“CSTA dan ACM sudah mengembangkan kurikulum informatika untuk anak SD sampai dengan SMA sejak tahun 2003, yang kemudian direvisi pada tahun 2006 dan tahun 2011. Informatika dinyatakan sangat penting untuk membangun kapasitas intelektual, menuntun siswa ke berbagai karir di masa depan yang sangat tergantung kepada komputer, mengajarkan penyelesaian persoalan, dikaitkan dengan disiplin ilmu lainnya, dan dapat melibatkan semua siswa walaupun tidak akan melanjutkan studi di bidang studi informatika [kurikulum Computer Science (CS) CSTA versi 2011]. Kurikulum versi 2011 menekankan Computational Thinking sebagai landasan utama pada semua tingkatan pendidikan (dasar, menengah pertama dan menengah atas). Pada perkembangannya, ACM, CSTA dan code.org merilis kerangka kurikulum CS utk K-12 pada tahun 2016, yang dapat diakses di https://k12cs.org, yang saat ini merupakan acuan utama dari pengembangan kurikulum untuk berbagai negara bagian Amerika, dan juga di UK dan negara lainnya. CSTA juga merilis standar kurikulum sebagai pelengkap. Ada perbedaan antara kerangka kurikulum dan standar kurikulum. K–12 Computer Science Framework menyediakan kerangka kurikulum yang menyeluruh dan panduan “tingkat tinggi” per kelas, sedangkan standar yang diterbitkan CSTA dan ACM menyediakan ekspektasi penguasaan siswa secara detil dan terukur. Kerangka kurikulum menjadi bahan masukan utama dalam penyusunan standar.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 21)
“Coding adalah salah satu kegiatan dari implementasi algoritma yang dibangun melalui informatika dan Computational Thinking. Coding adalah keterampilan menulis kode program, sebagai implementasi dari problem solving dengan menggunakan komputer. Keterampilan Coding tanpa didasari kemampuan computational thinking dan problem solving, akan menjadi kegiatan berorientasi keterampilan saja. Diharapkan, bahwa tidak akan timbul salah persepsi di masyarakat seperti halnya salah persepsi tentang TIK dan Informatika yang selama ini terjadi. Coding adalah salah satu menjadi bagian kecil dari Informatika.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 22)
“Informatika adalah disiplin ilmu tentang prinsip-prinsip dan praktek yang melandasi pengertian dan pemodelan dari “komputasi”, dan aplikasinya dalam pengembangan sistem komputer. Landasan Berpikir untuk belajar informatika dinamakan “Computational Thinking”, yaitu suatu kerangka” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 45)
“dan proses berpikir yang mencakup perangkat keras dan perangkat lunak, dan menalar (reasoning) mengenai sistem dan persoalan. Moda berpikir (thinking mode) ini didukung dan juga dilengkapi dengan pengetahuan teoritis dan praktis, serta himpunan teknik untuk menganalisis, memodelkan dan memecahkan persoalan. Peserta didik yang belajar informatika akan mendalami bagaimana suatu “sistem komputational” berfungsi, baik yang mengandung komputer atau tidak.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 46)
“Informatika dapat dipandang sebagai sebuah cabang ilmu yang tersendiri karena: membawa seseorang ke suatu cara berpikir yang unik, dan berbeda dari bidang ilmu lainnya (computational thinking), sudah tahan lama (ide dan konsep sudah 20 tahun atau lebih, dan masih terpakai sampai sekarang), dan setiap prinsip inti dapat diajarkan tanpa tergantung kepada teknologi spesifik.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 47)
“Muatan informatika akan dikemas dalam kurikulum informatika yang merupakan seperangkat rencana dan pengaturan mengenai tujuan, isi, dan bahan pelajaran, serta cara yang digunakan sebagai pedoman penyelenggaraan kegiatan pembelajaran untuk mencapai tujuan pendidikan informatika yang dirancang untuk memberikan bekal kepada peserta didik secara berkesinambungan mulai dari PAUD, SD, SMP, hingga SMA. Bekal yang dimaksud meliputi beberapa kemampuan sebagai berikut. 1. Berpikir, yaitu computational thinking yang menjadi landasan dan prinsip pemecahan persoalan yang akan diselesaikan dengan bantuan komputer.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 77)
“Dari Gambar 5.1 dapat di ilustrasikan sebagai berikut. 1. Computational Thinking (CT) merupakan landasan berpikir yang perlu diajarkan terus menerus sesuai dengan jenjang pendidikan, dengan tingkat kesulitan dan kompleksitas yang menaik, mulai dari PAUD s.d SMA.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 79)
“Salah satu kemampuan yang dibutuhkan oleh manusia abad ke-21 Saat ini, dunia menuntut agar seseorang mampu memecahkan persoalan-persoalan yang semakin besar dan kompleks. Computational Thinking adalah suatu metoda penyelesaian persoalan secara efisien dan optimal, yang solusinya dilakukan oleh agen pemroses informasi, khususnya komputer jika proses dilakukan secara otomatis, berulang dan membutuhkan resources komputasi yang besar yang akan lama jika dilakukan secara manual. Oleh karena sebagian besar persoalan masa kini dalam berbagai bidang perlu diselesaikan dengan bantuan komputer, CT perlu diajarkan ke peserta didik sejak dini karena sebelum mampu menyelesaikan persoalan yang besar dan kompleks, seseorang perlu berlatih mulai dari persoalan kecil sederhana, kemudian secara bertahap berkembang kemampuannya untuk menyelesaikan persoalan yang besar dan kompleks, dengan menerapkan pola penyelesaian yang mirip, tetapi dengan detail cara yang berbeda jika persoalannya menjadi lebih besar dan kompleks. Kemampuan Computational Thinking mencakup dekomposisi, abstraksi, konstruksi algoritma, dan pembentukan pola penyelesaian persoalan, yang sulit untuk diajarkan secara teoritis saja, menjadi mudah dengan pendekatan konstruksionisme di mana siswa mengkonstruksi kemampuan berpikir komputasional melalui latihan-latihan dalam bentuk 81 tantangan berpikir yang menarik. Saat ini, Indonesia sudah bergabung dalam komunitas internasional yang terdiri atas lebih dari 50 negara, untuk mengadakan tantangan berpikir sebagai cara pembentukan pengetahuan Computational thinking mulai anak berusia 5 tahun s.d. 18 tahun (SMA). Komunitas tersebut berkumpul setiap tahun untuk secara bersama menggarap soal-soal menarik yang akan dipakai pada acara tahunan “Bebras Challenge” atau “Tantangan Bebras”. Indonesia dapat bergerak bersama negara-negara yang lebih maju dalam pendidikan informatika melalui komunitas dan kegiatan ini. Indonesia membentuk komunitas universitas dengan prodi rumpun informatika dan telah menggelar Tantangan Bebras sejak 2016, yang mendapat sambutan cukup positif dari beberapa sekolah. Kegiatan pembelajaran terkait Computational Thinking sudah siap dilaksanakan dengan materi yang dikembangkan bersama oleh komunitas bebras.” (The Ministry of Education and Culture of Indonesia (MoEC), 2020, p. 80)
wing-2008
Computational thinking is an analytical thinking used to solve a problem. It shares with mathematical thinking in abstraction, a well-known approach used for solving a problem. Beside abstraction as a mental tool for solving problem, computational thinking requires understanding how computer works so that one can use it to automate the abstraction. In this case, [[wing-2008]] stated that
“Computational thinking is taking an approach to solving problems, designing systems and understanding human behaviour that draws on concepts fundamental to computing1 (Wing 2006).” (Wing, 2008, p. 3717)
“The essence of computational thinking is abstraction. In computing, we abstract notions beyond the physical dimensions of time and space. Our abstractions are extremely general because they are symbolic, where numeric abstractions are just a special case.” (Wing, 2008, p. 3717)
“And so the nuts and bolts in computational thinking are defining abstractions, working with multiple layers of abstraction and understanding the relationships among the different layers. Abstractions are the ‘mental’ tools of computing. The power of our ‘mental’ tools is amplified by the power of our ‘metal’ tools. Computing is the automation of our abstractions. We operate by mechanizing our abstractions, abstraction layers and their relationships. Mechanization is possible due to our precise and exacting notations and models. Automation implies the need for some kind of computer to interpret the abstractions. The most obvious kind of computer is a machine, i.e. a physical2 device with processing, storage and communication capabilities” (Wing, 2008, p. 3718)
Computer simulation is heavily used in all scientific fields and triggered the emergence of new computationally-influenced subfields such as computational biology and computational microeconomy. In this point, wing-2008 stated that “‘Computational thinking is influencing research in nearly all disciplines, both in the sciences and the humanities’ (Bundy 2007). Evidence of computational thinking’s influence on other fields abounds: computational thinking is transforming statistics, where with machine learning the automation of Bayesian methods and the use of probabilistic graphical models make it possible to identify patterns and anomalies in voluminous datasets as diverse as astronomical maps, functional magnetic resonance imaging scans, credit card purchases and grocery store receipts (e.g. Machine Learning Department 2008). Computational thinking is transforming biology, first with the shotgun sequencing algorithm accelerating our ability to sequence the human genome, and now with our abstractions representing dynamic processes found in nature, from the cell cycle to protein folding (e.g. Fisher & Henzinger 2007). Computational thinking is transforming economics, spawning a new field of computational microeconomics, with applications such as advertisement placement, online auctions, reputation services and even finding optimal donors for n-way kidney exchange (Abraham et al.2007).” (Wing, 2008, p. 3719)
In Wing's vision, the 3R analytical abilities currently possessed by students (reading, writing, and arithmetic) need to be completed by computational thinking skills ([[wing-2006]]). In this point, [[wing-2006]] stated that “Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability. Just as the printing press facilitated the spread of the three Rs, what is appropriately incestuous about this vision is that computing and computers facilitate the spread of computational thinking.” (Wing, 2006, p. 33)
wing-2011
Wing, together with Snyder and Cuny refined the CT definition as “Computational thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be e ectively carried out by an informationprocessing agent. [CunySnyderWing10]” (Wing, p. 2)
“need to understand how to communicate with them to most effectively harness their computing power.” (Shute et al., 2017, p. 143)
“elusive” (Shute et al., 2017, p. 143)
““solving problems, designing systems, and understanding human behavior, by drawing on the concepts fundamental to computer science”” (Shute et al., 2017, p. 143)
“solutions are represented in a form that can be effectively carried out by an information-processing agent”” (Shute et al., 2017, p. 143)
“The components common among researchers are: decomposition, abstraction, algorithms, and debugging.” (Shute et al., 2017, p. 145)
“CT skills are not the same as programming skills (Ioannidou et al., 2011), but being able to program is one benefit of being able to think computationally” (Shute et al., 2017, p. 146)
“understand the cognitive processes underlying each of these CT facets and the associated behaviors that can help us develop a competency model that can be used for the assessment of CT.” (Shute et al., 2017, p. 151)
“underlying conceptual foundation required to approach problems via a CT perspective and how that kind of CT perspective can be highlighted and supported in current K-12 subjects.” (Shute et al., 2017, p. 152)
“from high school teaching practices that occur in STEM courses.” (Shute et al., 2017, p. 152)
“their model is restricted to high school STEM course settings, where we feel that a foundational basis for CT starts much earlier, analogous to scientific inquiry or mathematic reasoning.” (Shute et al., 2017, p. 152)
“competency-based model of CT knowledge and skills” (Shute et al., 2017, p. 152)
“trial-and-error (testing arbitrary Zoombinis) to a systematic testing pattern,” (Shute et al., 2017, p. 153)
“some of which researchers can identify as evidence of CT.” (Shute et al., 2017, p. 153)
“This change, from trial-and-error to systematic testing provides evidence of problem decomposition.” (Shute et al., 2017, p. 154)
“eventually demonstrating their ability to abstract the patterns into an underlying rule of the puzzle.” (Shute et al., 2017, p. 154)
“from the elementary school version of the assessment.” (Shute et al., 2017, p. 154)
“learners must use feedback from the game to figure out which values per graphical feature (color and/or shape and/or pattern) are required to solve a puzzle” (Shute et al., 2017, p. 154)
“(in advance)” (Shute et al., 2017, p. 154)
“But lacking a standard definition to operationalize CT consequently leads to research where measurements vary greatly across studies, which makes the results less convincing and certainly difficult to compare.” (Shute et al., 2017, p. 156)
“(a) considers CT as a logical way of thinking, not simply knowing a programming language; (b) particularly focuses on conceptual development required to engage in problem decomposition, abstraction, algorithmic design, debugging, iteration, and generalization; (c) examines performance-based competencies related to each of these facets of CT, and (d) can be strengthened and emphasized within existing (e.g. STEM) curricula.” (Shute et al., 2017, p. 157)
#terms/ct Shute Sun Asbell-Clarke 2017 Demystifying computational thinking
#summaryByAI This research paper investigates the growing field of computational thinking (CT) in education. The authors review existing definitions, interventions, assessments, and models of CT. They synthesize the literature and propose a working definition of CT as the conceptual foundation needed to solve problems effectively and efficiently, with solutions that are reusable in different contexts. The paper categorizes CT into six facets: decomposition, abstraction, algorithm design, debugging, iteration, and generalization. Finally, the authors introduce their competency model of CT, which aims to guide the development of CT pedagogy and assessment in educational settings.
.
“The introduction of CT in primary mathematics curricula puts many
demands on teachers, and their understanding of CT in mathematics is key to its successful introduction.” (Nordby et al., 2022, p. 35) A good argument on why should we conduct research on teacher’s understanding. So how can I make an argument for teachers’ perspective?
“Through observations and interviews, we find promis-
ing starting points for including CT, related to pattern recognition, problem solving and the use of programming activities.” (Nordby et al., 2022, p. 35) Promising starting points are pattern recognition, problem solving, and the use of programming activities.
“While AI refers to the intelli-
gence evident in machines or software and is often focussed
on in higher grades [4], CT is considered a skill that can
be added ‘to every child’s analytical ability’ [6 p. 33] and,
therefore, can be included from the early years of primary
education.” (Nordby et al., 2022, p. 35) Good argument to compare and contrast AI and CT. While AI is mostly taught in high grade, the CT can be introduced in lower grade.
For my argument: while teachers’s perspective on lower have been studied, less studies are found for the higher level. But why should we do it in higher level? Because higher grade offers opportunities to teach more complex AI, complex CT.
“Norway introduced CT into the mathematics curriculum
in 2020. In this paper, we explore mathematics teachers’
understanding of CT as part of the mathematics curriculum
three months after its introduction, as well as throughout the
scholastic year.” (Nordby et al., 2022, p. 36) The author shows the aim of the study.
“However, the existing
studies did show that screen-based computer programming
and digital tangibles were dominant in primary mathemat-
ics classrooms, and that most CT interventions were led by
researchers rather than the teachers themselves [28]. Con-
sequently, Nordby et al. [28] raised the question of whether
teachers lack CT proficiency and what challenges they face
in relation to their CT expertise. Adding to these concerns,
after analysing 47 lesson plans in mathematics across grades
1–5, Israel and Lash [12] found that the majority of the les-
sons did not include any integration of mathematics and
CT—the lessons focussed either on mathematics (less often)
or on CT (more often).” (Nordby et al., 2022, p. 36) Research on CT shows that CT activities are dominated (lead) by researchers, not by teachers.
Akibat: memungkinkan untuk melibatkan guru dalam desain aktifitas CT bagi siswa.
“Rich et al. [8] presented an in-depth qualitative study investigating what 12 elementary school teachers thought about integrating CT in their existing elementary school cur- ricula. Using the description of Bocconi et al. [9] of the key components of CT as their point of departure, the teachers were asked to comment on how these key components fit their existing mathematical and science practices, revealing that the teachers most often related CT to problem solv- ing. This is not surprising. A recent review investigating CT in mathematical problem solving in K–12 education found that problem solving is an important and fundamental term within both CT and mathematics; this overlap forms the base of the well-established argument for integrating CT in mathematics [Refvik & Bjerke, submitted].” (Nordby et al., 2022, p. 36)
“Taken together, we see that there is a need to focus more on teachers’ knowledge and understanding of CT.” (Nordby et al., 2022, p. 37)
“Keeping in mind that CT is relatively new to educators in general, and that Norway has recently introduced CT into their new mathematics curriculum, we find it important to investigate the teachers’ roles as gatekeepers and give them a voice in the matter” (Nordby et al., 2022, p. 37)
“t teachers’
understanding needs to be understood a) in connection to
programming, and b) in connection to mathematics” (Nordby et al., 2022, p. 39) Ini menarik. Teachers’ understanding haruskah dipahami secara matematis? Ataukah hanya perlu dipahami dalam kaca mata programming.
“The teachers in our study discussed algorithms both in
terms of following and in terms of creating instructions.
When planning mathematics lessons that involved CT, both
Laura and Kyra relied heavily on instructions given in a text-
book that their school used.” (Nordby et al., 2022, p. 39) Di sini teachers struggle dengan istilah algorithm. Mereka menganggap algorithmic thinking sebagai ”following” the formula, bukan membuat formula itu sendiri.
“Kimmi took a somewhat different approach. She interpreted working with algorithms (as in algorithmic thinking) as something similar to working with standard algorithms (Kyra and Olivia also reported a similar under- standing in their interviews)” (Nordby et al., 2022, p. 40)
“In doing this, she reasoned that she had included algo-
rithmic thinking, or CT, in her teaching. In the same man-
ner, she argued that she teaches her students CT by explain-
ing how pattern recognition (teaching figure numbers) and
trial and error (using word problems) are activities that we
already do in mathematics: ‘What I think [CT] is, is that one
should, in a way, increase the understanding of numbers,
perhaps by seeing patterns in numbers and playing around a
bit’ (Kimmi, interview 2, 02:26)” (Nordby et al., 2022, p. 40) Teacher bisa jadi menganggap bahwa dia sudah memasukkan aspek CT dalam keseharian ketika dia mengajar matematika biasa.
“As such, she reasoned, because the algorithmic thinking is ‘already there’, there is no need to add anything to ‘tick off’ the added competence goals in the curriculum following the inclusion of CT in mathematics” (Nordby et al., 2022, p. 40)
“Interestingly, pattern recognition and problem solving in CT appeared to be the two concepts that were the most con- nected to the established mathematics information ecology in the minds of the teachers. When discussing pattern rec- ognition, for example,” (Nordby et al., 2022, p. 43)
“Another concern is how the unplugged and screen-based
tasks used by the teachers contribute to changing the math-
ematics ecology. That is, CT takes up time and space that
can, at times, move the mathematical content into the back-
ground; more importantly, it can lead teachers to misinter-
pret or lose focus on the primary goals of the curriculum,
such as when Kimmi introduced standard algorithms in
Grade 4” (Nordby et al., 2022, p. 44) Mengajarkan CT melalui math dapat push math into background. It could lead teacers to lose focus on the goals of curriculum.
“Our analysis reveals two possible directions for teachers: (1) rejection and some resistance, or (2) inspira- tion and a willingness to engage with new ideas and activi- ties.” (Nordby et al., 2022, p. 44)
Contents
As soon as informatics and computer science gained popularity, Wing's idea of computational thinking [[computational-thinking-wing]] got attention from researchers, practitioners, and policymakers in the educational field. In this case, [[bocconi-chioccariello-dettori-ferrari-engelhardt-2016]] stated that
wing-2006
Computational method allows us to solve problems that no one would be capable of tackling alone. wing-2006 stated that “Computational methods and models give us the courage to solve problems and design systems that no one of us would be capable of tackling alone.” (Wing, 2006, p. 33)
Computational thinking is a fundamental skill, and children need to be able to master it, together with reading, writing, and arithmetic. In this case, wing-2006 stated that “Computational thinking is a fundamental skill for everyone, not just for computer scientists. To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability.” (Wing, 2006, p. 33)
Computational thinking is not a rote skill. In this case, wing-2006 stated that “Fundamental,notroteskill. A fundamental skill is something every human being must know to function in modern society. Rote means a mechanical routine. Ironically, not until computer science solves the AI Grand Challenge of making computers think like humans will thinking be rote;” (Wing, 2006, p. 35)
Wing’s idea is to think like a computer scientiest wing-2006. She stated that “Professors of computer science should teach a course called “Ways to Think Like a Computer Scientist” to college freshmen, making it available to non-majors, not just to computer science majors.” (Wing, 2006, p. 35)
Computational thinking requires thinking in multiple level of abstraction. Although it is strongly related to programming, it is more than being able to program a computer. wing-2006 stated “Conceptualizing, not programming. Computer science is not computer programming. Thinking like a computer scientist means more than being able to program a computer. It requires thinking at multiple levels of abstraction;” (Wing, 2006, p. 35)
“r science is and what it might contribute to solving problems across the spectrum of human inquiry.” (Barr and Stephenson, 2011, p. 49)
“The computer science education community can play an impor-
tant role in highlighting algorithmic problem solving practices and
applications of computing across disciplines, and help integrate the
application of computational methods and tools across diverse areas
of learning.” (Barr and Stephenson, 2011, p. 49)
“Rather, it is “the study of computers and algorithmic processes in-
cluding their principles, their hardware and software design, their
applications, and their impact on society”” (Barr and Stephenson, 2011, p. 49)
“Felleisen and Krishnamurthy [3] have argued that “imaginative programming” is the most crucial element of computing because it closely aligns mathematics with computing and in this way brings mathematics to life.” (Barr and Stephenson, 2011, p. 49)
“com- putational thinking, as distinct from computer science,” (Barr and Stephenson, 2011, p. 49)
“Hemmendinger [6] notes that we must be aware of the risks of arrogance and over- reaching when discussing the role of computational thinking, es- pecially across disciplines.” (Barr and Stephenson, 2011, p. 50)
“Hemmendinger concludes that the ultimate goal should not be to teach everyone to think like a computer scientist, but rather to teach them to apply these common elements to solve problems and discover new questions that can be explored within and across all disciplines.” (Barr and Stephenson, 2011, p. 50)
“embedding computational thinking in K-12 requires a practical approach, grounded in an op- erational defi nition.” (Barr and Stephenson, 2011, p. 50)
“Rather, the goal of the meeting was to reach a consensus of what computational think- ing means in K-12, as well as explain the particularities of K-12 education to the CS education representatives” (Barr and Stephenson, 2011, p. 51)
“The computer scientists participating, in particular, noted that educational change was considerably more complex than they suspected and that working with educators from multiple diverse disciplines meant learning to “disconnect computational thinking from computer science”.” (Barr and Stephenson, 2011, p. 51)
“When chal- lenged with the task of describing what makes computational thinking distinct from other kinds of thinking” (Barr and Stephenson, 2011, p. 51)
“CT is an approach to solving problems in a way that can be implemented with a computer. Students become not merely tool users but tool builders. They use a set of concepts, such as ab- straction, recursion, and iteration, to process and analyze data, and to create real and virtual artifacts. CT is a problem solving methodology that can be automated and transferred and applied across subjects.” (Barr and Stephenson, 2011, p. 51)
“The power of computational thinking is that it applies to every other type of reasoning. It enables all kinds of things to get done: quantum physics, advanced biology, human-computer systems, de- velopment of useful computational tools.” (Barr and Stephenson, 2011, p. 51)
“through active problem solving.” (Barr and Stephenson, 2011, p. 51)
“how they might be embedded in activities across multiple disciplines. Table 1 shows the results of these efforts.” (Barr and Stephenson, 2011, p. 51)
“Design solutions to problems” (Barr and Stephenson, 2011, p. 51)
“Implement designs (programming as appropriate); ■” (Barr and Stephenson, 2011, p. 51)
“Test and debug” (Barr and Stephenson, 2011, p. 51)
“explicit use” (Barr and Stephenson, 2011, p. 52)
“decompositio” (Barr and Stephenson, 2011, p. 52)
“• abstraction” (Barr and Stephenson, 2011, p. 52)
“negotiation” (Barr and Stephenson, 2011, p. 52)
“whole,” (Barr and Stephenson, 2011, p. 52)
“consensus building” (Barr and Stephenson, 2011, p. 52)
“Increased use” (Barr and Stephenson, 2011, p. 52)
“of computational vocabulary where appropriate to describe problems and solutions;” (Barr and Stephenson, 2011, p. 52)
“Acceptance” (Barr and Stephenson, 2011, p. 52)
“This counters the potential claim that computa- tional thinking can only be addressed in informal education expe- riences where discipline based-learning and classroom constraints are not major encumbrances.” (Barr and Stephenson, 2011, p. 53)
“Teachers also need resources that demonstrate how to most appropriately and effectively integrate these new concepts, fi rst into their own sphere of content and pedagogical knowledge, and then into their classroom content and practice.” (Barr and Stephenson, 2011, p. 53)
barr-stephenson-2011
The importance of computational thinking lead to the requirement of introducing the concept in primary and secondary education, rather than waiting until they learn the concept in college. In this point, barr-stephenson-2011 stated that “It is no longer sufficient to wait until students are in college to introduce these concepts. All of today’s students will go on to live a life heavily influenced by computing, and many will work in fields that involve or are influenced by computing. They must begin to work with algorithmic problem solving and computational methods and tools in K-12.” (Barr and Stephenson, 2011, p. 49)
Computer science has essential role in the effort of bringing the concept across discipline. However, they need to understand that the complexity of K-12 lead to a responsibility to provide support to K-12 changes. In this point barr-stephenson-2011 stated that “The computer science education community can play an important role in highlighting algorithmic problem solving practices and applications of computing across disciplines, and help integrate the application of computational methods and tools across diverse areas of learning. At the same time, CS educators must understand the complexities of the K-12 educational setting, incorporating that knowledge into outreach activities and support for K-12 changes.” (Barr and Stephenson, 2011, p. 49)
This is linked to Shute Sun Asbell-Clarke 2017. So let see how it is connected to the note in Obsidian. duaaaa
“The goal of this exploratory study was not to examine teachers’ conceptions or misconceptions about CT, but rather to dig deeper into their thinking on how CT could be integrated within their existing elementary school curricula.” (Rich et al., 2019, p. 172)
“This study aimed to answer the following research questions: • How do elementary school teachers view computational thinking to be related to their mathematics and science teaching practices? • What challenges do elementary school teachers anticipate in bringing CT into their math and science teaching?” (Rich et al., 2019, p. 173)
“The first author, who had no previous relationship with the teachers, conducted a semi-structured interview (Licht” (Rich et al., 2019, p. 174)
“Computational Thinking, Mathematics, and Science 175 man, 2006) with each teacher.” (Rich et al., 2019, p. 175)
“Two researchers (the first author and a research assistant) collaboratively used a process of open coding to capture initial themes in two of the interviews.” (Rich et al., 2019, p. 175)
“For three of the six CT components (algorithmic thinking, automation, and decomposition), teachers’ responses reflected the influence of shared vocabulary between mathematics content and pedagogy and computational thinking.” (Rich et al., 2019, p. 178)
“For the remaining three components of CT (debugging, abstraction, and generalization), teachers’ responses did not reflect prior knowledge of use of” (Rich et al., 2019, p. 181)
“similar terms in mathematics or science.” (Rich et al., 2019, p. 182)
“Generalization, on the other hand, was the most commonly discussed CT component.” (Rich et al., 2019, p. 182)
“teachers brought up several concerns that are common to most classroom initiatives, such as lack of parental support, potential language barriers, and student motivation.” (Rich et al., 2019, p. 185)
“Teachers in our study understood computational thinking as a kind of problem solving, making more connections to their mathematics teaching than to their science teaching.” (Rich et al., 2019, p. 186)
“One interesting thing to note is that teachers in our study typically talked about decomposition of numbers rather than decomposition of problems.” (Rich et al., 2019, p. 189)
“The goal of this exploratory study was not to examine teachers’ conceptions or misconceptions about CT, but rather to dig deeper into their thinking on how CT could be integrated within their existing elementary school curricula.” (Rich et al., 2019, p. 172)
“This study aimed to answer the following research questions: • How do elementary school teachers view computational thinking to be related to their mathematics and science teaching practices? • What challenges do elementary school teachers anticipate in bringing CT into their math and science teaching?” (Rich et al., 2019, p. 173)
“The first author, who had no previous relationship with the teachers, conducted a semi-structured interview (Licht” (Rich et al., 2019, p. 174)
“Computational Thinking, Mathematics, and Science 175 man, 2006) with each teacher.” (Rich et al., 2019, p. 175)
“Two researchers (the first author and a research assistant) collaboratively used a process of open coding to capture initial themes in two of the interviews.” (Rich et al., 2019, p. 175)
“Finding #1: Teachers think of CT as problem solving with stronger connections to their mathematics instruction than to their science instruction.” (Rich et al., 2019, p. 176)
“Finding #2: Teachers identified elements of their practice that can serve as productive starting points for learning and instruction of decomposition, algorithmic thinking, and automation.” (Rich et al., 2019, p. 178)
“For three of the six CT components (algorithmic thinking, automation, and decomposition), teachers’ responses reflected the influence of shared vocabulary between mathematics content and pedagogy and computational thinking.” (Rich et al., 2019, p. 178)
“Finding #3: Teachers made the fewest connections to debugging and abstraction, and the most connections to generalization.” (Rich et al., 2019, p. 181)
“For the remaining three components of CT (debugging, abstraction, and generalization), teachers’ responses did not reflect prior knowledge of use of” (Rich et al., 2019, p. 181)
“similar terms in mathematics or science.” (Rich et al., 2019, p. 182)
“Generalization, on the other hand, was the most commonly discussed CT component.” (Rich et al., 2019, p. 182)
“Finding #4: The challenges of teaching math and science through CT most anticipated by teachers related to limited class time and developmental appropriateness.” (Rich et al., 2019, p. 185)
“teachers brought up several concerns that are common to most classroom initiatives, such as lack of parental support, potential language barriers, and student motivation.” (Rich et al., 2019, p. 185)
“Teachers in our study understood computational thinking as a kind of problem solving, making more connections to their mathematics teaching than to their science teaching.” (Rich et al., 2019, p. 186)
“Teachers’ conceptions of CT as problem-solving and as connected to mathematics echo results of previous work with preservice (Yadav, Zhou, Mayfield, Hambrusch, & Korb, 2011) and inservice teachers (Sands, Yadav, & Good, 2018).” (Rich et al., 2019, p. 187)
“One interesting thing to note is that teachers in our study typically talked about decomposition of numbers rather than decomposition of problems.” (Rich et al., 2019, p. 189)
“Interviews” (Cohen et al., 2018, p. xi)
“Interviews enable participants – interviewers and interviewees – to discuss their interpretations of the world in which they live, and to express how they regard situations from their own point of view” (Cohen et al., 2018, p. 506)
“to explore issues in depth, to see how and why people frame their ideas in the ways that they do, how and why they make connec- tions between ideas, values, events, opinions, behav- iours, etc” (Cohen et al., 2018, p. 506)
“interviewee fatigue may hamper the interview and anonymity may be difficult” (Cohen et al., 2018, p. 506)
“gathering information to serve the research objectives, acquiring information” (Cohen et al., 2018, p. 508)
“test hypotheses or to suggest new ones; or to be an explana- tory device to help identify variables and relationships” (Cohen et al., 2018, p. 508)
“One advantage, for example, is that it allows for greater depth than is the case with other methods of data col- lection” (Cohen et al., 2018, p. 508)
“At one end of the fifth continuum is the attempt to find regularities – of response, opinions etc. – in order to begin to make generalizations from the data, to describe what is happening; at the other end is the attempt to portray and catch uniqueness, the quality of a response, the complexity of a situation, to understand why respondents say what they say, and all of this in their own terms” (Cohen et al., 2018, p. 511)
“the semi-structured interview” (Cohen et al., 2018, p. 511)
“semi-structured interview, the topics and questions are given, but the questions are open-ended and the wording and sequence may be tailored to each individual interviewee and the responses given, with prompts and probes” (Cohen et al., 2018, p. 511)
papert-1980
The essence of LOGO in Papert’s vision is seeing the turtle as an object-to-think. In this case, [[papert-1980]] stated that “By now the reader must anticipate that I shall say an object-to-think-with, that will contribute to the essentially social process of constructing the education of the future.” (Papert, 2020, p. 182)