Tuesday, October 28, 2014

Computational Thinking

                Sengupta et al’s article sought to describe computational thinking as a scientific process that also involves inquiry and problem solving. Computational thinking was also described as a science as a practice theory, which includes modeling and representational forms. The article placed computational thinking into science and math standards, citing that their similar cognitive processes for reasoning.
                Grover and Pea’s article described computational thinking as information processing so that a solution may be sought out by a processing agent or through a set of instructions. The National Science Foundation has listed seven big ideas for computational thinking. The last big idea says that computing enables innovation in other fields or subject areas. Effective pedagogical approaches to computational thinking are still being developed; curriculum and assessment are still being developed.

                Between both articles computational thinking was described as a thought process that describes problems such that they may be solved simplistically, such as by an algorithm. Computational thinking should be described as a science as it involves inquiry and creation of ideas and artifacts. Also, both articles ask important questions such as what is the best pedagogical approach to computational thinking and how can instructors scaffold computer science so that the content is challenging for students with little to large background knowledge. Both articles agree that pedagogical approaches to computational thinking are still being developed and that the most effective ways are still yet to be sought out. Computational thinking has a great importance in how frequently technology is used currently. Both articles stressed the importance of preparing students to be problem solvers; computational thinking develops this skill set as a science.


Grover & Pea’s article, Computational Thinking in K-12: A Review of the State of the Field, focused on the definition of computational thinking, the application of CT within K-12 schools and concerns with each, and the next steps for improvement of CT application and broadening of discourse.  The Royal Society provided the concise definition that computational thinking is the “process of recognizing aspects of computation in the world that surrounds us,” and using knowledge and tools from CS to “understand and reason about both natural and artificial systems and processes.”  The current CT within schools focus on a variety of systems, including abstractions and pattern generalization to debugging and systemic error detection.  Grover and Pea touch upon the controversy and debate surrounding where to place the potential curricula and the amount of emphasis placed on it.  Ultimately, Grover and Pea conclude that researchers are still in the process of forming the specific curricular associated with CT and have mainly focused on defining it. 

The Sengupta, et al article focused on the various practices associated with CT, their relationship to science and math, and their integration into K-12 science topics.  Through looking at past research and evidence, the researchers promoted a specific learning environment that fosters the joint development of a scientific topic and the computational thinking associated with these higher level science ideas.  This learning environment was determined to include a specific type of programming, in this case agent-based to help promote both modeling and simulations of the science topic and CT. Finally, Sengupta outline four major design principles for integrating understanding and development of science and CT: 1.  Support low-threshold and high ceiling activities, 2. Design programming, 3. Support algorithm visualization and 4. Sequence learning activities.  Findings from the study supported these ideas and showed that students greatly learned from this teaching environment and scaffolding, especially one-on-one, was very beneficial.  

Major themes:
·      CT will create students capable of inquiry (coming up with their own questions) who have the ability to solve these problems in a logical way
·                          CT involves using CS, inquiry, and reasoning skills
·      CT should be used as a medium for/integrated into teaching and understanding other subjects.
·      CT is an active process-not just a technological machine doing the work-where modeling, simulations, and real world applications are stressed and performed
·      Incorporating CT into schools is highly beneficial for all students

One aspect that I really liked and Grover and Pea touched upon it was the fact that CT may and can be used as a medium for understanding other subjects.  Finding connections between concepts across disciplines is critical for complete understanding.  This leads to one issue/controversy that the researchers faced: whether or not computational thinking should be a separate subject, within a specific discipline or spread out over many.  In a world where technology is so prevalent, I can definitely see the pros to CT being its own separate subject.  This way, more emphasis and time can be given for real world applications and hands-on projects, opportunities that may fall by the wayside when CT is incorporated within a specific topic.  However, with this mindset, how would you go about incorporating all the subjects and their connections into one class?  However, including it in the maths and sciences is essential, whether within the same class or a separate one. 
Another question I had regarded resources.  In schools that do not have many funds or lack certain technology, how can CT, using CS, be effectively carried out?  While deSessa mentioned that many schools do not have this problem, there are schools that lack sufficient funds or are overcrowded.       

Finally, I just wanted to say that I liked deSessa’s reasoning, in the article, Computational Media and New Literacies-The Very Idea, and how he/she explicitly states what computational literacy is- “intelligence achieved cooperatively with external materials,” not just solely by the technological machine-and separates it from the term computer literacy.  Through defining these terms and stating the differences, I was able to see the greater importance CT held, the active nature involved in completing CT, and how it applies to many other domains.


Grover and Pea review what has been done and what is lacking in CT K-12 education. A need for CT education has been articulated and somewhat accepted, meaning that they know what aspects of computer-related education are most important for K-12 education. Research is needed on how students best learn CT (i.e. pedagogical content knowledge in CT.) They posed many questions including where in the curriculum it would fall, who would teach it, whether it would be integrated with other courses or stand alone.

Sengupta et al described a specific means for incorporating CT in the K-12 science classroom. They proposed the integration of core science courses and CT, noting the similarities in thinking, problem solving and most importantly modeling. They then proposed many details on specifically what types of programs are best for K-12 education and why. Finally, they presented a study that showed content knowledge gains in the core sciences through using CT.

Both articles stressed the importance of computational thinking, not computer programming specifically. I find this important because the kind of thinking described in the articles in very transferable. While computers are ubiquitous in society, not all people need to be skilled programmers.
My concern with the push for CT in all science classrooms is twofold. First, it requires students to be in a computer lab for class. Most schools only have a few computer labs, and it could be very difficult for many teachers to try to implement CT in their curriculum simultaneously.
Second, I do not think that there is a qualified teacher force to implement these ideas. The research presented showed that students learn best when they are taught one-on-one from a professional researcher, and that without these added scaffolds, improvements in disciplinary gains are very small. Teachers would need to be trained significantly on CT for it to ever enter the science curriculum.
These papers made me realize that I should probably have taken some kind of computer science course. I felt very lost in the academic language at times. I also know that I am completely unqualified to teach CT. I know that to be able to implement it will require significant professional development.

Computational Literacy

            The readings this week focused on the implementation of computational thinking in high school curriculum.  In Grover and Pea, the authors presented a synthesis of contemporary definition and debate of computational thinking as a discrete topic with little reference to it’s implications for other subjects.  Sengupta et al. take the problem solving mechanisms of computational thinking one step further and envision their integration into science curricula.  Using kinematics and ecosystems as sample contexts, Sengupta et al. walk the reader through their “three world” system and clearly map the ways in which carefully scaffolded computational thinking can improve student comprehension of complex systems.     
            I found the argument in Sengupta et al very compelling, and think that the process of abstraction and pattern recognition can be readily incorporated into Lehrer’s modeling cycle.  During the “construction world”, students can define and create their own models, test them in the “enactment world”, and finally modify them during the “envisionment world”.  Besides readily supporting active modeling, computer models can additionally help students envision complex interactions among elements in a system that would otherwise require many physical and two dimensional models to represent.  While I think that physical models are irreplaceable in biology, computational models can provide the critical missing link of dynamics and interrelation that my students struggled with in the interviews.  I’m excited to learn more about the programs in class and potentially include the ecosystem program in my food web curriculum to help explain trophic cascades and keystone species. 

            In comparison, the Grover and Pea article seemed a little limited in it’s vision for computer science in the classroom.  I think we are a long way off from compulsory computer science in high school, but agree that computational thinking is very valuable in modern society and across many disciplines, so it makes more sense to me to incorporate computational theory into existing math and science courses.  Similar to highlighting engineering in biology, chemistry, and physics like we talked about last week, incorporating computer science into the other sciences can help students see the interconnectivity between sciences and the application of computer models across disciplines.  By exposure to computer programming in a well-scaffolded way like Sengupta et al. suggest, you can counteract the issues of challenge and disinterest that Grover and Pea raise.  When students see that computer models are fun, accessible, and helpful for a wide variety of problems, they may be more likely to choose a computer science elective and subsequently achieve the goals that Grover and Pea set for computational literacy, avoiding the common challenges to novices and skipping the unnecessary pandering to ‘sexed’ interests like gaming or sewing.        

Computational Thinking

Computational Thinking
(Grover & Pea, Sengupta et al.)

The Grover & Pea paper essentially gives insight into where computational thinking is now in K-12 education and gives an overview of some gaps in research as well as priorities for future research.  The article gives many viewpoints of how people define computational thinking and what it entails.  The article brings up questions about how to assess computational thinking and what teachers need to develop into successful computer science teachers.

The Sengupta et al. paper draws on the similarities between computational thinking and developing scientific knowledge through modeling, reasoning and problem solving in many science and math disciplines.  The purpose of this paper was to show, “That the development of scientific modeling in K-12 curricula can be synergistically supported by a science curriculum that is based on computational thinking” (pg. 3).  This paper goes into detail about a possible framework that incorporates agent-based computation into K-12 science education.  It focuses on two disciplines as examples (Physics and Biology), and explains how and why CT can and should be integrated into science education.  The paper then explains a pilot study and examines findings and possible implications.

Both of the readings this week focus on computational thinking and its integration into K-12 education.  They also mention modeling and simulation as key parts of computational thinking.  The idea of cross-cutting concepts from last week is also mentioned as the readings describe computational thinking as much more than just programming. The sengupta et al. paper goes into much more detail and mentions ideas aligned with what we have been learning in class about modeling, scaffolding, representations, inquiry, revision, and more.  The paper even uses Lehrer & Schauble as a reference to a science as practice perspective! The authors clearly understand modeling and how we view it today; I wonder how many questions or discussions these Vandy authors had with Lehrer while composing this research?

I have used computational modeling in an ecology class before to explore population dynamics and found it to be extremely useful.  We basically had a program created that reflected the equation used by biologists in population dynamics and then explored what changing each variable would do to the future population.  The program let us change variables and then it would graph the population over time and we could visualize what effect each variable had on the population.  I think integrating computer simulations and models into K-12 science education would be an effective way to scaffold learning and understanding about certain scientific concepts.  However, I don’t think I buy into the idea of learning the actual programing behind the models we are using.  I see the potential value of learning the programming as well, but I think it would add an unnecessary challenge to any classroom.  The important thought process might be there, but the programming aspect of class might overshadow the content specific knowledge of biology, or chemistry, or physics.  The thought of spending 5 weeks of class to learn and explore a new computer-programming program troubles me.  I also think a lot more research needs to be done on CT before implementing it in the classroom.


Computational thinking is a burgeoning subject/field, and that to not give the opportunity for K-12 students to learn about it is definitely destructive to their futures.  Computer literacy has become so much more valued not only in the CS field but also in many others (hello, STEM careers).  Last week, some local news channel had a story about the new common core/standards that were being implemented in Tennessee, and they interviewed a woman about how she disagreed with there being computer literacy added to the curriculum.  Her sole reasoning was that she was concerned about the students who did not have access to a computer at home.  Although this could look like a valid argument to some, I completely disagree because students already have to do internet research, homework assignments, and paper writing with a computer regardless of computer science work.  Plus, students without computers are the ones who would benefit most from more work with computers since they do not have regular use and interaction with them at home.
I like how Grover & Pea discuss the use of fantasy in getting students engaged with CT; game design and robotics are really fun ways to introduce some fundamentals to otherwise uninterested students.  I would also like to see more information/investigation about the idea of computing as a medium for teaching other subjects.  Modeling software in science could be huge for this; the computer program we used in class to see the changing moon phases comes to mind.  The subject of astronomy in general would benefit greatly if students had greater CT because it could help simplify advanced math and physics, so that topics are easier to comprehend without delving into complex computations.

            I am not sure if I completely agree with Sengupta et al’s point that CT should be integrated with learning in the math and science domains.  A separate course, like an introduction to computer literacies, would be helpful so that students have a familiarity with CT and possibly go more in depth than a science class could.  However, I do agree that aspects of CT should be interwoven with the curriculum.  The abstractness of CT is perfect for engaging students in higher levels of thinking, and will hopefully help them to connect classroom models to their physical phenomena with greater ease.


The readings for this week discuss the importance of incorporating computational thinking into science for K-12. Grover and Pea review the research that has been the response to Wing’s idea of using computational thinking in school curricula to help support science literacy and raise computational literacy. Sengupta offers a theoretical framework on how to incorporate computational thinking into the classrooms.

As with many of our readings this semester, modeling and representation is imbedded in teaching, or using, computational thinking and literacies. Computational thinking also includes similar principles of teaching engineering practices that we read about last week. Engineering and computational practices use models and representations to learn and then reason and explain possible ways to solve a problem. For older students, computational programs would allow them to have a better understanding of complex ideas that includes connecting different levels or ideas (abstractions) to make a whole picture. Computational thinking could be a good method to support the learning of explanation and/or argumentation, which are important parts of science literacy, according to Sampson and Reiser.

Last weeks reading, A Framework, put emphasis on building up a child’s knowledge throughout their time at school. Sengupta seems to agree with this, and suggests that computational thinking should be introduced in primary schooling, and then built upon. Computational thinking also appears to be a potential tool to carry and build crosscutting concepts across subjects, as it would also be supportive in mathematics. Sengupta and Grover mentioned that computational thinking could also help in the designing and scaffolding of lessons. Students would be able to create their own questions, based off of prior knowledge, and then designing their own ways to solve them. Using computational thinking as a crosscutting concept, or to support crosscutting concepts, would make more meaningful connections from prior knowledge to the new concepts students are learning.

Literacy in computational thinking is important in future classrooms, as technology is a large piece of our society and helps scientists in analyzing or modeling their findings. However, I am not sure of the qualifications I have, or others, have to teach this along side their curriculum. It sounds like a few of the programs could be more complicated than others when programming. How would teachers be trained for this type of instruction? For example, I have never taken a computer science class. What training would I have access to, or have to take in order to successfully be able to teach in my classroom? Or, would I have to bring in another teacher who is more familiar with programming and these programs? Also, how much time can be dedicated for the children to learn how to use the programs?