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?
Science Literacies 2015
Sunday, November 15, 2015
Tuesday, November 10, 2015
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.
.
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.
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 21st 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.
Subscribe to:
Posts (Atom)