Buehl Reading Comments

Buehl makes an interesting point about a student's "match-up" with the author when they are presented with domain-specific texts. Does the student match up with the author's experience, vocabulary, and conceptual knowledge about the topic at hand? As future science teachers, I think that this awareness will be particularly important if we are trying to introduce our students to scientific texts or research; after all, most scientific conceptual knowledge is not necessarily intuitive, and the students are not incredibly likely to come across scientific vocabulary in their everyday life. That being said, how can we help ease our students' transition into scientific literacy? This question might be the basis of Buehl's discussion and inclusion of strategies for frontloading instruction.

Buehl also mentions that students of poverty may have more significant knowledge gaps due to the lack of availability of out-of-school resources. While this isn't too surprising, it does affect how a teacher needs to modify their teaching styles in the classroom. We often think of the American education system as a general "equalizer," where students of any background have the opportunity and resources to succeed. However, according to Buehl, students with greater knowledge gaps can quickly become apathetic or demoralized if they see themselves as falling behind with respect to their peers. How can we make a more equitable school experience for students of all backgrounds?

Computation Thinking

When I was in third grade, would always love to play Reversi, a simple board game where you try to capture opponents pieces by surrounding them with yours (kind of a simplified version of Go). For whatever reason, I distinctly remember the side of the game box advertising it as "minutes to learn, years to master". Using this sentence as a segue, I would like to relate this to the concept of "low floor, high ceiling" found in the Grover and Pea article. I've taken my fair share of CS classes, even going so far as to tinker around with developing an app with one of my friends. It really is pretty easy to get the ball rolling with a "hello world" program, but programming offers so much growth all at the fingertips of the user (my friend went on to study CSE at MIT, so there's that). I think this sort of topic is very helpful, especially when considering the wide range of aptitude found in a typical classroom. This quality could also be extended to other domains. In chemistry, for example, how can I as a teacher implement strategies that allow for this sort of opportunity for growth? 

Both Grover and Pea as well as Sengupta et al. discuss the benefits of thinking like a programmer when approaching a program. I've heard that philosophy and CS are strangely intertwined. While counter-intuitive, it does make sense; both philosophy and CS require approaching a problem or idea with an open mind, logical, sequential steps, and a comprehensive organizational hierarchy. However, this thought process could be applied to almost any problem, not just CS or philosophy. In lab, for example, a problem or issue might arise an answer for which hasn't been found in literature. Solving this problem requires thinking from a variety of angles. A good chemist will not think just of one variable, but many, and how changing a single step in a procedure might affect the outcome. Or, sort of the reverse, a good scientist should be able to look at a problem holistically, and synthesize all relevant data into a plan or path of investigation.

Week 12 Readings

Both of these articles talk about introducing computer programming into K-12 education as a way of helping students learn math and science.  I have some reservations, which I’ll go into in a minute, but on the surface, I think this is a great idea.  Part of my undergraduate background is in bioinformatics, which is essentially molecular biology meets computer science – gene and protein sequencing, protein structures, comparative analysis, pathway analysis – all of them fall under the bioinformatics background.  Having students use computer programming, or as the Sengupta paper called it, agent based programming, to investigate the unique challenges and phenomena observed in biological systems could be very interested.  My reservations lie in the fact that I am not a computer programmer.  We have talked quite a bit in this class about the importance of developing expert thinking in our students; however, in this regard, I would be a novice, potentially even more so than my students depending on their own experiences programming.  Also, as the Sengupta paper notes, it is very difficult to learn how to code and would tie up valuable class time if I had to teach them from my limited knowledge of Python and R.  However, the visual interface systems described in the Sengupta paper would probably be very helpful in alleviating these difficulties. 

The other thing both the Sengupta and the Grover articles discuss is the importance of Low Threshold, High Ceiling activities when using agent-based modeling.  I highly agree with their recommendations, especially in our current digital age.  It is likely that at least some of our students will come in with some prior knowledge of how to program while others will know nothing.  Activities that are able to serve both ends of the spectrum will go a long way in successfully using these strategies in our teaching.

Week 12 post

The readings this week can best be summarized in my opinion in  4 principles given by  can be Grover and Pea. These  guidelines allow students to broaden their horizon especially in the science fields. This added knowledge opens up the door for future government contracts, work in the private sector and opportunities to enter a salary job.  These skills also are so important to the way a child develops that they have to be  instituted .
1. Computing is a creative human activity. This one is elf explanatory but helps to emphasize how basic this skill is and sadly many are not even proficient in it.
2. Abstraction reduces information and detail to focus on concepts relevant to understanding problem solving. We deal with this every day from test to essay to reports. The sooner this skill is mastered the  better off test scores from unit test to the SAT and ACT will be for them,
3. Data And Information facilitate the creation of knowledge. In research fields such  as science and math we are so data driven. This skill allows you to  pick out the meaning less data and focus on the meat of the text .

4. Digital devices, systems, and the networks that interconnect
them enable and foster computational approaches to
solving problems. The United states has made a huge push to becoming a technical giant in the next 20 years . This base allows us to bring overseas tech and computing jobs back home and placed in sites such as Silicon Valley in California and other technological centers.

 

 

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Computational thinking

Grover and Pea explored the rationale for incorporating computational thinking into the K-12 curriculum. The authors explained the varied perspectives and evolving definitions of computational thinking in order to develop a rationale for its inclusion in main stream classrooms. CT “ involves solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science.” Although it is clear that computer science is pervasive in today’s society, it is still unclear how CT should be included in school. For example, should it be taught as a separate subject, or integrated within the existing curriculum? Challenges in incorporating CT include teacher training, development of pedagogical content knowledge, and the need for gender-neutrality. Additional research is needed to explore developmentally appropriate ways to teach CT for varying ages and skill levels.

Sengupta et al. make the case that not only is computational thinking and important skill in its own right, but that it should also be used to teach students how to develop and interact with representations in science and math. Integrating CT into science curricula can effectively deepen students’ understanding of natural phenomena. Agent based computer programs like StarLOGO have a “low floor and high ceiling” – allowing easy access for beginners as well as enrichment opportunities for advanced learners. These types of programs are effective tools for introducing CT through science modeling activities. Thus, students can engage in CT and scientific practices at the same time.

I personally feel that that it is very important to incorporate CT into the curriculum because so far knowledge in computer science has not been equitably distributed. For example, although my elementary school had a “computer science class” this was limited to learning how to type, how to make power point presentations, and how to surf the web. No mention was made of programming or any type of CT. This uneven distribution of knowledge/skills is reflected in the gender gap that exists in the CS industry. Not only is CT an important skill for the future, but it is also a good way to develop problem solving and critical thinking skills. The latter is especially important if today’s society is becoming increasingly reliant on technology.

Computational thinking and how to get it in the classroom

Week 12: Computational thinking and how to get it in the classroom

This week the readings focused on the idea of computational thinking (CT). CT is a “thought process involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent”. Which sounds complicated. If you break it down, it is basically what science research does. As a scientist, I formulate hypotheses, and then conduct experiments to study the hypothesis, while representing my findings using data processing and modeling. There is a strong push at the moment to teach this concept to students because the same techniques used in computer programming can be applied to STEM teachings. There are seven key points to computational thinking that focus around computing and programming being a creative process, by removing abstract ideas one can focus on what is relevant, by using data one can create knowledge, and by using algorithms, programming and digital devices one can solve problems. The final point is that computing can be applied to many different fields of thought, including the sciences, humanities, arts, medicine, engineering and business. I can definitely see how this would be beneficial for developing and fostering students as adept problem solvers. Most students don’t know how to address a problem or where to start when asked to solve a problem, by teaching students computational thinking skills they would be better modelers. There is a huge push right now in science research even, for more quantitative over qualitative research and computational thinking addresses this issue. By focusing on the data and removing the abstract, a scientist or student is better able to access a problem and work towards solving or just understanding it. The Sengupta paper focuses on how to get computational thinking, along with modeling, taught as the standard. They propose a framework for changing the K-12 science and math curriculum to focus on CT and modeling based techniques. The only issue I see if having such a heavy focus on computer based teaching/learning. For me, science is very hands on. I experience science in the real world and don’t want to focus on teaching in on a computer. I may use virtual labs in my classroom occasionally when there is no other option, but I don’t want that to be how I am forced to teach. So as much as I think the concepts of computational thinking can relate to science teaching, I don’t want the computational/programming/computer driven focus to take over.  

Reading, 'Riting, 'Rithmetic, and 'Rithms!

Grover & Pea’s article introduces various definitions of computational thinking and explains how these definitions have developed and evolved over the years. The article then gives reasons for the legitimacy of computation thinking in K-12 curriculum and provides examples of CT implementation in schools.

The Sengupta article was more specific in the definition of computational thinking and gave ideas for integrating CT into a traditional science classroom. The author emphasized the importance of “design-based learning activities” in keeping with the “science as practice” mentality that ties back to our discussions of modeling. Sengupta emphasizes that computation can be used to teach basic science and math concepts such as graphing and rates, thereby integrating CT into lower level classrooms than previously thought practical, without taking additional time to carve out an additional class period. However, Sengupta also notes that CT can be expanded and complicated for those students who require a more advanced knowledge of computation. To whoever is reading this: did you notice how Sengupta gave Amanda a shoutout at the end of the article?!

Computational thinking is a valuable addition to school curriculum that provides students with a useful outlet to build conditional logic, algorithmic thinking, and abstract creativity. Today there are more and more jobs that are either solely computer science, or rely heavily on programming and general computer knowledge. I can speak from personal experience about the dangers of withholding computer education from students at the secondary school level. When I graduated high school, I did not know how to use Microsoft Excel, and entering into the biology major in college, I was expected to use Excel heavily for both data entry and statistical analysis. My grades suffered heavily from the burden of spending hours trying to extract information from a simple computer program that I could not use. As I learn more about computers and programming, I am more and more convinced that had I been introduced to CT at an earlier age, I probably would have pursued a CS major, or at the very least have been more prepared for the digital demands that 21^st century science departments place on students.  To not prepare students for this would be to ill-prepare them. Even if their future jobs do not involve programming, all students can benefit from the ability to think in a conditional, algorithmic, abstract and creative mindset.

The sad truth is, I don’t know the meaning to those key words I used in that last sentence (conditional, algorithmic, abstract) because I have such a poor CT background. This reading has been especially difficult for me because, unlike other science education concepts, this is one that I just cannot visualize, having no experience with the field.

Week 12 readings

This week we will be talking about computational thinking. As Jeanette Wing describes it, computational thinking is the “thought process involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.” I thought this description perfectly defines the scientific practices that we’ve been learning all semester. It applies to the practices because first the students have to identify a problem, identifying necessary parts through the process of “abstraction” where they learn to think like experts in peeling away superficial features to put only relevant and important elements into programming. From that students can try to make a model through programming to make sense of data and then make their own representation from their outputs. I think computational thinking could be a really good complement to science learning, especially in situations such as learning about kinetics or ecological systems as described in the article. Personally the only sort of computer programming I’ve done was AP computer science in high school where we worked with C++, a programming language. It was text-based and very abstract and while it helped us think more logically, it felt like an end to itself instead of a means to help us understand more about other subjects, such as science. In essence it was not very cross-disciplinary. However, I question how to integrate computational thinking into a science classroom. I have not used any of the programs described in the articles, so I don’t know how much of it consists of “true” programming versus plugging in variables and adjusting sliders. I wish the articles had given us an example of computational thinking forming a part of a science classroom in a more practical, day-to-day sense. However, the idea of it is very attractive to me indeed.

Ray's Week 12 Memo

Grover & Pea start us off by towards towards a definition of CT (Computational Thinking). Many tasks and features of CT are listed, but the core revolves around information process and developing knowledge in constructive ways using data and programming skills. A couple classes ago we talked a lot about programming as a creative or artisitic activity, and Grover & Pea make two key points in the introduction that zero in on this: 1. Computing is a creative human activity, and 5. Programming is a creative process that produces computational artifacts. There is a lot of value, I think, to presenting CT as a creative process, a means by which the student (or programmer, engineering, whoever) creates something from their own mind and then has practical purpose. Offering this to K-12 students may be a strong foundation for the framework. Grover & Pea then move onto discuss some of the difficulties of this introduction of CT to K-12 classrooms, not the least of which being assessing CT, involving girls in CT, and finding the adequate resources to bring students along an introductory level of CT. This is where a lot of the matieral from Sengupta, et al.’s paper helps by theorizing an effective framework for integrating CT.

            An early concept discussed in that paper is abstraction and generalization. The creative skills needed to effectively apply CT requires a mastery of abstraction and imaginative visualization, especially when planning CT solutions which is a core skill. This is the adhesive that holds together the Model of Scientific Phenomenon, which is visualized on page 11 of our PDF document. The visualization well summarizes the continuous and cyclical nature of scientific discovery. Beginning at the top of the visualization, the curiousity of Scientific Inquiry (an innate human trait) leads students to seek knowledge on an initial basis. To best grasp the significance of this knowledge, the student seeks the design of algorithm, or the constructs of programming as it is conceived to best relate to the phenomena. As the algorithmic models continue to improve and garner practicality, the application of these algorithms now take the form of engineering and simulation. This experience immerses the student in a ‘deepening of conceptual understanding’ and the students begins the cycle again by seeking deeper scientific truth. This is a principled approach, allowing us to view CT as the vehicle by which students move from seeking understanding to applying their understanding in practical ways.

            The Sengupta et al. article also lays framework for self-evaluation and comparison by the students as they model their own CT, by providing the expert models that can reveal to the students what methods and techniques of CT are most adequate. Providing the students with a great volume of quality CT tools is essential for developing these creative skills—just children role models of CT just as role models are helpful in anything else. The prospects of integrating CT on deeper levels than ever before is very exciting.

Week 12 Readings

“Computational Thinking in K-12: A Review of the State of the Field”

Grover and Pea

This paper speaks about how computational thinking can be a very important and different aspect of teaching of STEM subjects in the classroom. Computational thinking is described as a student having a practical approach to a problem in the following way:

1.     Thinking of computation in a creative way to solve a problem

2.     Dismissing abstract views that can reduce the focus on the necessary information that is needed in understanding and solving the problem

3.     Using data and information to facilitate the creation of knowledge

4.     Using different computational tools, such as algorithms, programming, and digital devices that can be used to solve a problem

The paper also mentions in order for this to be fostered correctly in the classroom, there must be a “low floor, high ceiling” guide. This would mean that whatever computational tool was used in the classroom was “low” enough for novice students to experience and learn and, at the same time, be “high” enough for expert students to benefit and learn beyond the parameters of the questions at hand.

“Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework”

Sangupta, Kinnebrew, Basu, Biswas, and Clark

            This paper offers a more theoretical approach as how computational thinking can be implemented into the classroom. It speaks on the concept of abstractions, benefits of computational thinking, agent-based computation, visual programming, and selection of initial curricular topics. Also, the paper takes a looks at the principles of program design and a pilot study performed.

My blog is very limited this week because I found both of the papers to be confusing. I am not familiar with computation, so I could not entirely grasp the “big picture” the authors were trying to relay. I’m struggling to see how this concept can be implemented into a classroom as well. Am I thinking too literal or is my ignorance to the literal definition of computation limiting me from grasping the connection between computation and computational thinking?

Week 12 Memo

Sengupta, etal: Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

                  The authors argue that while computational thinking (CT) plays a fundamental role in computer science, it could also have intriguing novel applications in the STEM disciplines by exposing K-12 students to practices of simulation, representation, abstraction, and prediction. According to the article, these computational concepts can support modeling and experimentation practices in science classrooms. The authors argue for agent-based computation via visual programming, meaning that users are able to program certain aspects of their computational models via manipulation of graphical objects. As part of their research, the authors studied the effects of agent-based computation on middle school student learning in ecology and kinematics. Using a program called CTSiM, students were able to create a computational algorithm, run a simulation of said algorithm, compare their simulation to an expert model, and revise their model as needed. The results indicated that significant learning gains were made through computational thinking, but individual student scaffolding is a crucial component of successful computation-based learning.

• ·             Immersing students in an experience or environment: This immersion is good for learning because it allows students to simulate and manipulate scientific phenomena.
• ·             Design-based learning: Computation activities can be used to help students focus on experimental design and modes of representation, which helps them engage in alternative scientific models and critical thinking regarding scientific argumentation.
• ·             Generalizability and decomposition: Due to the general nature of computational modeling and the ability of computer-based models to break down a phenomenon into more digestible components, the authors argue that CT can be used to simplify scientific models over a wide range of applications.

Grover & Pea: Computational Thinking in K-12: A Review of the State of the Field

                  This article addresses the effect of Jeannette Wing’s celebrated article, “Computational Thinking,” on attitudes towards incorporating computer-based learning in K-12 science education. Wing noted that CT encourages a widely applicable critical thinking skill set, and she elaborated on its role in fostering problem-solving techniques and abstraction. The authors also see these overarching benefits to computer-based modeling, but note that current school curricula do not provide much room for adding additional material. By focusing critical attention on CT’s goals, effectiveness, and its applicability to state standards and assessments, programmers will have a higher shot of incorporating and mainstreaming computational-based materials in schools.

• ·             Low floor, high ceiling: Effective programming should be easy for the user to interact with while simultaneously providing significant room for students to grow and explore.

While both articles advocated for the potential usefulness of CT in K-12 education, the Grover & Pea reading seems to believe that certain applications of computer-based learning have yet to be investigated thoroughly. They argue that without knowing what CT skills students are expected to have and be assessed on, computational modeling will continue to struggle to gain mainstream incorporation in the American education system. As a future educator, I think that CT can help students reach valuable learning goals. However, Grover & Pea bring up an interesting point: how, in a current system that is implicitly run by standardized testing, can we incorporate CT to align with “testable” goals? Can we afford to take time away from explicitly-stated education standards? I hope so, but it is hard to tell without actually having taught.