Category Archives: Science

3 Simple Ways To Build Scientific Creativity

In 1911, Elizabeth Kenny, a nurse in Australian Outback, was called upon to take care of a little girl who she thought had infantile paralysis. She wrote to her mentor, Dr. McDonnell, for advice who wired her back with a message to treat “according to the symptoms as they present themselves.”

Not realizing that the girl really had polio, Elizabeth started finding ways to alleviate the symptoms. She noticed that the girl’s muscles were very tense, so she used hot compresses which she theorized would help relax the muscles. The girl found instant relief from the hot compresses and they reduced her muscle spasms. Next, she saw that the girl could barely move her limbs. Once again, she hypothesized that the muscles needed retraining and increased blood flow. So she started a regime of motion therapy and massage (an approach that later evolved into physical therapy). The results were dramatic and the girl recovered and was able to walk again!

In comparison, the conventional approach to treating polio at the time was to immobilize the limbs by attaching splints which pretty much ensured that patients would not be able to fully recover their mobility. Elizabeth went on to treat many more polio patients, despite being rebuffed by the medical establishment. It took the medical community several decades to acknowledge that her methods of treating polio were indeed effective.

Not knowing that she was actually treating polio, turned out to be a blessing for Elizabeth. It led her to create fresh hypotheses based on what she observed, come up with creative techniques and test them.

Most advancements in science came by because of creative leaps in generating hypotheses or designing better experiments. Creativity plays an integral role in all real-world science explorations – from problem finding to generating and testing hypotheses. As psychologists, David Klahr and Kevin Dunbar who proposed the Dual Space Search in Science approach,  explain, “The successful scientist, like the successful explorer, must master two related skills: knowing where to look and understanding what is seen. The first skill – experimental design – involves the design of experimental and observational procedures. The second skill – hypothesis formation – involves the formation and evaluation of theory.

So, how do we build some of this scientific creativity among younger students?

Research in scientific creativity can be viewed as an interaction between general creativity skills and science knowledge and skills. Here are a few simple approaches to build scientific creativity among students.

Problem Finding

Problem finding in general is considered a core aspect of creativity, and it extends to all domains including arts, math and even science. Real-world problem finding is more predictive of creative achievement than standard measures of divergent thinking.

One way to encourage problem finding in science is to have students list problems they want to explore in a science topic. For example, give students an exercise to think of as many research topics as they can, on subjects like the behavior of ants or the growth of plants. The idea is to give them a chance to think of what they already know and discover areas they want to extend their understanding in. 

Hypotheses Generation

The ability to generate many alternative hypotheses is related to success in science. However, research shows that children tend to get stuck focusing on a single hypothesis. One approach to build the ability to generate multiple hypotheses is to present a partially-defined experimental scenario or setup, and ask students to generate as many hypotheses as they can.  For example, give students an adjustable ramp and different balls as the setup to explore connections between different variables like height, weight, speed and time. That could lead to hypotheses around what happens when you roll different weight balls or change the height of the ramp and so on. 

Scientific Imagination

Scientific imagination is one of the key aspects in the scientific creativity model proposed by Hu and Adey. Einstein had often mentioned how imagining himself chasing a beam of light gave him the insights that eventually led to the development of special relativity. The role of such imagination in science, which is different from creative imagination, is now considered valuable.

One way to build scientific imagination is to give students story writing tasks on topics like “what if there was no gravity” or “the sun is losing its light”. The goal isn’t to just write an imaginative story but to get students to use their scientific knowledge to guide their story. 

Asking Meaningful Questions in Science

In early 1820s, the French scientist and mathematician, Joseph Fourier, asked himself a very simple question: what determines the average temperature on Earth? Or in other words, when the Sun’s rays strike the Earth, why doesn’t the Earth keep getting hot?

Asking these questions made Fourier realize that the Earth’s heated surface emits invisible radiation (infrared radiation). And while he did not have the tools then to prove this, he also intuited that the Earth’s atmosphere plays a role in keeping the Earth warm. It took other scientists and their probing questions – like John Tyndall (what is the relation between density of gas and heat absorbed?) and Svante Arrhenius (how strongly is radiation absorbed by carbon dioxide?) – that finally proved that the presence of carbon dioxide and other greenhouse gases play a crucial role in warming the Earth.

Good questioning has always played a role in leading to creative breakthroughs in pretty much every domain from arts to sciences. It’s a thinking skill that underlies critical, creative and complex problem solving.

Unfortunately, research studies in science have found that not only does the number of questions students ask drop with grade level, fewer students ask questions of high cognitive level.

Simpler questions tend to seek or clarify factual information and are essential to build an understanding of the science topic. However, once there is sufficient familiarity with the topic, higher cognitive level questions can lead to a deeper understanding, creative and inventive applications and even transformation of the field. These “wonderment” questions try to find connections between concepts or extend the area by identifying additional aspects to explore.

Research studies have found that teaching students how to ask good questions can be quite effective. Students who received instruction or were provided a framework to ask research questions were able to generate more higher level questions than those who didn’t.

Model Based Inquiry (MBI), a more authentic way to teach science, also provides a natural way to structure the questioning process. A model, in general, is a representation of bigger system or phenomenon. It describes the different components, the relationships between the components and the mechanisms (that are often hidden) that underlie these relationships.

Another way to look at a model is that it tries to clarify three types of questions – what, how and why. The “what” questions correspond to the different components in the system. The “how” questions correspond to the relationships, or how different components affect each other. Finally, the “why” questions try to get to the bottom of how different relationships work.

All of these questions have the potential of changing the model thereby improving our understanding of the phenomenon. These model-based questions will typically lead to higher level, research questions in science.

Using the scientific model as a starting point to generate more high level questions can be an effective strategy in science education. As Professor Chin, who researches students’ learning approaches is science, says, “As educators, we know that the skill in the art of questioning is essential to teaching well. However, with the emphasis today on active learning, critical and creative thinking, skill in the art of questioning is also critical to learning well.” Using MBI has the potential to make this process more structured and less intimidating in the classroom.

Model Based Inquiry For An Authentic Science Education

During a period of six months in 1821, Michael Faraday conducted a series of well-thought out experiments that broke some of the earlier theories about electricity and resulted in the first electrical motor. A few months before that, Oersted had discovered that electrical current flowing through a wire, could affect the orientation of a magnetic compass nearby. Over several experiments, Faraday showed that the magnetic field produced by the electrical current was circular in nature, and that electricity and magnetism could be used to produce motion.

But equally important is the experiments he chose not to do. For instance, he didn’t run an experiment to see the effect of electricity on the magnet under different light conditions. Why? Because it would not make sense to do so given what scientists knew about the nature of electricity.

The existing models that scientists had about electrical current had nothing to do with light. The prevailing notion of electricity at that time was that it was a material fluid as opposed to an energetic condition and completely independent from magnetism. So the experiments that Faraday designed were carefully constructed to either confirm or refute existing models of electricity and magnetism. And that’s what led him to advance the field of electromagnetism.

Unfortunately, the Scientific Method (TSM) as taught in schools today often misses this finer point.

To do a science fair project students often pick a question from a pre-existing list or construct one hastily with no real rationale based on their understanding of how the system or phenomenon works. The emphasis is on following the steps in the scientific process which effectively makes the whole exercise a guessing game with no deeper learning attached to it.

In a study of science education in schools, researchers looked at how middle school students in a classroom were approaching inquiry experiments on plants. Students came up with several ideas like feeding the plant coke, using various kinds of dyes or different types of soil. However, the teacher never asked why any of those questions make sense.  As the authors explain, “Do these students have any reason to believe (or hypothesize) that the physiology of a plant will respond in particular ways to these features in their environment? Because such questions are arbitrary—i.e., make no sense without the context of at least a beginning model for understanding the phenomenon—then any hypotheses emerging from these questions are likely to be little more than poorly informed guesses.

In fact the most creative parts of science – generating hypotheses, theorizing from observations and designing experiments – get lost in the traditional approach of strictly following the scientific method.

So what exactly is Model Based Inquiry (MBI)? Using MBI allows students to developdefensible explanations of the way the natural world works”, and it uses four kinds of conversations (often in an iterative fashion):

  • Organizing Prior Information: Without knowing something about a subject, or in other words without a starting model, constructing any hypothesis is like a shot in the dark. So the first step is to list out any information you already have – what the different components and elements are and how they related to each other.
  • Generating a Testable Hypothesis: Once students have an initial model, the next step is to generate hypotheses that are grounded in the model. The idea is to generate competing hypotheses to test specific aspects of the model, that can help deepen understanding of the underlying concept.
  • Seeking Evidence: This part of the conversation is around figuring out what kinds of data to collect to test the model, which can then be used to support an argument.
  • Constructing a Scientific Argument: The final conversation which is often missing in practice, is to tie it back to the model to support or refute specific claims about the model.

While at a high level this may look similar to the scientific method, the focus on the model during the process makes a big difference.

The scientific method, while being a very useful tool for structured thinking, has the side effect of oversimplifying science in education and going further away from how real scientists actually reason and think in science. Incorporating MBI has the potential to change how students relate to science and engage them at a deeper level.  

How To Think Like A Scientist

Wilson Greatbatch was an American engineer and inventor, who had more than 150 patents to his name over his lifetime. His most famous invention is the implantable Pacemaker, which has saved countless lives since it came out. But it almost didn’t happen!

Greatbatch was working on a device to record heart sounds, when he accidentally installed the wrong resistor and realized that the device was now giving off rhythmic electrical pulses. He realized at that moment, that he had hit on something important. Pacemakers before that time were bulky devices that worked on power mains, but Greatbatch’s discovery showed that they could work with battery and could be made small enough to be implanted.

While this may seem at the surface to have been an accidental discovery, Greatbatch was really thinking like a good scientist. Kevin Dunbar and Nancy Nersessian, have studied scientists and their thought processes for many years, and have distilled the core thinking patterns that underlie creative scientific thinking. Here are a few strategies and techniques that they believe lead to better scientific accomplishments:

Unexpected Results

Accomplished scientists have often mentioned the role of chance in leading to a discovery. But what distinguishes great scientists from average ones is how they pursue the unexpected results. As Dunbar explains, a good heuristic to go by is, “If the finding is unexpected, then set a goal of discovering the causes of the unexpected finding.

To investigate an unexpected finding, scientists have to pay attention to the finding and recognize that it could lead to some new learning first. It turns out that some scientists have a tendency to make serendipitous discoveries. Sandra Erdelez, a scientist at University of Missouri, has been studying this for many years and found that some people, called the encounterers, have a tendency to stop and “collect” useful or interesting information they bump into. Based on their individual differences in bumping into unexpected information, she classifies people into three types – non-encounterers, occasional encounterers and super-encounterers.

Analogical Thinking

One of the most useful cognitive techniques frequently used in science is analogical thinking. Rutherford-Bohr’s analogy between solar system and atoms or Newton’s analogy between projectiles and moon helped those scientists construct a better model.

Analogies have helped with different aspects of scientific thinking like generating models, designing experiments or formulating hypotheses. As Dunbar explains, “We have found that rather than trying various permutations on a question, the scientists search for a similar problem that has been solved and seek to import its answer to their current problem.” The advantage of analogical thinking, is that it helps the scientists come to a solution quickly by avoiding iterative trials.

Imagistic Reasoning

Imagistic reasoning makes use of images to help in analyzing and understanding a phenomenon. For example, Faraday’s starting point in constructing his field concept was using an image to represent the lines of field like those that form when iron filings are sprinkled around a magnet. By using a more idealized representation through an image, he was able to capture the underlying model.  

Nersessian believes that imagistic reasoning, along with analogical reasoning and thought experiments are part of “abstraction techniques” and help construct a model of a scientific concept.

While most people are familiar with analogical reasoning, As Nersessian explains, “…there are numerous cases that establish the prominence of reasoning from pictorial representations in the constructive practices of scientists who were struggling to articulate new conceptualizations. Such imagistic representations have often been used in conjunction with analogical reasoning in science.

 

Research in over a decade has demonstrated the significance of these cognitive techniques and strategies in science, and should be included in science education.

We are excited to launch a new middle school science program in partnership with Positive Ally, starting this coming academic year. Our goal is to bring these cognitive techniques to the forefront to build deeper understanding of scientific concepts and help students apply their thinking in solving real world problems.

Thought Experiment: A Creative Exercise in Science

One day at the Cathedral of Pisa, Galileo who was still a teenager, watched a chandelier that a monk had just lit swinging in an arc. Using his medical training, he started timing the motion and discovered that even though the swing got shorter and shorter, the time of each swing stayed the same. That observation so excited him, that he rushed back home to experiment with strings and weights, and it eventually led to a life long fascination with pendulums and motion.

But one of his most interesting discoveries, one that was incorporated in Newton’s first law of motion,  was not the product of direct experimentation. It was his ability to imagine a scenario that was almost impossible to replicate in real life. It’s what Ernst Mach later called as a Gedankenexperiment, or a thought experiment.

Galileo realized that without friction, a ball rolled along a double incline plane will reach its original height on the other side just like a pendulum (Fig. a). He then asks to imagine what would happen if one side of the double inclined plane is made longer. The ball will then travel a longer distance till it retains its original height (Fig. b). In the limiting case of infinite length, the ball would continue rolling since it can’t reach its original height (Fig. c). This completed upended the Aristotelian view of motion that the natural state of a body is that of rest, and motion requires some force.

Thought experiments have played a significant role in the history of Science from Galileo to Einstein. Scientists expand knowledge of a concept, by creating mental models and running virtual experiments on them. In fact, cognitive scientists believe that people reason by carrying out thought experiments on internal mental models.

But more than that, thought experiments are essentially a creative exercise. Creativity at its core is about playing with models – changing different aspects or adding new associations – and iterating to find a better solution. Whether it is using SCAMPER to manipulate an attribute or reversing an assumption, creative thinking provides ways to manipulate mental models in a quest to discover breakthrough ideas.

As Nancy Nersessian, an expert on model-based thinking in Science, explains, “While thought experimenting is a truly creative part of scientific practice, the basic ability to construct and execute a thought experiment is not exceptional. The practice is highly refined extension of a common form of reasoning. It is rooted in our abilities to anticipate, imagine, visualize, and re-experience from memory. That is, it belongs to a species of thinking by means of which we grasp alternatives, make predictions, and draw conclusions about potential real-world situations we are not participating in at that time.

While the role of thought experiments in advancing scientific knowledge is undisputed, what is lesser known is its role as a pedagogical tool up until recently. After dropping out of the rigid school system in Germany, Einstein found the perfect school in Switzerland, where Johann Pestalozzi‘s methods in visual and conceptual understanding were used.

It was there that Einstein first engaged in a thought experiment that would make him the scientific genius of his time. As he told a friend later, “In Aarau I made my first rather childish experiments in thinking that had a direct bearing on the Special Theory. If a person could run after a light wave with the same speed of light, you would have a wave arrangement which could be completely independent of time. Of course, such a thing is impossible.

It’s unfortunate that over time thought experiments as a pedagogical tool have been dropped from science education. Students now spend most of their time learning facts and running predefined experiments as opposed imagining and framing their own thought experiments. Perhaps by re-introducing thought experiments, more students will find science engaging and stimulating, just like Einstein.