Welcome to 2017 Video Showcase.

This is Osman Yasar, the presenter of "How a scientist thinks."

Because of the short length of the video, I had to leave out a great deal of information. Any one interested in more details, please feel free to contact me at oyasar@brockport.edu. I have a full article on the topic. Thanks.

What is an example of how this is implemented in the classroom?

It is such a great project that has a significant impact on teacher education and students. Thanks for sharing with us.

Thanks, Ibrahim.

Hi Osman,

I wonder whether you could "unpack" your diagram a bit--I'm not sure I understand how all the rings relate to each other. How does your model drive your teacher training and/or curriculum resources? 

I'm also curious about how your thoughts about ordinary thinking and scientific thinking connects with Piaget's descriptions of children's natural tendencies, even in infancy, to observe the world, explore it, build up ideas (schemata) about it, and revise those schemata in the face of additional information/experiences?  

Osman

Quite an interesting video, and yes, you are packing much in there! I look forward to your feedback to Lynn's questions above. From my POV, a couple thoughts jump out...

 

First, I would agree with your premise that all individuals, even at the youngest age, use and develop models to make sense of the world around them. They are not tabula rasa, but develop internal explanations that work for them (some call these misconceptions, others call them preconceptions). There's a large body of literature documenting these known preconceptions in science education, such as confusing speed and velocity, heat or temperature or reasons for the phases of the moon, or why our seasons are different based on the proximity of the Earth to the Sun across the year, versus the tilt of its axis with direct/indirect light, etc. The National Academies of Science, Engineering and Medicine and the National Research Council have published many works, e.g., How People Learn/How Students Learng, that discuss this "conceptual change" approach to learning and indeed there is much work about "uncovering" these student preconceptions (or their existing models) and challenging them to see if their model holds up, if not....getting them to wrestle with it. Research found that preconceptions are deeply seated, resistant to change, and hard to overturn. Often times students may parrot back what they know the teacher wants to hear, while still maintaining their "model" I'm sure you've seen the many videos about this (quite famous now known as the "Private Universe" series). See: https://www.youtube.com/watch?v=TrXaQu_qGeo

 

In the new K12 Framework for Science Education (2012) from the Academies and the Next Generation Science Standards-NGSS (2013), they concur with your sentiments about facilitating deeper learning of the core ideas of science through engaging students in eight science and engineering practices, of which one is "Using and Developing Models" See: http://ngss.nsta.org/PracticesFull.aspx. You are in good company!

 

I think your schematic represents some complex idea or phenomena (or the model) at the top of the image, where learners may examine/test their model or "unpack" the phenomena (analysis) into smaller "chunks" running across the bottom of the image, AND/OR through a cyclical process, they may synthesize or "pack" the smaller chunks together into a larger construct or complex model that explains the smaller chunks? I could be completely wrong here...so please correct me.

 

Like Lynn asks, might you provide if possible (briefly) an example of how your "model" (no pun intended) works within a larger unit or curriculum?

 

PS: The NGSS espouse that deeper learning of the core ideas of science occurs as students ask their own driving questions about a phenomena, design and conduct investigations to answer their questions, these investigations generate data, which they then use to as evidence to support their models, arguments, and explanations...through teacher facilitated student-student discourse and sense-making. Does your program work in a similar manner?

 

I agree strongly with the premise that understanding scientific thinking and the scientific method is key to the appreciation of science. Fantastic that you are building curriculum resources for K-12 classrooms. 

do you believe that using meta-reflection (what is science, how does science work) is more effective or do you think that course-based research experience followed by a reflection on why something didn't work well is the more powerful approach for students to become aware of what scientists do?

Hi, Anne.

My answer to you lies in the pyramid on the 3rd slide.

Just like the evolution of matter and information, our thinking is cyclical between generalizations and details.

The top of the pyramid is where we meta-cognitively know what we know in terms of experience and details (bottom part of the pyramid). We should not stay neither at the top nor at the bottom. One who oscillates between these two ends (deductively and inductively) is using the full capacity of the mind. So, teaching students how a scientist thinks and how they can make that a habit of mind would give them the top view, but it needs to be supported via experience by swinging down the pyramid in an ongoing and cyclical fashion. In other words, we need to do it both ways, but currently, I think, inquiry-based teaching appears to be a one-way (inductive) approach. Deductive and inductive teaching needs to be combined and a tool that does them together is modeling and simulation. There may be other curricular tools.

For more details, please read my article at:

https://app.box.com/s/1p4rcrro7fi4bfg23naflie1a...

 Hi Osman,

I'm not sure I understand how teaching/learning grounded in inquiry is only an inductive approach--can you elaborate? I guess I'm finding it a bit challenging to categorize all thinking as either inductive or deductive, and I'm wondering whether your attention to the "information processing" (computational?) aspects of taking in information is somewhat different than other articulations--like the NRC book that Albert referenced earlier. And speaking of other articulations, it doesn't seem that you resonate that much to Piaget, but you do find Vygotsky useful--what about the notion of ZPD (or other aspects of activity theory) that does seem useful to you?

Hi, Lynn.

I wrote that note in a hurry and used a broad brush to paint my response. Today, I tried to write long answers but it got cluttered this time as you can see below. So, my suggestion is to read my paper at: https://app.box.com/s/1p4rcrro7fi4bfg23naflie1a.... I am gratefuyl for your interest and will be happy to continue this conversation.

I did not mean to generalize inquiry-based teaching as only being inductive because it depends how it is done. Historically, both deductive and inductive approaches have been used at all levels of science education. The deductive approach to instruction entails the teacher introducing a new concept or theory to students by explaining it first, then showing an application or two of the theory or concept, and wrapping up the instruction by affording students an opportunity to apply the theory or concept by completing homework problems. This has been and continues to be the traditional approach to science instruction, and it often leads to apathy and the eventual attrition of students. The inductive approach to instruction, by contrast, first presents students with a problem, a case, or data from an experiment. Students are then guided to explore underlying facts, issues, and the like. As the culminating step, students are led to acquire on their own an understanding of the underlying concept or organizing principle. Inquiry-guided learning, problem-based learning, and project-based learning are all among forms of inductive instruction. So, while empirical evidence suggests that the inductive approach to instruction fosters greater intellectual growth, prudent educators should take advantage of both deductive and inductive approaches of teaching. At least one concern is that constructivist and unguided learning works only when learners have sufficiently high prior knowledge to provide “internal” guidance ^{(Kirschner, P. A., Sweller, J. & Clark, R. E. (2006). Educational Psychologist. 41 (2), 75-86.)}

Refs:

J. Bransford, A. Brown, and R. Cocking, How People Learn: Brain, Mind, Experience, and School, Nat’l Academy Press, 2000.

M.J. Prince and R.M. Felder, “Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases,” J. Eng. Education, vol. 95, no. 2, 2006, pp. 123–138.

M.J. Prince and R.M. Felder, “The Many Faces of Inductive Teaching and Learning,” J. College Science Teaching, vol. 36, no. 5, 2007, pp. 14–20.

 

There are other forms of reasoning involved in scientific (and ordinary) thinking as I explained in my paper at https://app.box.com/s/1p4rcrro7fi4bfg23naflie1a.... These include:

1.     Problem solving

 

 

6. Reasoning à

6a. Deductive Reasoning

2.     Design and modeling

6b. Inductive Reasoning

3.     Hypothesis testing

6c. Abductive Reasoning

4.     Concept formation

6d. Casual Reasoning

5.     Conceptual change

6e. Analogical Reasoning

I argue that the essence of these are associative (+) and distribuitive (-) storage. retrieval and processing of information by a distributed network of neurons. Inductive and deductive thinking are examples of such processing.

Finally, I meant ZPD when I mentioned Vygotsky. I need to read a bit more on Piaget.

The link between a) associative and distributive storage, retrieval, and processing of information at the most fundamental level, and b) cognitive functions have been shown by some researchers such as:

King, R. D. (2011). Rise of the robo scientists. Scientific American, 54 (1), 73-77.

Langley, P. (2000). Computational support of scientific discovery. International Journal of Human-Computer Studies, 54, 393-410.

Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to Grow a Mind: Statistics, Structure, and Abstraction. Science, 331, 1279-1285.

Yang, Y. (2009). Target discovery from data mining approaches. Drug Discovery Today, 54 (3-4), 147-154.

Additional refs:

Thagard, P. (1999). How scientists explain disease. Princeton, NJ: Princeton Univ. Press.

Thagard, P. (2012). The Cognitive Science of Science. Cambridge, MA: The MIT Press.

Hi Osman

You have definitely provided a lot to think about! Thanks for your  thoughtful elaborations. I look forward to musing on them further!

Lynn