This provided a chance to discuss modeling the high and low temperature data using two different sets of coefficients (just like NIST) to get a better model.
Figure 2. The molar heat capacity at constant pressure for oxygen changes non-linearly as the temperature changes over a wide range. The Excel trendline equation for the best fit function is a third order polynomial. Note that the line of the equation does not model the low temperature data points well.
The assignment involving ∆G and KP asks students to graph the data in such a way as to find a linear relationship between ∆G and KP, and then examine the slope of the line. This exercise is scaffolded during the separate calculation of ∆G and the examination of the Gibbs energy data set. Many students comment that they are shocked to discover that the slope of the line for the graph of ∆G/T vs. ln(KP) (see Figure 3) has the value of -R (or -1/R depending on which quantity is graphed on the y-axis). In examining the chemical equilibrium data, the students had to consider whether the data contained outliers or whether some other “strange” mathematical function such as a logarithmic function was required. 123 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
Figure 3. In order to obtain a linear relationship between the Gibbs Energy and the equilibrium constant for the combustion of methane, ∆G/T vs. ln(KP) need to be graphed. Note that the slope of the line is the negative value of R in kJ/mol. The course also examines non-ideal gases. The Matheson Gas Book (36) contains data for many gases - usually a set of P vs. V/n and P vs. Z data. Students were provided with data sets for four gases and for the P vs. V/n data, asked to calculate Z as well as graph the data. They were asked to calculate V/n from the P vs. Z data as well and compare the two data sets for each gas and speculate about why some gases behave more ideally than others and in what general pressure range. Other thermodynamic topics include liquid-vapor equilibria (pure and binary liquids) and colligative properties (why molarity or molality is used instead of moles or grams to model this behavior). In these modules, synthesized data was provided to students; they created a mathematical model of the behavior, and were asked to explain how the model represented something about how the world around us worked or could be described. In the kinetics section, students analyze data to determine the reaction order of three different reactions. One of the data sets used is the elimination of ethanol from the blood in human bodies; this data set is clearly zeroth order and not dependent upon the blood alcohol concentration (see Figure 4). This provides a chance to discuss why this reaction is zeroth order (the enzyme concentration is so low that it appears to be zeroth order in ethanol) and what the implications are to the drinker. During the kinetics unit, the students also examine the only “real” data set used in the course - the decomposition of Vitamin C in citrus juice concentrates (37). By the time the students begin to examine this data set, about 75% of the way through the course, they have realized that the data they have been seeing is idealized or synthesized. Their first clue is when they calculate the standard deviation between the calculated and “actual” values for both q and ∆H because the standard deviations are identical (assuming that their calculation was 124 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
the same - not even necessarily correct). If students ask, they are told that the data was created for them and was not “real” (measured).
Figure 4. Determination of the kinetic order for the blood concentration of ethanol. Because the concentration of ethanol vs. time is linear, this reaction is zeroth order.
However, the real data set has noise in it and serves to foster a discussion about the need to consider a “global” solution to the question about the kinetics of the decomposition of Vitamin C. Assuming that the mechanism for the decomposition is the same across all citrus juice concentrates, the kinetics should be the same. This means that although some data appears to be second order, if first order kinetics describes most of the data provided, this is probably the correct model for the behavior. Students often comment about how glad they are that they have worked with idealized data to begin with since the real data is so messy. Physical Chemistry textbooks tend to present “perfect” data, but do not necessarily discuss what you need to know/do/decide when working with “real” data that is messy and noisy. This data set also examines the kinetics of the decomposition of Vitamin C at multiple temperatures and provides a way to examine the Arrhenius equation. The final kinetics modeling exercise is to examine a drug overdose situation (38). Students use Excel and Euler’s “step-ahead” method to calculate the concentration of a drug in the GI tract and bloodstream (and can calculate the amount eliminated in this consecutive reaction scheme) to determine whether a child has overdosed on medication. Using this information, they have to act as 125 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
physicians to decide if they have to treat the child, and how they will treat the child if their life is in danger. Although most modern physical chemistry research is quantum mechanical and/or spectroscopic in nature, it did not seem reasonable to focus the entire course on this area. Instead, the phenomena examined via a case study is the absorption of light by large organic materials, whether they re-emit that light through fluorescence or phosphorescence, and how photochromism and thermochromism work. This unit expands upon a lesson originally designed by Gwen Shusterman (39). Students first examine the chemical structures of 12 compounds and guess whether they are colored or not, then they examine the wavelength of maximum absorption and try to determine which aspect of the molecular structure corresponds to the wavelength of maximum absorption, λmax, (and in what way). The students graph λmax vs. number of conjugated double bonds to discover that while there is a relationship between these two quantities, there is scatter in the data. However, they still can use it to predict whether the wavelength of maximum absorption will be longer or shorter than others for which they have data. This leads to the examination of the particle in a 1-D box model for conjugated hydrocarbons.
Course Structure In all of the modules, there are “critical thinking questions” that ask students to describe what they are “seeing” in the data as well as what this might mean (e.g. which gas behaves most ideally and why this might be the case, or what reaction order describes the kinetics of the decomposition of Vitamin C in citrus juice, and what recommendations they have for reducing the rate of decomposition after they have examined the temperature dependent data). Each module is designed so that the students graph and model the data individually, but need to discuss with the group members the meaning of the model they developed. As a group they provide answers to the questions asked in the assignment. Then students write an individual journal entry to explain what they did for their part of the group assignment, how the group functioned, and what they learned (remember the metacognition requirement?). All of these are uploaded into our Learning Management System (LMS - Blackboard at Cal Poly Pomona). Each of these portions of the modeling exercise is purposefully designed to 1) teach students a new Excel/data analysis skill, 2) have students collaborate and have to explain to others the data processing and meaning they have made from the data analysis, 3) have students use technical language to explain what they did and the meaning they made from the exercise, and 4) to metacognate about their experiences. After the work from the students is due, the solutions appear in the LMS along with applications of that topic and a solved problem (with discussion about how to solve it). From the faculty side, having individuals process the data holds them accountable for the learning. Having students collaborate to make meaning of the models increases the “correctness” of their responses and reduces the faculty grading load, yet still allows for individual grades within the group project since 126 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
students detail what they did (and how the group functioned) on the group portion of the project in their journal entry. The group memberships are changed multiple times during the term (about 4 or 5 times) so that the students get to work with all of the others in the class. Felder (10) details ideas for creating groups; I also try to avoid putting all of the “flaky” students in a single group so there is always someone in class for each student to collaborate with. Each assignment receives commentary about something - even if it just lets students know they did a nice job on the assignment.
Student Response Because students are interacting with each other and with the faculty member "in class" during the "lecture" portion of the course, everyone involved gets to know each other well. My rapport with the students in this course is quite different than in other courses I have taught. The students become so comfortable that they often forget that I’m a faculty member. The students tell me that they think of me more as a learning guide than a “professor”. This is consistent with what Bressette (18) experienced. The student responses to teaching with these modeling modules have been quite positive. Many have commented that although the campus philosophy is “learn by doing”, this was the first course where they actually learned by doing and were quite happy about the experience. The students even get so excited about my collection of teaching toys (e.g. photochromic nail polish), that they bring me new samples to teach with or send me links to videos and products that I can use in the course. This indicates that the students understand the course content enough to identify (and share) additional applications they find in “the real world”. When surveyed about their thoughts on the course structure, one student wrote, I feel personally the responsibility that you ask of us and the level of synthesis that you require us to have. It is a great challenge to function at such a high level for me, that I feel frustrated and even to the point of giving up...but I know that that means you have taken me to my breaking point, and that is where true learning blossoms. This course is very challenging for me, and in a way I like that you have served for us the structure of this class in a very free way. I like that I am able to do most of the work by myself on the computer, but it is intimidating in a room of my peers who are doing the same thing when we are in class that I freeze up. I like that you are teaching us to be computer literate while calculating so much chemistry. I think though that maybe a mini lecture about the equations/theory would be a little helpful before we start a new module (unless the whole point is for us to discover the theories on our own…which is difficult but not impossible, just difficult). It is clear that this student has never seen a course specifically designed to provide an overview of the content, access prior learning in a direct way, scaffold 127 Teague and Gardner; Engaging Students in Physical Chemistry ACS Symposium Series; American Chemical Society: Washington, DC, 2018.
the learning to help students succeed, and learn by doing. This comment also seems to provide evidence that the major goals of the course were accomplished at least from the perspective of the student. This student is clearly metacognitive, and has been continually thinking about their learning process and how deeply they came to understand the content. They realize that they are being challenged to discover things themselves, even if lecture may be an easier way to disseminate the equations/theories. Although this particular student is notably metacognitive many other student comments are similar. In polling the students about the structure of the course, the most frequent positive comments are about #1 computer literacy, #2 interactions with instructor, #3 deep learning, #4 instructor flexibility, #5 time to synthesize material (between Excel modeling and due date), #6 on-line submission of assignments, and #7 real world applications. Students would also like to see more applications, better integration of the Excel models, homework, and exam problems as well as more lecture and problem solving on the board. Student feedback has been so positive about this course, that the modeling exercises also were used in a two term biochemistry majors course. In this case, the modules were not the primary mode of instruction, but supplemented more traditional instruction. The responses of these students to the course structure were similar to those of the non-majors. They indicated that they really learned the material presented when there was a modeling exercise for that content. These students also share examples of the real world examples they find that match the content of the modules or provide suggestions for additional modules. Knowing the reputation of Physical Chemistry courses, I’m always astounded when students stop me on campus to tell me excitedly that they are so glad they managed to get a spot in my Physical Chemistry course and are looking forward to the new term starting.
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