Active learning in BIS2A
In every lecture, we will ask you to answer questions, either in a small group or individually. These questions serve several purposes:
Functions of in-class questions
Questions stimulate students to examine a topic from a different perspective, one that the instructor considers relevant to student learning.
Questions act as mini "self-tests" for students. If you are uncertain about what question is being asked or how to answer it, this is a good time to (a) ask the instructor for clarification and/or (b) take note to review this topic immediately after class with a TA, the instructor, classmates, or the internet. If the instructor took the time to ask you the question in class, this is a big clue that they think that the topic and both the question and the answer are important.
Some in-class questions will ask students to formulate questions themselves. This is typically an exercise that is designed to force the student to reflect on and try to articulate the point of the lesson. These are critical exercises that force you to think more deeply about a topic and to place it in the broader context of the course.
Some questions may ask the student to interpret data or to create a model (e.g., perhaps a picture) and to communicate what they see to the class. This exercise asks the student to practice explaining something out loud. This can be a great self-test and learning experience, both for the person answering and fellow students who should also be using the time to examine how they would have answered the question and how that compares with the feedback of the instructor.
Questions in the discussion that follows and the thought process involved in solving a problem or answering the questions are opportunities for the instructor to model expert behavior in an interactive way—sometimes it is equally important to understand HOW we arrive at an answer as it is to understand the answer.
Some questions are designed to stimulate thought and discussion rather than to elicit a discrete answer. If called on, you should not feel compelled to have one "right" answer!! Understanding this is very important. Once you realize that it is perfectly acceptable (and sometimes desirable) to not know all of the answers (if you did, what would be the point of coming to class?), it can take away a lot of the anxiety of getting called on. While it is okay to not know "the answer", it is nevertheless important for you to attempt to make a contribution to the discussion. Examples of other meaningful contributions might include: asking for clarification; associating the question with another class topic (trying to make connections); and expressing what you are comfortable with and what confuses you about the question. Don't be afraid to say "I don't know". That's perfectly okay and even expected sometimes. Be prepared for the instructor to follow up with a different question, however, that will try to either highlight something that you likely do know or to ask for your help with identifying a point of confusion.
Getting ready for lecture
To help you get ready for each lecture, we provide study guides that include instructions on how to prepare for class. You should do your best to complete the assigned reading and suggested "self-assessments" before coming to class. This will ensure that you are ready for discussions and that you can make the most of your time during class. We do not expect you to be an expert before lecture, but we do expect you to do the pre-reading and by doing so make yourself familiar with the required vocabulary and spend some time thinking about the concepts that will be discussed. We will build on that basic knowledge in lecture. If you do not have at least some of the basic building blocks before hand, you will make less efficient use of your time in class.
We cannot emphasize too strongly that YOU have the primary responsibility for learning the material in this (or any other) course. Although we are invested in your success, your instructors and TAs cannot magically implant knowledge. Like any other discipline that requires mastery (e.g., sports, music, dance, etc.), we can help guide you and critique your performance, but we can not replace the hours of practice necessary to become good at something. You would never expect to become a proficient pianist by going to lessons once or twice a week and never practicing. To most of us, it seems self-evident that you need practice to become good at something like music, art, or sports. It should not be surprising that the same rule applies with learning biology or any other academic subject.
We see ourselves as your coaches for this class; we want all of you to succeed. However, for this to happen, you have to take your practice seriously. This means coming to class prepared, participating in class, studying the material covered in class as soon as possible, identifying where you are uncertain and getting help to clarify those topics as soon as possible, and trying to make thoughtful contributions to the online discussions (not just the bare minimum required to "get the points").
Bottom line: you need to be active participants in your learning.
Knowledge and Learning
Teaching and Learning Science
Teaching and learning science are both challenging endeavors. As instructors, we need to communicate complex, highly interconnected concepts that will serve as a foundation for your future studies. We also want our students to demonstrate mastery of these ideas at a high level. As students, you need to learn a large new vocabulary, create mental models on which you can "hang" new conceptual knowledge, and demonstrate that you can actually use this new knowledge. The process challenges both the instructor and the student. Although the process involves hard work, it can also be incredibly rewarding and fun. There is nothing more satisfying for an instructor than those “Aha!” moments when a student suddenly understands an important concept.
In BIS2A we face some interesting teaching and learning challenges. One key challenge is that we discuss physical things and ideas that exist or happen on time and/or size scales that are not familiar to most students. What does this mean? Consider the following example:
Example: Some challenges associated with creating mental models
An instructor teaching wildlife biology may want to talk about concepts in evolution by using bird beaks as a starting point for discussion. In this case, the instructor does not need to spend time creating mental pictures of different shaped bird beaks (or at the very least only needs to show one image); most students will readily draw on their past knowledge and everyday lives to create mental pictures of duck, eagle, or wood pecker beaks and infer the different functional reasons why Nature might have selected different shapes. As a consequence, the students will not need to expend any mental effort imagining what the beaks look like and can instead focus all of their energies on the core evolutionary lesson.
More colloquially: If you are asked to think about something new that is closely related to something you already know well, it is not too difficult to focus on the new material.
By contrast, in BIS2A we ask students to think about and discuss things that happen on the atomic, molecular and cellular scales and at rates that span microseconds to millennia. Most students, we will guess, have not lived life on the micro to nanometer scale. Yet, this length scale is where most of the events common to all biological systems takes place. Beginning students, who have not thought much about how things happen at the molecular scale, lack mental models upon which to add new information. This starting point places a burden on both the student and the instructors to create and reinforce NEW mental models for many of the things we talk about in class. For instance, to really talk about how proteins function, we first need to develop a common set of models and vocabulary for representing molecules at the atomic and molecular levels. Not only do these models need to find ways of representing the molecule’s structure, but the models must also contain abstract ideas about the chemical properties of molecules and how these molecules interact. Therefore, students in BIS2A need to put some effort into constructing mental models of what proteins "look" like and how they behave at the molecular scale. Since the entire course centers around biomolecules and processes that happen at a microscopic scale, a similar argument can be made for nearly every topic in the class.
Note: Possible Discussion
How do you interpret the term mental model and why do you think that it is important for learning?
Some of the in-class and study guide exercises are designed to help with meeting this challenge; most students have found them very useful. However, some students are more accustomed to studying for exams by memorizing information rather than understanding it. (It's not their fault; that's what they were asked to do in the past). As a result, if the problems are approached with the "memorize-at-all-costs" attitude some of the BIS2A exercises may initially seem pointless. For instance, why are your instructors asking you to repeatedly draw some of the concepts described in class? What multiple-choice question could that exercise possibly prepare you for? While it is true that some of your instructors won't ask you to draw complicated figures on an exam, these drawing exercises are not trying to prepare students for one specific question. Rather the instructor is trying to encourage you to begin creating a mental model for yourself and to practice using it. The act of drawing can also serves as a "self test." When you force yourself to write something down or to create a picture describing a process on paper, you will be able to independently assess how strong your conceptual grasp of a topic really is by seeing how easy or hard it was to put your mental image of something onto paper. If it is hard for you to draw a core concept or process from class WITHOUT EXTERNAL ASSISTANCE, it is likely that you need more practice. If it is easy, you are ready to add new information to your model. Throughout the course, you will continue to add new information to your mental model or to use the concept represented in your mental model in a new context. Keep your drawings - or other self-testing mechanisms - current. Don't fall behind.
Incidentally, the presentation of a course concept on an exam in a context that the student has never seen before is NOT an evil plot by the instructor. Rather it is a way for the instructor and student to assess whether the concept has been learned and whether that knowledge can be used/transferred by the student outside of the specific example given in class or in the reading. Asking the student to repeat the latter would represent an exercise in memorization and would not be an assessment of valuable learning and independent thinking or a representation of what happens in real life.
The idea that students in BIS2A will be tested on their ability to USE concepts in specific contexts that they haven't seen before is critical to understand! Take special heed of this knowledge. Developing usable conceptual knowledge takes more discipline and work than memorizing. The quarter also moves VERY fast and concepts are layered one on top of the other. If you get too far behind, it is very, very difficult to make up for lost time two or three days before an exam. Be as disciplined as you can and keep up with course materials.
So, some concepts are hard to teach and to understand. What are we to do? Something instructors and students both do is to use various communication tricks to simplify or make abstract ideas more relatable. We use tools like analogies or simplified models (more on the importance of these shortly) to describe complex ideas. Making things more relatable can take various forms. Instructors might try to use various simlies or metaphors to take advantage of mental pictures or conceptual models that students already have (drawn from everyday life) to explain something new. For instance, the thing X that you don't understand works a little like thing Y that you do understand. Sometimes, this helps ground a discussion. Another thing you might catch an instructor or student doing is anthropomorphizing the behaviors of physical things that are unfamiliar. For example we might say molecule A “wants" to interact with molecule B to simplify the more correct but more complex description of the chemical energetics involved in the interaction between molecules A and B. Anthropomorphisms can be useful because, like similes and metaphors, they attempt to link the creation of new ideas and mental models to concepts that already exist in the student's brain.
While these tools can be great and effective they nevertheless need to be used carefully - by both the instructor and the student. The main risk associated with these simplifying tools is that they can create conceptual connections that shouldn't exist, that lead to unintended misconceptions, or that makes it more difficult to connect a new concept. So while these tools are valid, we - students and instructors - also need to be vigilant about understanding the limits these tools have in our ability to learn new ideas. If these pedagogical tools are useful but their use also carries risk, how do we proceed?
The remedy has two parts:
- Recognize when one of these "simplifying" tools is being used and
- Try to determine where the specific analogy, metaphor etc. works and where it fails conceptually.
The second instruction is the most difficult and may prove challenging for learners, particularly when they are first exposed to a new concept. However, the act of simply thinking about the potential problems associated with an analogy or model is an important metacognitive exercise that will help students learn. In BIS2A your instructors will occasionally expect you to explicitly recognize the use of these pedagogical tools and to explain the trade-offs associated with their use. Your instructors will also help you with this by explicitly pointing out examples or prodding you to recognize a potential issue.
Note: Possible Discussion
Can you give an example from your previous classes where an instructor has used an anthropomorphism to describe a nonhuman thing? What were/are the trade-offs of the description (i.e. why did the description work and what were its limitations)?
It is also worth noting another problematic issue that can needlessly confound students just starting out in a discipline - the use of vocabulary terms that potentially have multiple definitions and/or the incorrect use of vocabulary terms that have strict definitions. While this is not a problem unique to biology, it is nevertheless important to recognize that it occurs. We can draw from real-life examples to get a better sense of this issue. For instance, when we say something like "I drove to the store", a couple of things are reasonably expected to be immediately understood. We don't need to say "I sat in and controlled a four-wheeled, enclosed platform, that is powered by the combustion of fossil fuel to a building that collects goods I want to obtain and can do so by exchanging fungible currency for said goods" to convey the core of our message. The downside to using the terms "drove" and "store" is that we have potentially lost important details about what really happened. Perhaps the car is battery powered and that is important to understanding some detail of the story that follows (particularly if that part of the story involves calling a tow truck driver to pick you up after the car has broken down). Perhaps knowing the specific store is important for understanding context. Sometimes those details don't matter, but sometimes if they aren’t known it can lead to confusion. Using vocabulary correctly and being careful about word choice is important. Knowing when to simplify and when to give extra detail is also key.
In the laboratory, undergraduate students in biology will often report back to their mentors that "my experiment worked" without sharing important details of what it means to have "worked", what the evidence is, how strong the evidence is, or what the basis is for their judgment - all details that are critical to understanding exactly what happened. If and/or when you start working in a research lab do yourself and your advisor the favor of describing IN DETAIL what you were trying to accomplish (don't assume they'll remember the details), how you decided to accomplish your goal (experimental design), what the exact results were (showing properly labeled data is advised), and providing your interpretation. If you want to end your description by saying "therefore, it worked!" that's also great.
Note: Possible Discussion
Can you think of an example where the imprecise or incorrect use of vocabulary caused needless confusion in real life? Describe the example and discuss how the confusion could have been avoided.
Models and simplifying assumptions
Creating models of real things
Life is complicated. To help us understand what we see around us—in both our everyday lives and in science or engineering—we often construct models. A common aphorism states: all models are wrong, but some are useful. That is, no matter how sophisticated, all models are approximations of something real. While they are not the “real thing” (and are thus wrong), models are useful when they allow us to make predictions about real life that we can use. Models come in a variety of forms that include, but are not limited to:
Types of models
- Physical models: These are 3-D objects that we can touch.
- Drawings: These can be on paper or on the computer and either in 2-D or virtual 3-D. We mostly look at them.
- Mathematical models: These describe something in real life in mathematical terms. We use these to calculate the behavior of the thing or process we want to understand.
- Verbal or written models: These models are communicated in written or spoken language.
- Mental models: These models are constructed in our minds and we use these to create the other types of models and to understand the things around us.
Usually, in science and everyday life alike, simple models are preferred over complex ones. Creating simple models of complex real things requires us to make what are known as simplifying assumptions. As their name implies, simplifying assumptions are assumptions that are included in the model to simplify the analysis as much as possible. When a simplified model no longer predicts behavior of the real thing within acceptable bounds, too many simplifying assumptions have been made. When little predictive value is gained from adding more details to a model, it is likely overly complex. Let’s take a look at different types of models from different disciplines and point out their simplifying assumptions.
An example from physics: a block on a frictionless plane
Figure 1. A line drawing that models a block (of any material) sitting on a generic incline plane. In this example some simplifying assumptions are made. For instance, the details of the materials of the block and plane are ignored. Often, we might also, for convenience, assume that the plane is frictionless. The simplifying assumptions allow the student to practice thinking about how to balance the forces acting on the block when it is elevated in a gravity field and to see that the surface it is sitting on is not perpendicular to the gravity vector (mg). This simplifies the math and allows the student to focus on the geometry of the model and how to represent that mathematically. The model, and its simplifying assumptions, might do a reasonably good job of predicting the behavior of an ice cube sliding down an glass incline plane but would likely do a bad job of predicting the behavior of a wet sponge on an incline plane coated with sand paper. The model would be oversimplified for the latter scenario.
Source: Created by Marc T. Facciotti (Own work)
An example from biology: a ribbon diagram of a protein—the transmembrane protein bacteriorhodopsin
Figure 2. This is a cartoon model of the transmembrane protein bacteriorhodopsin. The protein is represented as a light blue and purple ribbon (the different colors highlight alpha helix and beta sheet, respectively), a chloride ion is represented as a yellow sphere, red spheres represent water molecules, pink balls and sticks represent a retinal molecule located on the "inside" of the protein, and orange balls-and-sticks represent other lipid molecules located on the "outside" surface of the protein. The model is displayed in two views. On the left the model is viewed "side on" while on the right it is viewed along its long axis from the extracellular side of the protein (rotated 90 deg out of the page from the view on the left). This model simplifies many of the atomic-level details of the protein. It also fails to represent the dynamics of the protein. The simplifying assumptions mean that the model would not do a good job predicting the time it takes for the protein to do its work or how many protons can be transported across a membrane per second. On the other hand, this model does a very good job of predicting how much space the protein will take up in a cellular membrane, how far into the membrane the retinal sits, or whether certain compounds can reasonably “leak” through the inner channel.
Source: Created by Marc T. Facciotti (own work), University of California, Davis
Derived from PDBID:4FPD
An example from chemistry: a molecular line model of glucose
Figure 3. A line drawing of a glucose molecule. By convention, the points where straight lines meet are understood to represent carbon atoms while other atoms are shown explicitly. Given some additional information about the nature of the atoms that are figuratively represented here, this model can be useful for predicting some of the chemical properties of this molecule, including solubility or the potential reactions it might enter into with other molecules. The simplifying assumptions, however, hide the dynamics of the molecules.
Source: Created by Marc T. Facciotti (Own work)
An example from everyday life: a scale model of a Ferrari
Figure 4. A scale model of a Ferrari. There are many simplifications and most only make this useful for predicting the general shape and relative proportions of the real thing. For instance, this model gives us no predictive power about how well the car drives or how quickly it stops from a speed of 70 km/s.
Source: Created by Marc T. Facciotti (Own work)
Note: possible discussion
Describe a physical model that you use in everyday life. What does the model simplify from the real thing?
Note: possible discussion
Describe a drawing that you use in science class to model something real. What does the model simplify from the real thing? What are the advantages and disadvantages of the simplifications?
The spherical cow
The spherical cow is a famous metaphor in physics that make fun of physicists tendencies to create hugely simplified models for very complex things. Numerous jokes are associated with this metaphor and they go something like this:
"Milk production at a dairy farm was low, so the farmer wrote to the local university, asking for help from academia. A multidisciplinary team of professors was assembled, headed by a theoretical physicist, and two weeks of intensive on-site investigation took place. The scholars then returned to the university, notebooks crammed with data, where the task of writing the report was left to the team leader. Shortly thereafter the physicist returned to the farm, saying to the farmer, "I have the solution, but it only works in the case of spherical cows in a vacuum"."
Source: Wikipedia page on Spherical Cow - accessed November 23, 2015.
Figure 5. A cartoon representation of a spherical cow.
By Ingrid Kallick (Own work) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
The spherical cow is an amusing way to ridicule the process of creating simple models and it is quite likely that you will have your BIS2A instructor invoke the reference to the spherical cow when an overly simplified model of something in biology is being discussed. Be ready for it!
Bounding or asymptotic analysis
In BIS2A, we use models frequently. Sometimes we also like to imagine or test how well our models actually represent reality and compare that with expectations from what we know to be true for the real life thing. There are many ways to do this depending on how precisely you need to know the behavior of the thing you're trying to model. If you need to know a lot of detail, you create a detailed model. If you're willing to live with less detail, you will create a simpler model. In addition to applying simplifying assumptions, it is often useful to assess your model using a technique we call bounding or asymptotic analysis. The main idea of this technique is to use the model, complete with simplifying assumptions, to understand how the real thing might behave at extreme conditions (e.g., evaluate the model at the minimum and maximum values of a variable). Let’s examine a simple real life example of how this technique works.
Imagine that you need to leave Davis, CA and get home to Selma, CA for the weekend. It's 5PM and you told your parents that you'd be home by 6:30. Selma is 200 miles (322 kM) from Davis. You're getting worried that you won't make it home on time. Can you get some estimate of whether it's even possible or if you'll be reheating your dinner in the microwave?
Create simplified model and use of bounding
You can create a simplified model. In this case you can assume that the road between Davis and Selma is perfectly straight. You also assume that your car has only two speeds: 0 mph and 120 mph. These two speeds are the minimum and maximum speeds that you can travel—the bounding values. You can now estimate that even under assumptions of the theoretically "best case" scenario, where you would drive on a perfectly straight road with no obstacles or traffic at maximum speed, you will not make it home on time. At maximum speed you would only cover 180 of the required 200 miles in the 1.5 hours you have.
In this real life example a simplified model is created. In this case, one very important simplifying assumptions is made: the road is assumed to be straight and free of obstacles or traffic. These assumptions allow you to reasonably assume that you could drive this road at full speed the whole distance. The simplifying assumptions simplified out a lot of what you know is actually there in the real world that would influence the speed you could travel and by extension the time it would take to make the trip. The use of bounding—or calculating the behavior of at the minimum and maximum speeds—is a way of making quick predictions about what might happen in the real world.
We will conduct similar analyses in BIS2A.
The importance of knowing key model assumptions
Knowing what simplifying assumptions are made in a model is critical to judging how useful it is for predicting real life and for starting to make a guess about where the model needs improving if it is not sufficiently predictive. In BIS2A you will periodically be asked to create different types of models and to explicitly identify the simplifying assumptions and the impact of those assumptions on the utility and predictive ability of the model. We will also use models together with bounding exercises to try learning something about the potential behavior of a system.
The scientific method overview
An example of oversimplification that confounds many students of biology (particularly early in their studies) is the use of language that hides the experimental process used to build knowledge. For the sake of expediency, we often tell stories about biological systems as if we are presenting unquestionable facts. However, while we often write and speak about topics in biology with a conviction that gives the appearance of "factual" knowledge, reality is often more nuanced and filled with significant uncertainties. The "factual" presentation of material (usually lacking discussion of evidence or confidence in the evidence) plays to our natural tendency to feel good about "knowing" things, but it tends to create a false sense of security in the state of knowledge and does little to encourage the use of imagination or the development of critical thinking.
A better way to describe our knowledge about the natural world would be to explicitly qualify that what we know to be "true" in science represents only our current best understanding of a topic; an understanding that has not yet been refuted by experiment. Unfortunately, repeated qualification becomes rather cumbersome. The important thing to remember is that while we may not say so explicitly, all of the knowledge we discuss in class represents only the best of our current understanding. Some ideas have withstood repeated and varied experimentation while other topics have yet to be tested as thoroughly. So if we're not as certain about things as we'd like to believe sometimes, how do we know what to put confidence in and what to be skeptical of? The complete answer is non-trivial but it begins with developing an understanding of the process we use in science to build new knowledge. The scientific method is the process by which new knowledge is developed. While the process can be described with long lists of "steps" (often seen in textbooks), its core elements can be described more succinctly.
Succinct description of scientific method (adapted from Feynman)
- Make an observation about the world.
- Propose a possible explanation for the observation.
- Test the explanation by experiment.
- If the explanation disagrees with experiment, the explanation is wrong.
At its core, that's it! In science there may be multiple, simultaneously proposed explanations or ideas that are tested by experiment. The ideas that fail experimentation are left behind. The ideas that survive experimentation move forward and are often retested by alternative experiments until they too either fail or continue to be retained.
Making an observation and asking a question
The ability to make useful observations and/or ask meaningful questions requires curiosity, creativity, and imagination—this cannot be overstated. Indeed, historically, it is first and foremost the application of these skills, perhaps more than technical ability, which has led to big advances in science. Many people think that making meaningful observations and asking useful questions is the easiest part of the scientific method. This is not always the case. Why? Seeing what others have not yet asked and creativity both take work and thoughtful reflection! In addition, our senses of observation are often biased by life experience, prior knowledge, or even our own biology. These underlying biases influence how we see the world, how we interpret what we see, and what we are ultimately curious about. This means that when we look at the world, we can miss a lot of things that are actually right under our noses. Douglas Adams, who is best known for his book entitled The Hitchhiker’s Guide to the Galaxy, once expanded on this point by writing:
“The most misleading assumptions are the ones you don't even know you're making.”
Scientists, therefore, need to be aware of any underlying biases and any assumptions that may influence how they internalize and interpret observations. This includes approaching our bias that the variety of places we get our knowledge (i.e., textbooks, instructors, the Internet) are representing the absolute truth with a healthy dose of skepticism. We need to learn to examine the evidence underling the “facts” we supposedly know and make critical judgments about how much we trust that knowledge. More generally, taking the time to make careful observations and to uncover any assumptions and biases that could influence how they are interpreted is, therefore, time well spent. This skill, like all others, needs to be developed and takes practice and we’ll try to start you on this in BIS2A.
For fun, and to test your observation skills, Google “observation tests”. Many of the search results will take you to interesting psychological tests and/or videos that illustrate how difficult accurate observation can be.
Generating a testable hypothesis
The "possible explanation" referred to in step three above has a formal name; it is called a hypothesis. A hypothesis is not a random guess. A hypothesis is an educated (based on prior knowledge or a new viewpoint) explanation for an event or observation. It is typically most useful if a scientific hypothesis can be tested. This requires that the tools to make informative measurements on the system exist and that the experimenter has sufficient control over the system in question to make the necessary observations.
Most of the time, behaviors of the system that the experimenter wants to test can be influenced by many factors. We call the behaviors and factors dependent and independent variables, respectively. The dependent variable is the behavior that needs explaining while the independent variables are all of the other things that can change and influence the behavior of the dependent variable. For example, an experimenter that has developed a new drug to control blood pressure may want to test whether her new drug actually influences blood pressure. In this example, the system is the human body, the dependent variable might be blood pressure, and the independent variables might be other factors that change and influence blood pressure like age, sex, and levels of various soluble factors in the blood stream.
In BIS2A, and beyond, we prefer to avoid using language like “the experiment proved her hypothesis” when referring to a case like the blood pressure example above. Rather we would say, “the experiment is consistent with her hypothesis.” Note that for convenience, we referred to the alternative hypothesis simply as “her hypothesis”! It would be more correct to state, “the experiment falsified her null hypothesis and is consistent with her alternative hypothesis.” Why take this shortcut since doing so adds confusion when a student is trying to learn? In this case, it was done to illustrate the point above about language shortcuts and hence the lengthy explanation. However, be aware of this commonly used shortcut and learn to make sure you can read in the correct meaning yourself.
Note: possible discussion
What does the statement about falsifying hypotheses mean in your own words? Why is falsification critical to the scientific method?
In an ideal case, an experiment will include control groups. Control groups are experimental conditions in which the values of the independent variables (there may be more than one) are maintained as close to those in the experimental group with the exception of the independent variable being tested. In the blood pressure example, an ideal scenario would be to have one identical group of people taking the drug and another group of people identical to those in the experimental group taking a pill containing something known to not influence blood pressure. In this oversimplified example, all independent variables are identical in the control and experimental groups with the exception of the presence or absence of the new drug. Under these circumstances, if the value of the dependent variable (blood pressure) of the experimental group differs from that of the control group, one can reasonably conclude that the difference must be due to the difference in independent variable (the presence/absence of the drug). This is, of course, the ideal. In real life it is impossible to conduct the proposed drug dosage experiment; the sheer number of possible independent variables in a group of potential patients would be high. Fortunately, while statisticians have come to the rescue in real life, you won’t need to understand the nuances of these statistical issues in BIS2A.
Accuracy in measurement, uncertainty, and replication
Finally, we mention the intuitive notion that the tools used to make the measurements in an experiment must be reasonably accurate. How accurate? They must be accurate enough to make measurements with sufficient certainty to draw conclusions about whether changes in independent variables actually influence the value of a dependent variable. We take, yet again, the blood pressure example above. In that experiment, we made the important assumption that the experimenter had tools that allowed her to make accurate measurements of the changes in blood pressure associated with the effects of the drug. For instance, if the changes associated with the drug ranged between 0 and 3 mmHg and her meter capably measured changes in blood pressure with a certainty of +/- 5 mmHg, she could not have made the necessary measurements to test her hypothesis or would have missed seeing the effect of the drug. For the sake of the example, we assume that she had a better instrument and that she could be confident that any changes she measured were indeed differences due to the drug treatment and that they were not due to measurement error, sample-to-sample variability, or other sources of variation that lower the confidence of the conclusions that are drawn from the experiment.
The topic of measurement error leads us to mention that there are numerous other possible sources of uncertainty in experimental data that you as students will ultimately need to learn about. These sources of error have a lot to do with determining how certain we are that experiments have disproven a hypothesis, how much we should trust the interpretation of the experimental results and, by extension, our current state of knowledge. Even at this stage, you will recognize some experimental strategies used to deal with these sources of uncertainty (i.e., making measurements on multiple samples, creating replicate experiments). You will learn more about this in your statistics courses later on.
For now, you should, however, be aware that experiments carry a certain degree of confidence in the results and that the degree of confidence in the results can be influenced by many factors. Developing healthy skepticism involves, among other things, learning to assess the quality of an experiment and the interpretation of the findings and learning to ask questions about things like this.
While the preceding treatment of the experimental method is very basic—you will undoubtedly add numerous layer of sophistication to these basic ideas as you continue in your studies—it should serve as a sufficient introduction to the topic for BIS2A. The most important point to remember from this section is that the knowledge represented in this course, while sometimes inadvertently represented as irrefutable fact, is really just the most current hypothesis about how certain things happen in biology that has yet to be falsified via experiment.
The Design Challenge
Your BIS2A instructors have devised something that we call “The Design Challenge” to help us approach the topics we cover in the course from a problem solving and/or design perspective. This teaching tool helps us:
• develop a frame of mind or way of approaching the material and
• design a set of sequential steps that help structure thinking about course topics in a problem-solving context.
How is it intended to work? When we encounter a topic in class, “The Design Challenge” encourages us to think about it in the following problem-solving centric way:
- Identify the problem(s) - this may include identifying "big" problems and also decomposing them into "smaller" nested sub-problems
- Determine criteria for successful solutions
- Identify and/or imagine possible solutions
- Evaluate the proposed solutions against the criteria for success
- Choose a solution
By using the structure of the design challenge, topics that are typically presented as lists of facts and stories are transformed into puzzles or problems that need solving. For instance the discussion about the topic of cell division is motivated by a problem. The problem statement can be: "The cell needs to divide". Some of the criteria for success can include needing to have a near identical copy of DNA in each daughter cell, distributing organelles between the daughter cells so that each remains viable etc. These would be considered sub-problems to the larger “the cell needs to divide” problem. One can then go on to explore what the challenges are and try to use existing knowledge and imagination to propose some solutions for each of those problems. Different solutions can be evaluated and then compared to what Nature seems to have done (at least in the cases that are well studied).
This exercise requires us to use imagination and critical thinking. It also encourages the student and instructor to think critically about why the particular topic is important to study. The Design Challenge approach to teaching biology attempts to make the student and instructor focus on the important core questions that drove the development of the knowledge in the first place! It also encourages students to dream up new ideas and to interact with the material in a manner that is question/problem-centered rather than “fact”-centered. The question/problem-centered approach is different from what most people are used to, but it is ultimately more useful for developing skills, mental frameworks and knowledge that will transfer to other problems that they will encounter during their studies and beyond.
For example, the guiding problem in BIS2A is to understand “How to Build a Cell”. This rather complex problem will be broken down into several smaller sub-problems that include:
- acquiring the building blocks to construct cellular parts from the environment
- acquiring the energy to build cellular parts from the environment
- transforming the building blocks of the cell between different forms
- transferring energy between different storage forms
- creating a new cell from an old cell
- problems we identify in class
As we explore these sub-problems, we will at times explore some of the different ways in which biology has addressed each issue. As we get into details, let us however make sure to stay focused on and not forget the importance of always staying linked to the questions/problems that motivated us to talk about the specifics in the first plac
Scientific Method vs. The Design Challenge
At this point you might be thinking: "What is the difference between the scientific method and the design challenge rubric and why do I need both?" It's not an uncommon question so let's see if we can clarify this now.
The design challenge and the scientific method are both processes that share similar qualities. The critical distinguishing feature, however, is the purpose behind each of the processes. The scientific method is a process used for eliminating possible answers to questions. A typical scenario where one might use the scientific method would involve someone making an observation, proposing multiple explanations, designing an experiment that might help eliminate one or more of the explanations, and reflecting on the result. By contrast the design process is used for creating solutions to problems. A typical scenario for the design challenge would start with a problem that needs solving, defining criteria for a successful resolution, devising multiple possible solutions that would meet the success criteria and either selecting a solution or reflecting on changes that might be made to the designs to meet success criteria. A key operational difference is that the design challenge requires that criteria for success be defined while the scientific method does not.
While both are similar, the differences are still real and we need to practice both processes. We'll assert that we use both processes in "real life" all of the time. A physician, for instance, will use both processes interactively as she forms hypotheses that try to determine what might be causing her patient's ailments. She will turn around and use the design process to build a course of treatment that meets certain success criteria. A scientist may be deep into hypothesis generation but he will eventually need to use a design process for building an experiment that will, within certain definable success criteria, help him answer a question.
Both processes, while similar, are important to use in different situations and we want to begin getting better at both.