Get Out and Get Data…

In September of last year, the University of Kansas Biological Field Station graciously opened its facilities to the environmental science students of Basehor-Linwood High School. Scott Campbell, associate director of outreach and public services for Kansas Biological Survey, received the 20 students at the Armitage Center. Scott, a true educator, engaged the students in a discussion about the broad mission of the field station. Students curiously asked many questions about the current research that was being conducted.

Students received some general guidelines about how to treat the animals ethically. Soon students began a fierce competition to catch the most frogs. In the classroom a discussion about population surveys would have been met with little excitement. At the side of the pond, with frogs leaping through the cool September grass, there was not a student in twenty who thought this was a meaningless exercise. The excitement was palpable.

Once the frogs were collected , students retired to the Armitage Center for sack lunches. The frogs were cooled in a large refrigerator to make them easier to work with. Students had practiced weighing and measuring frogs in the classroom. Now these skills were put to work- there were 134 frogs to weigh, measure, and score for color patterns.

This scene was punctuated by moments of chaos when a frog or two would make a dive to get out of the grips of the high school students. After all the data was collected students returned to the pond to release the frogs. On the way, Mr. Daniel Smalley, their teacher, caught a small black snake.

 

The snake made its way to Mr. Stan Roth who is an adjunct research assistant and educator for the Kansas Biological Survey. Stan identified the species and engaged the students in a conversation about the natural history of the snake. Many students touched a snake for the first time.

Finally, students were able to seine in the pond. They had a good harvest of small fish and invertebrates.

Before the students returned to class they visited the Rockefeller Prairie and walked the trails. Students collected 10 flowering heads of goldenrod. The flowering heads were quickly covered in gallon ziplock bags and sealed shut. Inside all the insect species that were foraging or hunting on the flower heads were sealed too.

Back at school the students compiled the data into a Google spreadsheet. This data was then analyzed and graphed by hand. Thus, students had the chance to analyze data about a population that they had collected. Mr. Smalley then entered the information into Plotly an online graphing platform. The computer allowed the students to more easily analyze the distribution variables like snout to vent length and weight.

The final graph that students examined compared the length of frogs to their weight. Mr. Smalley explained that we should expect to see a strong connection between these two variables. Further, he explained, that this was an example of a mathematical model that could be used to predict and explain the population. Who knew there could be so much math in environmental science?

After the frog data was analyzed students took out ten bags of Goldenrod. The bags had been frozen. Students separated out the insects from the Goldenrod. They had to identify the insect species. Thankfully, Mr. Smalley has had a lifelong obsession with collecting bugs so with his help and a few field guides students quickly were able to determine the species they were looking at. Mr. Smalley then helped the students put together a food web based on these species. The bugs could then be categorized by their tropic level . Students collected the bugs of similar trophic levels together. This included 14 jumping spiders that served as top predators! Each level was weighed together. The students turned this into a large bulletin board that was displayed in the hallway. Mr. Smalley explained that this too was a model that showed where the biomass (a proxy for energy) was located in this micro community. Students really took to the project and decided that It would be good to include the actual organisms. Thus, all 14 spiders, herbivorous insects, and Goldenrod flower heads found their way on the bulletin board.

Experiences like this empower our youth to see themselves as shareholders of knowledge rather than passive vessels who blithely learn facts about things like ecosystems only to recite them back on tests.

The Implications of Mindset Research on Science Classrooms

A recent article in Buzzfeed News shared an overview of some of the concerns surrounding growth mindset research and pedagogies focused on leveraging that research. The membership of KABT has considered the article and its critiques and has created the following response.

 

The Implications of Mindset Research for Science Classrooms

Mindset research, which focuses on the differences between growth and fixed mindset, has been popularized by Carol Dweck and her associates (Yeager, 2012) (Dweck, 2008) (Dweck C. S., 2012). While there is a growing market for classroom materials, publications and workshops related to promoting growth mindsets in students, there is a growing discussion regarding the inadequacies of the research base for mindset methodologies.

 

The initial work by Dr. Dweck has been criticized for some of its experimental design and statistical practice. Those mistakes have been accepted by Dr. Dweck and corrections and revisions have been acknowledged. The accumulating list of errors has led to concerns regarding the validity of the results. Statistical practice in social science is an area in need of considerable improvement and a demand for best practice from the consumers of the research, the practitioners, is a way to incentivize this improvement. Teachers should not shrug off statistical malpractice as only a footnote.

 

Strong statistical analysis improves the confidence of readers in the reproducibility (or lack thereof) of the work. In this instance the missing statistical confidence pairs with a lack of reproduction to this point. Reproducing education research requires tremendous skill in both pedagogy and experimental design. There are concerns that the small number of attempts at reproduction do not faithfully recreate the appropriate conditions for eliciting the effect. There are other questions about the value of the growth mindset effect if it is so small and fragile that reproduction by researchers is exceedingly difficult.

 

At its heart growth mindsets tap into a long-held belief that hard work is valuable. Many teachers find the idea of a student having agency over their own achievement to be desirable. Growth mindset is not the solution to classroom culture. Students need more than just hard work. Tremendous energy can be spent smashing into a door until you are through it, or you can simply turn the handle and open it. When mindsets augment a productive culture and a coherent curriculum they can be powerful.

 

When poorly-supported methodologies are used and the teaching practice is weak, a growth mindset is of little value. As the market saturates with products claiming to be based on mindset research, we must identify which are doomed to failure from unthoughtful application. Many flying machines failed due to ignorance of flight mechanics, but flight was always possible. Similarly, when a speaker with a shallow understanding of the literature fails to demonstrate value in a product we should remember that a failed application is not the same as a faulty concept.

 

Inquiry in the science classroom is well-supported by research as best practice. Interconnected understanding, developed through retrieval practice and responsive feedback, is superior to linear content delivery by a lecturing expert to passive audience members. Shaping that feedback through a growth mindset lens appears to have a positive impact despite the fact that reproduction of the research is proving difficult. Indeed, our job as educators is to do something that is hard! We must synthesize the research regarding mindset with best practices in assessment, classroom management, curricular design, differentiation, inquiry instruction, choice theory and many other overlapping domains to produce the strongest experience possible in unique students who change every semester. This is our job and they call us educators.

 

We must be faithful to the body of research. We can simultaneously incorporate aspects of the growth mindset research into our classroom and remain skeptical of the work. If future studies reveal flaws and allow us to develop a better description of how student perceptions shape learning, we should follow that work also. We also need to share our perspective from the field to shape investigations to be more useful and applicable. Perhaps more dynamic classroom methodologies will yield stronger signals in new mindset research. Perhaps responsive differentiation will allow us to visualize specific demographics who stand to benefit the most from a growth mindset. Perhaps training in retrieval practice as studying will increase the propensity of students to adopt and develop a growth mindset through greater yields from investment.

 

Ultimately we must close the gap between practitioner and researcher in education. Medical doctors consider experimental treatments and provide feedback on their results on a regular basis. Attorneys publish briefs and review frequently to respond to an ever-changing body of legislation. We, too, must become more than consumers of research. Teachers must communicate with researchers because our classroom experience can make experimental design better. We can demand stronger statistical practice, more meaningful treatment conditions and more targeted assessment tools. The improved research will return as more actionable results which we can use to improve our technique again.

 

We must allow our classroom practice to respond to the current literature while acknowledging and addressing its flaws. It is highly unlikely that a growth mindset is the educational silver bullet. It is also unlikely to be entirely smoke and mirrors. Instead we are trying to understand a remarkably complex system so that we can help it mature as effectively as possible. As far as promoting a growth mindset can further that goal, we should use it. When the body of research indicates there is a better approach, teachers should change methods. We should not switch a moment later than when the research publishes, but not a moment before either.

References

  1. Dweck, C. (2008). Mindsets and math/science achievement.
  2. Dweck, C. S. (2012). Mindsets and human nature: Promoting change in the Middle East, the schoolyard, the racial divide, and willpower. American Psychologist, 67(8), 614.
  3. Yeager, D. S. (2012). Mindsets that promote resilience: When students believe that personal characteristics can be developed. . Educational Psychologist, 47(4), 302-314.

 

Sternberg Museum Summer Science Camps

Fort Hays State University’s Sternberg Museum is providing another year of high-quality field experiences for students. They are offering courses for elementary, middle, and high school students, and even have international trips available.

The full catalog is available here. If you need more information, or are interested in one of the available scholarships, contact education director David Levering using the information below.

Greetings from the Sternberg Museum of Natural History! We are excited to offer our 2017 Summer Science Camps and Programs designed to immerse students in the wonders of Earth and life science!
The Sternberg Museum education and science staff presents experience-driven lessons and activities that get students directly involved in the process of science. We emphasize building knowledge, skills and the mental tools to deal with information and questions in a scientific manner.
Outdoor exploration is at the heart of our science camps and programs. Getting students outside interacting with nature, each other and instructors helps to anchor our lessons with powerful firsthand experiences. We look forward to sharing the wonder of science and exploration with you this summer!
Sincerely,
David Levering
Education Director
DALevering@FHSU.edu
785-639-5249

A 3D Gene Expression Lesson on Epigenetics

Disclaimer: As far as standards go, I really like the Next Generation Science Standards. Particularly important to me is the emphasis it places on learning not just the content (disciplinary core ideas), but how scientists work/think (science practices) and connections between ideas (cross-cutting concepts). Over the last 3-4 years, I have been giving my favorite activities and labs an NGSS facelift to modify them to better fit this framework. I am going to share with you a lesson that I feel address all 3 dimensions of the NGSS.

 

Is your lesson “3D”? Use the NGSS Lesson Screener tool to find out.  LINK

Many students really enjoy their genetics units, but one of the more difficult things to understand is gene expression. Several years ago, I would have presented my students with the “central dogma”, given some notes over transcription and translation, then worked through a few scaffolds to get them to understand how amino acid chains are produced. After reading Survival of the Sickest in 2008, I started to mention that epigenetics was a thing, though I didn’t have my students investigate it with any depth.

With the introduction of the Next Generation Science Standards, an emphasis has been placed on understanding the implications of the processes in the classic dogma without getting overly concerned about what specific enzymes might be doing at a given time. This has freed up more time to explore the regulation of gene expression, including epigenetics. There are a number of amazing resources out there (like this… and this… and this…), but here is how I cover gene regulation with my 9th grade biology students:

This format is something I have adapted (with few changes) from an NGSS training put on by Matt Krehbiel and Stephen Moulding, which I attended thanks to KSDE. I like this because it is flexible, provides students with the entire trajectory of the lesson from the beginning, and can double as a lesson plan. Can you guess the reasoning behind the color-coded words? That, too, is explicit, though it is in most cases more for my own benefit. RED words are commands for the students. It tells them how they should address the problem and how I will assess their work. The GREEN words relate to cross-cutting concepts (in this case, systems/system models and patterns), while the BLUE(ish) words are science practices.

Depending on how much time you have available, this could take 2 to 4 50-minute class periods (or 1-2 block periods if you’re lucky enough to roll with that schedule).  I like to use more time for this because I have designed discussion and collaboration into the process, but the “Gather Information” and (obviously) “Individual Performance” sections could be done by students on their own and wouldn’t require a classroom. Devoting a little extra class time will also allow for you to conduct ad hoc informal formative assessments (read over a kid’s shoulder and ask them questions) as you move around your room.

Part 1: Gathering Information

Have you listened to the RadioLab episode, “Inheritance”? If not, you should do that. I find that RL is a good way to indoctrinate your students into the world of science podcasts. And this episode is one of my favorites. 

I really like reading with my students, asking them questions that get them thinking deeper as they go, so I usually devote an entire class period to reading an article on epigenetics. I break my class into three groups with each group reading a different article, and students will (for the most part) self-select based on the length or difficulty of the reading.  I use readings pulled from Discover Magazine, Nature Education, Nat Geo’s Phenomena blogs. Students sit around large tables and talk and write and sketch as they read. There is structure and agency, direction and freedom, and I love those days. But if you’re in a hurry (in my opinion, one of the worst reasons to do something), I guess you could assign the reading as homework.

via GIPHY

Part 2: Thinking Deeper

To really understand something, you need to really dig into it. This section is meant to be collaborative. If I have some really outstanding students grouped together, I will encourage them to divide the work in this section between them, then teach their group members in a scaffold.  I wouldn’t normally do this with an extension/research-based activity because I want to make sure each student has a chance to interact with each aspect of the activity. If I can’t trust all the group members to produce the same quality of work, I won’t recommend the divide-and-conquer approach.

When dealing with my AP/College Biology students, I would word a question like #5 differently. With the general biology kids, I recognize most of them will not end up in a biology-centric career. They will, however, be citizens of the world, and voters. So I try to incorporate questions where they reflect on their emotional response to the content. I know it is popular to think of scientists as unfeeling, opinion-less automatons, but that is disingenuous. I live with a scientist, trust me. I use experiences like this to really emphasize the importance of evidence-based, empirical thinking and using data to drive decision-making.

Part 3: Individual Performance

How do you know if your students “get it”?  A lot of the time, when using a science notebook or interactive journal, it might be several days before you go back and read everything your students wrote (and maybe, sometimes, you still don’t read everything). What I like to do is tell students they will have 15 minutes to produce the best possible answer after I give them 5 minutes to discuss with their classmates how they will address the last couple of items for this assignment. Once the writing starts, I am walking the room, reading over shoulders, and looking for patterns. Are there any things that I think they should have gotten, but most people are missing? Are we particularly strong in certain areas? Are students adding models to their answers in support? This lets me know if I need to reteach something or if we can move on.

I also look for answers that are good, but might be missing one bit of information to take it over-the-top. It is a good rule of thumb to think that, if one student is making a mistake, there are other students making the same error. I will then (not so) randomly ask students to read exactly what they have written down. By using an answer that is mostly correct, it takes some of the stigma away from making a mistake. We can then have a discussion with the class to see if we can identify where the answer can be changed or added to, and praise the parts of the answer that were done well. Students with sub-par responses are encouraged to add to their answers, and we learn more together.

Conclusion 

If you are still with me, what do you think? What does this activity do well? Where can I get better? What are my students missing? If you would like to modify/use this activity, you can find a GoogleSlides version here. Send me an email (andrewising[at]gmail) or tweet (@ItsIsing) and let me know how it went!

Summary Post for Teaching Quantitative Skills

Part 1: Teaching Quantitative Skills using the Floating Disk Catalase Lab: Intro
Part 2- Teaching Quantitative Skills in a Lab Context: Getting Started in the Classroom
Part 3- Establishing an Experimental Procedure to Guide the Home Investigation
Part 4- Teaching Quantitative Skills: Data Analysis
Part 5- Curve Fitting AKA Model Fitting–the End Goal
Part 6- The Final Installment: Extending and Evaluating Quantitative Skills.
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These are links to the posts on Teaching Quantitative Skills with the Floating Disk Enzyme Lab

  1. http://www.kabt.org/2016/11/29/teaching-quantitative-skills-using-the-floating-disk-catalase-lab-intro/
  2. http://www.kabt.org/2016/12/01/teaching-quantitative-skills-in-a-lab-context-getting-started-in-the-classroom/
  3. http://www.kabt.org/2016/12/04/establishing-an-experimental-procedure-to-guide-the-home-investigation/
  4. http://www.kabt.org/2016/12/09/data-analysis/
  5. http://www.kabt.org/2016/12/18/curve-fitting-aka-model-fitting-the-end-goal/
  6. http://www.kabt.org/2017/01/06/the-final-installment-extending-and-evaluating-quantitative-skills/

In DNA, C pairs with G and X pairs with Y?

Big news! I recently read an article in the Washington Post that wasn’t about our current political leadership, and I highly recommend it to all Biology teachers. An international team of researchers has published their findings in a paper titled, “A semisynthetic organism engineered for the stable expansion of the genetic alphabet” in journal PNAS. (If you like to also read the primary literature on these newspaper and magazine science stories, it is unfortunately behind a paywall.)

via NIH Flickr Acct.

I am no Eric Kessler, resident KABT expert on synthetic biology (synbio), but I was amazed by what I read. It is incredibly fascinating to consider the scientific breakthroughs that have been made during my teaching career, not to mention my lifetime. I was lucky enough to have Mr. Kessler as my AP Biology teacher when I was a high school student, and we barely touched on the topic of biotechnology in the halcyon days of the early 2000’s. Even in my undergraduate education, little time was spent on biotechnology and genetics labs. Fast forward about a decade and scientists are able to build synthetic nucleotides that can be copied into E. coli and conserved for more than 60 generations. This leads me to an obvious question: what will be possible when my current crop of freshpersons are leaving college?

Environmental biochemists have long hinted about the possibility of a microorganism capable of safely remediating oil spills and other industrial accidents. Could this lead to what amounts to biomachines capable of conducting targeted medical therapies in a patient? I have a sister with cystic fibrosis, and would like to imagine a time when an SSO (semisynthetic organism) is capable of producing functional copies of CFTR1, effectively curing her of the disease that once promised to take her life.

What was your reaction? What application would you like to see for this technology?

LINKS
Washington Post: “Biologists breed life form with lab-made DNA. Don’t call it ‘Jurassic Park’,” by Ben Guarino
Proceedings of the National Academy of Science of the United States of America: “A semisynthetic organism engineered for the stable expansion of the genetic alphabet,” by Y. Zhang and B. Lamb, et al.

KABT 2017 Winter Board Meeting

What: KABT Winter Board Meeting (RESCHEDULED)
When: Saturday, February 18th. 9AM-3PM (or earlier should we move through the agenda)
Where: Baker Wetlands Discovery Center; 1365 N 1250 Rd, Lawrence, KS 66046 (MAP)
Who: All KABT members are welcome to attend.

We will be having a potluck lunch. If you are coming and still need to “sign up” for something, Jesi Rhodes created a spreadsheet for us.

Tentative Agenda: GoogleDoc

If you have any questions or would like to have something added to our agenda, please don’t hesitate to send an email to askkabt@gmail.com.

Hope to see everyone there!

Drew Ising

ICYMI: Secretary of Education Confirmation Hearing

Below is video from the recent confirmation hearing for Secretary of Education candidate Betsy DeVos, courtesy of C-SPAN. You can find this video on their website, along with a transcript of the 3-hour proceeding.  I would recommend that any stakeholder in our education system (basically everyone) take the time to watch this hearing and develop their own reason- and evidence-based views on the answers provided.

If you have any comments regarding the candidacy of Ms. DeVos (be they positive, negative or otherwise), I encourage you to share those with your congressional representatives in the House and Senate. The members of the Senate Committee on Health, Education, Labor, and Pensions can be found here. Our own Pat Roberts, is a member of this committee, he can be contacted from this webpage. You can also call Senator Roberts’ offices to share your comments with him using the phone numbers below.
Washington, D.C. Office: (202) 224-4774
Dodge City, KS Office: (620) 227-2244
Topeka, KS Office: (785) 295-2745
Overland Park, KS Office: (913) 451-9343
Wichita, KS Office: (316) 263-0416

If you have any questions or would like to start a dialogue on this (or another) matter, drop us a comment in the KABT Facebook group or send us an email at askkabt@gmail.com.

The Final Installment: Extending and Evaluating Quantitative Skills.

A note:  You might want to scroll down, directly to Applying the NetLogo model to avoid my long winded setup and context)   

Getting Stuck in a Rut:

I grew up about 1 mile from the Santa Fe Trail which cuts diagonally across Kansas on its way from Independence, Mo. to Santa Fe, New Mexico.  And I have lived most of my adult life close to the trail.  Not everyone is familiar with this historical trail so here’s a quote from the Santa Fe Trail Association’s website that might put things into context:   “In 1821, the Santa Fe Trail became America’s first great international commercial highway, and for nearly sixty years thereafter was one of the nation’s great routes of adventure and western expansion. ”  For folks growing up on the plains, the trails are kind of a big deal.  For instance, along U.S. highway 400/50 in western Kansas you can pull over, park and walk in the ruts of the trail that still exist.  Here’s a Google Earth screen shot of the ruts trending to the SW. I have put a white polygon around the ruts.  Amazing, isn’t it?

 



More than 150 years have not erased these ruts.  How many wagons, people and livestock must have walked in these ruts, all with the same goal.  “Stuck in a rut” takes on additional meaning when you realize where the phrase comes from.  As you can see from this image as each of the ruts became “impassable” for the wagons they would start a new path parallel to it–still heading in the same direction with a focused goal.  Obviously, this highway opened up surrounding areas to Europeans but only if they got out of the ruts.  And just as obviously, this trail helped to set things in motion that eventually led to tragedy for the Native Americans.  That is another discussion.    But why bring up ruts on the Santa Fe trail as I finish out a series of posts about leveraging the yeast catalase floating disk lab to introduce and reinforce a plethora of quantitative skills to biology students?

Well, the short answer is that I think we, the teacher community, are particularly at risk of getting “stuck in a rut.”  Like the folks on the Santa Fe trail we are often looking for direct, point to point solutions for many of the challenges that surface in a classroom of students who all have different skills and backgrounds.  Take for example, “The Scientific Method”.  Here, was a simplification designed originally by Paul Brandwein to make science as a verb more accessible to both teachers and students.  Of course it was a simplification and of course, if Paul were still here, he’d be appalled at how one-dimensional this model has become.  We do that in science education–we make ruts—deep ruts.  Another example, that strikes close to home is the former AP Biology Lab manual–a series of labs that became known as the “Dirty Dozen” that folks felt they had to follow to the letter while almost always neglecting or ignoring the suggestions at the end of each laboratory for further, deeper investigations–another deep rut.

As many of you know, I’ve spent the last 9 years helping to prepare math and science teachers in the UKanTeach program.  In this program we introduce the students to the 5E lesson plan model to help them prepare effective (and efficient) lessons that are steeped in inquiry.  The design works fairly well and really serves as a great scaffold to build an effective lesson or series of lessons around.  Those of you familiar with the model may recognized that one could deconstruct these series of posts down into the 5E’s.  Engage, Explore, Explain, Extend, and Evaluate.  But, to avoid our basic nature of creating a rut to fall into, I’ve purposely left out any explicit designation, notation or label consistent with the 5E’s.  Part of that is because, I think you can see that some folk’s Extension activity might be another’s Evaluation activity.  It depends on the context in my opinion.  Perhaps more importantly, is that I don’t want to suggest that teaching quantitative skills is a linear process.  Following linear paths, creates ruts.  Instead, I hope I presented a multitude of paths or at least suggested that this lab is a very rich resource that opens all sorts of options you might consider.  It is up to you, the trail boss to decide how you plan to guide your class over the quantitative skills landscape and hopefully, you’ll find it rewarding to the point of taking quantitative skill instruction beyond what I’ve suggested here.

With that said, I am going to present material here that might fit more appropriately in the Extend or Evaluate phase of a 5-E lesson.  I see a couple of paths forward.  One takes the quantitative and content level skills learned in this exploration and applies them in an another laboratory investigation and the other takes those same skills but applies them in model-based environment.  Doubtlessly there are many other paths forward for you to discover but let’s focus on these for now.

A model environment that probes deeper into thinking about enzyme reaction kinetics:

But first some more history/ reminiscing.

In the 1980’s when personal computers first arrived on the educational scene one of the first applications were programs that provided simulations of biological phenomena.  I even wrote one that students could use to generate inheritance data with simulated fruit fly crosses.   I was pretty proud of it to the point that I actually marketed it for awhile.  Students had to choose their parent flies with unknown genotypes from primitive graphic images that provided phenotype information.  Once a cross was chosen, then the program would randomly according to the inheritance pattern generate about 48 fly images that represented the phenotypes possible.  The student had to infer genotypes from phenotypes.  However, when I wrote this program I created an option where the student could pick and choose the inheritance pattern to investigate.  So the program  only simulated data to confirm a given inheritance pattern. The data was realistic since it used a random function to generate gametes but it could have promoted more inquiry and scientific thinking.  I found this out when I cam across the Genetics Construction Kit (GCK) a piece of software written by John Calley and John Jungck.  This program lacked the graphics that mine had but it promoted inquiry much, much better.  Students didn’t start by choosing an inheritance patter.  Instead, they received a sample “vial” of flies with a number of different traits expressed.  They had to choose different flies, different traits and such and then design crosses, form hypotheses, look for data to support those hypotheses and go to work.  It was a revelation.  Even better to my way of thinking it “modeled” almost every type of inheritance you could study in those days.  Even more better—the program didn’t tell the student if they were right or wrong.  The student (and the teacher) had to look at the various crossing data to determine if the data supported their hypothesis.  This was an excellent educational tool to teach genetics.  If you and your students could meet the challenge I guarantee that the learning was deep.  (Collins, Angelo, and James H. Stewart. “The knowledge structure of Mendelian genetics.” The American Biology Teacher 51.3 (1989): 143-149.)   If you don’t have access to JSTOR you can find another paper by Angelo Collins on the GCK here:  Collins, Angelo. “Problem-Solving Rules for Genetics.” (1986).  I promoted the GCK heavily throughout the late 80’s and 90’s.  I still think it is an inspired piece of software.  The problem was that little bit about not providing answers.  Few of my teaching colleagues were comfortable with that.  I’d round up computers or a computer lab for a presentation or professional development.  Everything would be going smoothly.  There would be lots of ooh’s and aw’s as we worked through the introductory level and then everything would so south when the teachers would find out that even they couldn’t find out what the “real” answer was.  Over and over, I’d ask; “Who tells a research scientist when they are right?” but it wasn’t enough.  Teachers, then were not as comfortable without having the “real” answer in their back pocket.  I think that has changed now at least to some degree.

The software world has changed as well.  GCK went on to inspire Bioquest.  From their website:  “BioQUEST software modules and five other modules associated with existing computer software, all based on a unified underlying educational philosophy. This philosophy became known, in short, as BioQUEST’s 3P’s of investigative biology – problem posing, problem solving, and persuasion.”  Another early “best practice” educational application of computer technology was the software LOGO from Seymour Papert’s lab.  LOGO was agent based programming specifically targeted to students early in their educational trajectory.  My own children learned to program their turtles.  As the web developed and other software environments developed LOGO was adapted into NETLOGO.

Netlogo (Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.) is a modeling environment that is greatly underutilized in the educational community.  Netlogo is an agent based programming language.  There is a desktop version and there is a web browser version.  Agent based models can provide very interesting simulations models of real world. Agent based programming assigns various properties to individual “agents” along with a set of rules for how this agent interacts with other agents or with the “environment”.  No doubt you and your students will gain the most learning if you could explore coding and building your own model but unless we do this multiple times during the year, the time requirement to build these skills is usually prohibitive.  You don’t have to be a coder, though to utilize these tools.  Luckily the Netlogo community has already put together a model on Enzyme Kinetics that can extend your student’s understanding of enzymes.  (Stieff, M. and Wilensky, U. (2001). NetLogo Enzyme Kinetics model. http://ccl.northwestern.edu/netlogo/models/EnzymeKinetics. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.)

But it is not always clear how to take advantage of NetLogo simulations as a teaching resource.  Typically students tend load up one of the simulations, click on a few parameters, get some kind of graph that they don’t understand and then shut down—the program and their brain.  Models environments like this require systematic exploration much like a laboratory investigation.  And, just as you try to engage your student in lab based inquiry where they are thinking for themselves and not following a bunch of cookbook like instructions, you need to find ways to encourage your students to engage deeply with the model.  Parameters need to be changed in response to questions—not just to see what happens.  Seeing what happens with random changes can lead to insights but generally it is better to have a plan–to explore the model systematically.  This type if involvement or engagement by the student requires a bit of student motivation.  There are many sources of student motivation and Mihály Csíkszentmihályi‘s FLOW theory applied to education is a good place to start.  In FLOW theory the idea is to seek a good balance between the challenge before you and skills you bring to the table.  One of the important lessons I’ve learned over the years is that using model simulations requires every bit of set-up and preparation as a “normal” wet lab does.  And, more to the point for this example is that working with the actual physical/biological phenomenon ahead of time helps to create enough first-hand knowledge, beginning skills and such that working with models becomes more accessible to students.  Students aren’t used to working with software like this and it takes a bit of preparation to get them to explore the model in a productive way.  In this example of the floating disk catalase lab the students by this time will have explored models to develop a beginning conceptual understanding of enzyme reactions, designed and carried out experiments, collected and analyzed data, and perhaps have fit their data to mathematical expressions.  Hopefully, they are developing deep understandings of enzyme action that now need to be tested, reflected upon, and revised.  While continued exploration in the laboratory can provide that environment of reflection and revision the time and space limitations of a typical classroom likely prohibits a robust exploration.  This is where a simulation like the NetLogo Enzyme Kinetics can play a vital role in student learning.  Here the student or student teams can explore and manipulate all sorts of variables in a relatively short period of time.

Applying the NetLogo Model:

The NetLogo model is built on the same conceptual model for enzyme kinetics that we have explored before:


By Thomas Shafee (Own work) [CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons
 
Instead of generating an expression with Vmax and Km, though the agent based model assigns properties to the agents based on three “constants”.

Constant 1:  The rate of formation of the ES complex.

Constant 2:  The rate of dissociation of the ES complex back into E and S

Constant 3:  The rate of catalysis of the ES complex into E and P

You explore the model by changing these constants or changing the substrate concentration.  Changing the constants, changes the properties of the enzyme.

http://ccl.northwestern.edu/netlogo/models/EnzymeKinetics

I’ve put together a short video of that introduces how one might work with this model to create data similar to the data from the original wet lab.  You can find it here:

https://youtu.be/hPwqVlHvzMA

Here’s a M-M curve that I generated by changing the values of the constants and then seeing how those properties determined Enzyme rates/velocities at differing substrate concentrations.

In this curve I collected, 8 samples for each substrate concentration.

Here’s the data, generated by the model.  Looks a lot like the wet-lab data, doesn’t it?

And here is a curve fit to the Michaelis-Menten equation.  Note that the data from the NetLogo model has to be fitted to the idealized curve.

Note that the data from the NetLogo model has to be fitted to the idealized curve.

The thing is that I could go back into the Netlogo model and explore questions like, what happens if I lower the constant that describes the rate of Enzyme-Substrate formation relative to the constant that describes the dissociation of that complex?  Several questions come to mind.

Of course you don’t have to explore Netlogo as an extension or evaluation activity. You could have your students explore this spreadsheet from the Bioquest Esteem project:

Michaelis-Menten Enzyme Kinetics

Or if you are really ambitious you could have your students develop their own spreadsheet model like the one described in this paper from Bruist in the Journal of ChemEd.

Bruist, M.F. (1998). Use of a Spreadsheet To Simulate Enzyme Kinetics. Journal of Chemical Education, 75(3), 372.  http://biochemlab.org/wp-content/uploads/2011/06/Spreadsheets-in-enzyme-kinetics-Bruist.pdf

Or you could have your students explore the AP Biology Community’s own Jon Darkow Stella-based model for lactase enzyme activity:  https://sites.google.com/site/biologydarkow/lactase-enzyme-simulation  This is an HTML5 version created in the dynamic modeling system known as Stella.  

Practice, Practice, Practice (Curve fitting in the Photosynthesis Floating Leaf Disk lab)

To master any skill takes lots of practice–something we don’t provide enough of in academic classes.  We do in the performance and fine art classes but not so much in academics.  The excuse as to why not usually gets back to the extreme time limitation we face in the biology classroom.  Still with the right selection of lab topics skill practice is not only possible but highly productive.  For instance in this case, it turns out that the procedure, the data created, the data analysis, the curve fitting (to the same mathematical model) are all skill that can be applied to the Floating Leaf Disk lab, if the students explore how the intensity of light affects the rate of photosynthesis.

In 2015, Camden Burton and I presented some sample data sets from the Floating Leaf Disk lab at NABT.  Later I shared those in a series of posts on the AP Biology Community forum where a lively discussion on data analysis ensued.  If you are a member of the forum you can find the discussion here. 

One of the more problematic data sets we shared was data from a photoresponse curve experiment that explore how light intensity affects the rate of photosynthesis.  Here’s a photo of how light intensity was varied by varying the height of the stack of petri dishes.

 

 

Here’s the raw data for this lab using the lap timer on a smart phone:

 

The first step working with this data is to convert the lap times into cumulative times along with generating the descriptive stats.

 


Because the how fast a disk rises with this technique is inversely proportional to the actual rate of photosynthesis we need to convert this time into a rate by taking the inverse or the reciprocal.  And since this turns out to be a small decimal number with the units of float/sec, I’ve modified it by multiplying by 1000 seconds to get a rate unit of float per 1000 seconds.  The descriptive stats are converted/transformed in the same way.  This process of data transformation is not emphasized enough at the high school level in my opinion.

 


Graphing the means in this data table along with plus or minus 2 SEM error bars creates a graph something like this:

Which in my mind is a curve waiting to be fitted.  If you google something like “Photosynthesis Irradiance Curve” you’ll find a number of resources applicable to this experiment and guess what?  You’ll find that folks have been using the Michaelis-Menten equation to model the curve fitting.

I’ll let you explore the resources but here is the fit based on the Michaelis-Menten equation.  There is a modification to the Michaelis-Menten expression that we have to do for this particular lab.  Since this procedure actually is measuring the accumulation of oxygen as a product and some of the oxygen is being consumed at the same time for cellular respiration, we are actually measuring the net rate of photosynthesis.  To account for the oxygen consumed in respiration we need to add an additional term to the Michaelis-Menten equation.

I’ve changed the variables but the form of the equation is the same.  In the curve fitting that I have done, I have manually changed the value of R and let the solver vary Pmax and KI.

The fit for this set of data is not as good as we got for the catalase lab but it is not bad.

Interestingly, you can get a “good” fit to an exponential function as well–maybe even a better fit.  But, that is part of model fitting.  There is many biological reasons to consider that Michelis-Menten provides a model for photosynthesis but I can’t think of one for an exponential fit.  There are many ways to continue to modify the Michaelis Menten application to Photosynthesis Irradiance curves and you can find several with a bit of google searching.

Here’s one fit I managed in excel using the same techniques that we used earlier.

Here is a Desmos version you or your students can play with.

 

 https://www.desmos.com/calculator/dsfe5xfwyi


I think it is time to wrap this series of long winded posts up.  I hope, if you’ve read this far, that you have found some ideas to try in your class and I hope that despite the deep dive that an idea of how an increased emphasis on quantitative skills can also lead to an increase understanding of the content–at least it does for me.  Finally, I hope you and your students have a good time exploring data analysis—it really does feel good when the data works out like you think it should. 😉

 

Insect Trap Designs

This year, for the first time, I had my environmental science students design insect traps. They worked in groups to select their trap designs, create a budget and materials list. They also did some initial investigations on which type of insects would be attracted to each trap and why.

After setting the traps, students evaluated their trap designs. There were obvious improvement to be made, and some groups had time to try some improvements. Unfortunately, there was not enough time to do this well. (That will be changed next year!)

Too windy on the trail!

At the end, students created posters to evaluate their trap designs, and report what insects were collected from their traps. These posters were displayed next to their entire insect collections at Family Science Night. I wish I had better pictures!

I really enjoyed this project. Next year I will emphasize why each trap works for certain insects as a way to emphasize insect adaptations, anatomy, behavior, etc!