In the U.S. education system, magnet schools are public schools with specializedcourses or curricula.
Normally, a student will attend an elementary school, and this also
determines the middle school and high school they attend unless they
move. "Magnet"
refers to how magnet schools accept students from different areas,
pulling students out of the normal progression of schools. Attending
them is voluntary.
There are magnet schools at the elementary, middle, and high school levels. In the United States, where education is decentralized, some magnet schools are established by school districts and draw only from the district, while others are set up by state governments and may draw from multiple districts. Other magnet programs are within comprehensive schools,
as is the case with several "schools within a school". In large urban
areas, several magnet schools with different specializations may be
combined into a single "center," such as Skyline High School in Dallas.
Other countries have similar types of schools, such as specialist schools in the United Kingdom.
Most of these are academically selective. Other schools are built
around elite-sporting programs or teach agricultural skills such as
farming or animal husbandry.
In 1965, then Vice PresidentHubert Humphrey came to John Bartram High School in Southwest Philadelphia
to declare it the first magnet school in the country. Bartram's
curriculum was concentrated in the commercial field, offering commercial
and business training to students from all over Philadelphia.
History
DeBakey High School for Health Professions in Houston, Texas, is a magnet school specializing in medical sciencesThese 2nd graders from Buchanan Math Science Magnet School in Los Angeles,
work on an art project. After studying the physical environment of the
planet Mars, they are now designing a suitable Martian community.
In the United States, the term "magnet school" refers to public
schools with enrichment programs that are designed to attract and serve
certain targeted subgroups of potential students and their families.
There are two major categories of public magnet school structures in the
United States, and although there is some overlap, their origins and
missions remain largely distinct. The first type of magnet school is
the fully competitive admissions magnet school. These schools use
competitive admissions, usually rely on a standardized assessment score,
and are structured to serve and support populations that are 100%
gifted and/or talented students. Schools in this group generally rank
among the top 100 public high schools in the United States. Examples of
this type of school and program include the Maine School of Science and Mathematics, Thomas Jefferson High School for Science and Technology in Virginia, The School Without Walls
in the District of Columbia, and nine schools that all use competitive
admissions and are overseen by the New York City Department of Education
(which still uses the older term "specialized school" instead of "magnet school" to refer to them. Another type of "magnet school" or "magnet program" emerged in the United States in the 1970s as one means of remedying racial segregation in public schools, and they were written into law in Section 5301 of the Elementary and Secondary Education Authorization. Demographic trends following the 1954 Brown v. Board of EducationUS Supreme Court decision revealed a pattern later characterized as white flight, the hypersegregation of blacks and whites, as the latter moved to the suburbs. The first charter school, McCarver Elementary School, opened in Tacoma, Washington, in 1968.
This second type of magnet can often take the form of "a school within
a school," meaning that the school may have no competitive admissions
for the majority of the school population, and even the magnet program
itself may not have fully competitive admissions. This is consistent
with the equity-based objectives of such programs.
With the magnets designed to increase equity, at first school
districts tried using involuntary plans which involved court-ordered
attendance, the busing of children far from their homes, and building closer schools to achieve the required balance.
Later, voluntary school integration plans were developed. One approach
that educators within the public school system came up with was open
schools. During the Open Schools movement of the 1970s, several ideas
designed to influence public education were put into practice, including
Schools without Walls, Schools within a School, Multicultural Schools,
Continuation Schools, Learning Centers, Fundamental Schools, and Magnet
Schools.
"These schools were characterized by parent, student, and teacher
choice, autonomy in learning and pace, non-competitive evaluation, and a
child centered approach."
Magnet schools have been the most successful of the ideas that
originated from the Open Schools movement. It was expounded in 1971 by
educator Nolan Estes, superintendent of Dallas Independent School District. The Magnet Schools Assistance Program was developed in the early 1980s as a way to encourage schools to address de facto
racial segregation. Funds were given to school districts that
implemented voluntary desegregation plans or court orders to reduce
racial isolation.
From 1985 to 1999, a US district court judge required the state of Missouri to fund the creation of magnet schools in the Kansas City Public Schools to reverse the white flight
that had afflicted the school district since the 1960s. The district's
annual budget more than tripled in the process. The expenditure per
pupil and the student-teacher ratio were the best of any major school
district in the nation. Many high schools were given college-level
facilities. Still, test scores in the magnet schools did not rise; the
black-white gap did not diminish; and there was less, not greater,
integration.
Finally, on September 20, 2011, The Missouri Board of Education voted unanimously to withdraw the district's educational accreditation status from January 1, 2012.
Districts started embracing the magnet school models in the hope
that their geographically open admissions would end racial segregation
in "good" schools and decrease de facto segregation of schools in
poorer areas. To encourage the voluntary desegregation, districts
started developing magnet schools to draw students to specialized
schools all across their districts. Each magnet school would have a
specialized curriculum that would draw students based on their
interests. One of the goals of magnet schools is to eliminate, reduce,
and prevent minority group isolation while providing the students with a
stronger knowledge of academic subjects and vocational skills.
Magnet schools still continue to be models for school improvement plans
and provide students with opportunities to succeed in a diverse
learning environment.
Within a few years, in locations such as Richmond, Virginia,
additional magnet school programs for children with special talents
were developed at facilities in locations that parents would have
otherwise found undesirable. That effort to both attract voluntary
enrollment and achieve the desired racial balance met with considerable
success and helped improve the acceptance of farther distances,
hardships with transportation for extracurricular activities, and the
separation of siblings. Even as districts such as Richmond were released
from desegregation court orders, the parental selection of magnet
school programs has continued to create more racially diverse schools
than would have otherwise been possible. With a wide range of magnet
schools available, a suitable program could be found for more children
than only the "bright" ones for whom the earliest efforts were directed.
Some 21st-century magnet schools have de-emphasized the racial
integration aspects, such as Capital Prep Magnet School, a high school
in Hartford, Connecticut.
Capital Prep, a year-round school where more than 80% of its students
are black and Latino, boasts a near-0% dropout rate; 100% of its 2009
senior class was sent to a four-year college. According to the school's principal, the goal is to prepare all of its students for college.
Since coming into fruition, the number of magnet schools has
risen dramatically. Over 232 school districts housed magnet school
programs in the early 1990s. By the end of the decade, nearly 1,400
magnet schools were operating across the country.
Traditionally, these magnet schools are found in neighborhoods
with large minority populations. They advertise their unique educational
curricula in order to attract white students who do not live in the
surrounding area. In this way, the schools act as a "magnet" pulling
out-of-neighborhood students that would otherwise go to a school in
their traditional attendance zone.
Some magnet schools have a competitive entrance process, requiring an entrance examination, interview, or audition. Other magnet schools either select all students who apply, or use a lottery system among students who apply, while others combine elements of competitive entrance and a lottery among applicants.
Access to free transportation is a key component in facilitating
racial diversity in magnet schools. According to a survey distributed at
the Magnet Schools of America's (MSA) 2008 annual meeting, in magnet
schools with free transportation services, non-white students comprise
almost 33% of the student body, which is higher than the 23% found in
magnet schools without such services. Moreover, 11.9% of magnet schools
that do not provide transportation are largely one-race, while only 6.4%
of magnet schools with the provision of transportation are
characterized as one-race schools. Such services are integral in
ensuring that potential out-of-neighborhood students have access to
these schools of choice. Ultimately, the presence of free transportation
contributes to more integrated magnet environments.
Across the country, magnet school application forms assume that
its readers are proficient in reading and writing in English, understand
the school's curriculum, and recognize what kinds of resources are
offered to students at that respective school. In diverse urban contexts
especially, these assumptions privilege some families over others.
Parents who seek out magnet schools tend to be Asian, educated,
middle-class, and English-fluent. Thus, in order to break down the
racial disparities these schools were intended to dismantle, magnet
school programs have to be intentional in not only their outreach
efforts, but also how they create the application text itself.
Elementary school children coding in a robotics programme
Computer science education or computing education is the field of teaching and learning the discipline of computer science, and computational thinking.
The field of computer science education encompasses a wide range of
topics, from basic programming skills to advanced algorithm design and
data analysis. It is a rapidly growing field that is essential to
preparing students for careers in the technology industry and other
fields that require computational skills.
Computer science education is essential to preparing students for
the 21st century workforce. As technology becomes increasingly
integrated into all aspects of society, the demand for skilled computer
scientists is growing. According to the Bureau of Labor Statistics,
employment of computer and information technology occupations is
projected to "grow 21 percent from 2021 to 2031", much faster than the
average for all occupations.
In addition to preparing students for careers in the technology
industry, computer science education also promotes computational
thinking skills, which are valuable in many fields, including business,
healthcare, and education. By learning to think algorithmically and
solve problems systematically, students can become more effective
problem solvers and critical thinkers.
Background
In
the early days of computer programming, there wasn't really a need for
setting up any kind of education system, as the only people working with
computers at the time were early scientists and mathematicians.
Computer programming wasn't nearly popular enough to warrant being
taught, nor was it at a point where anyone who wasn't an expert could
get anything out of it. It was soon realized however, that
mathematicians were not a good fit for computer science work and that
there would be a need for people fully focused around the subject.
As time went on, there was a greater need for those who were
specifically trained in computer programming to match the demands of a
world becoming more and more dependent on the use of computers.
Initially, only colleges and universities offered computer programming
courses, but as time went on, high schools and even middle schools
implemented computer science programs.
Another differentiator of computer science education is that it
has primarily only been taught at university level until recently, with
some notable exceptions in Israel, Poland and the United Kingdom with
the BBC Micro in the 1980s as part of Computer science education in the United Kingdom.Computer science has been a part of the school curricula from age 14 or age 16 in a few countries for a few decades, but has typically as an elective subject.
Primary and secondary computer science
education is relatively new in the United States with many K-12 CS
teachers facing obstacles to integrating CS instruction such as
professional isolation, limited CS professional development resources,
and low levels of CS teaching self-efficacy.According to a 2021 report, only 51% of high schools in the US offer computer science.
Elementary CS teachers in particular have lower CS teaching efficacy and
have fewer chances to implement CS into their instruction than their
middle and high school peers. Connecting CS teachers to resources and peers using methods such as Virtual communities of practice
has been shown to help CS and STEM teachers improve their teaching
self-efficacy and implement CS topics into student instruction.
Curriculum
As
with most disciplines, computer science benefits from using different
tools and strategies at different points in a student's development to
ensure they get the most out of the teaching. Visual programming
languages like Scratch and MIT App Inventor
are effective in elementary and middle schools as a good introduction
to how programming languages function with a simple and easy to
understand block-based programming structure.
Once students have gotten a grasp for the very basics of programming
through these languages, usually teachers will move on to an easy to use
text-based programming language, such as Python,
where syntax is much simpler compared to more complex languages.
Generally, students are taught with languages that are popular among
professional businesses and programmers so that they can become familiar
with languages actually used in the workforce. Thus, in high school and
college, classes tend to focus on more complex uses of Python as well
as other languages such as Java, C++, and HTML.
Despite this, it isn't completely necessary to focus on the most
popular or used coding languages as much of computer science is built
off of learning good coding practices that can be applied to any
language in some form.
Teaching methods
Effective
teaching methods in computer science often differ from that of other
subjects as the standard slideshow and textbook format often used in
schools has been found to be less effective compared to standard
academic subjects.
Due to the problem-solving nature of computer science, a kind of
problem focused curriculum has been found to be the most effective,
giving students puzzles, games, or small programs to interact with and
create. Rather than applying techniques or strategies learned to tests
or quizzes, students must use material learned in class to complete the
programs and show they are following the class.
On top of this, it has been found that developing teaching methods that
seek to improve and guide students problem-solving and creative
abilities tend to help them succeed in computer science and other
classes.
The problem-solving aspect of computer science education is often the
hardest part to deal with as many students can struggle with the
concept, especially when it is likely they have not had to apply in such
a way before this point.
Something else that has become popular in more recent times are
online coding courses and coding bootcamps. Due to the nature of
computer science as a discipline, there are many who realize there
interest for it only later in life, or maybe it wasn't widely available
when they attend high school or college. These opportunities often
involve rigorous courses that are more geared to getting people ready
for the workforce rather than a more academic focus.
Coding bootcamps have become a great way for people to break out into
the computer science market without having to go to school all over
again.
Computing education research
Computing
education research (CER) or Computer science education research is an
interdisciplinary field that focuses on studying the teaching and
learning of computer science.
It is a subfield of both computer science and education research, and
is concerned with understanding how computer science is taught, learned,
and assessed in a variety of settings, such as K-12 schools, colleges
and universities, and online learning environments.
Background
Computer
science education research emerged as a field of study in the 1970s,
when researchers began to explore the effectiveness of different
approaches to teaching computer programming. Since then, the field has
grown to encompass a wide range of topics related to computer science
education, including curriculum design, assessment, pedagogy, and
diversity and inclusion.
Topics of study
One
of the primary goals of computer science education research is to
improve the teaching and learning of computer science. To this end,
researchers study a variety of topics, including:
Curriculum design
Researchers
in computer science education seek to design curricula that are
effective and engaging for students. This may involve studying the
effectiveness of different programming languages, or developing new
pedagogical approaches that promote active learning.
Assessment
Computer
science education researchers are interested in developing effective
ways to assess student learning outcomes. This may involve developing
new measures of student knowledge or skills, or evaluating the
effectiveness of different assessment methods.
Pedagogy
Researchers
in computer science education are interested in exploring different
teaching methods and instructional strategies. This may involve studying
the effectiveness of lectures, online tutorials, or peer-to-peer
learning.
Diversity and inclusion
Computer
science education researchers are interested in promoting diversity and
inclusion in computer science education. This may involve studying the
factors that contribute to under representation of certain groups in
computer science, and developing interventions to promote inclusivity
and equity.
Research communities
Top 50 computer science universities in North America
The Association for Computing Machinery (ACM) runs a Special Interest Group (SIG) on Computer science education known as SIGCSE which celebrated its 50th anniversary in 2018, making it one of the oldest and longest running ACM Special Interest Groups. An outcome of computing education research are Parsons problems.
Gender perspectives in computer science education
In many countries, there is a significant gender gap in computer
science education. In 2015, 15.3% of computer science students
graduating from non-doctoral granting institutions in the US were women
while at doctoral granting institutions, the figure was 16.6%. The number of female PhD recipients in the US was 19.3% in 2018. In almost everywhere in the world, less than 20% of the computer science graduates are female.
This problem mainly arises due to the lack of interests of girls
in computing starting from the primary level. Despite numerous efforts
by programs specifically designed to increase the ratio of women in this
field, no significant improvement has been observed. Furthermore, a
declining trend has been noticed in the involvement of women in past
decades.
The main reason for the failure of these programs is because
almost all of them focused on girls in high school or higher levels of
education. Researchers argue that by then women have already made up
their mind and stereotypes start to form about computer scientists.
Computer Science is perceived as a male dominated field, pursued by
people who are nerdy and lack social skills.
All these characteristics seem to be more damaging for a woman as
compared to a man. Therefore, to break these stereotypes and to engage
more women in computer science, it is crucial that there are special
outreach programs designed to develop interest in girls starting at the
middle school level and prepare them for an academic track towards the
hard sciences.
Evidently, there are a few countries in Asia and Africa where
these stereotypes do not exist and women are encouraged for a career in
science starting at the primary level, thus resulting in a gender gap
that is virtually nonexistent. In 2011, women earned half of the
computer science degrees in Malaysia. In 2001, 55 percent of computer science graduates in Guyana were women.
Recent Trends and Developments
Recently,
computational education has had an increased emphasis on incorporating
computational knowledge into education on all levels. This is due to the
world becoming more and more technologically driven. Organizations like
Code.org and initiatives like the Hour of Code, and massive open online courses
(MOOCs) have played a significant role in promoting computer science
education and making coding accessible to students worldwide; especially
making a difference for women, underprivileged and underrepresented
communities. These online learning platforms have also made computing
education more accessible, allowing individuals to learn coding
remotely. Additionally, we see technology increasingly being found in
numerous fields like health, business and technology.
Challenges
Over
the years, computing education has faced many various issues that have
in one way or another contributed to its unpopularity. One of the most
impactful of these issues is the equipment cost of effectively teaching
the discipline.
In the past, there were not many affordable options for providing
computers for each and every student that wanted to learn the
discipline. Due to this, computing education suffered in many areas with
little to no funding left over to adequately teach the subject.
This is the main reason why computing education is either extremely
lackluster or non-existent in many schools across the United States and
UK. The subject's unpopularity for many years mostly stems from it being
reserved for those who could afford the necessary equipment and
software to effectively teach it.
There have also been issues with finding and training good
teachers for the subject. Many schools in the past didn't see the value
in paying for training for teachers to be able to teach computer science
or get the licenses required. This has led to many schools in
disadvantaged areas, or simply areas with not a lot of people, to
struggle to hire the teachers necessary to provide a good computer
science curriculum.
Another issue with the teacher side of the discipline is the nature of
computer science itself, and that a standard teaching structure using
slides and textbooks has often been found to be ineffective. Computer
science is a very problem solving oriented subject and it has often been
found that teaching can be more effective when approaching it from this
perspective rather than the standard lecture format.
Computer science is also notorious for being a very difficult
subject in schools, with high failure and dropout rates over the years
it has been taught.
This is usually attributed to the fact that computer science as a
subject is very problem-solving heavy and a lot of students can struggle
with this aspect. This is especially true for high-school, where few
other subjects demand as high caliber of problem-solving ability as
computer science. This is compounded by the fact that computer science
is a very different discipline from most other subjects, meaning that
many students who encounter it for the first time can struggle a lot.
Despite the challenges faced by the discipline, computer science
continues to grow in popularity as a subject as technology grows and
computers become more and more important in the classroom as well as in
everyday life.
Algorithms and data structures are central to computer science.
The theory of computation concerns abstract models of computation and general classes of problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and preventing security vulnerabilities. Computer graphics and computational geometry address the generation of images. Programming language theory considers different ways to describe computational processes, and database theory concerns the management of repositories of data. Human–computer interaction
investigates the interfaces through which humans and computers
interact, and software engineering focuses on the design and principles
behind developing software. Areas such as operating systems, networks and embedded systems investigate the principles and design behind complex systems. Computer architecture describes the construction of computer components and computer-operated equipment. Artificial intelligence and machine learning aim to synthesize goal-orientated processes such as problem-solving, decision-making, environmental adaptation, planning and learning found in humans and animals. Within artificial intelligence, computer vision aims to understand and process image and video data, while natural language processing aims to understand and process textual and linguistic data.
The fundamental concern of computer science is determining what can and cannot be automated. The Turing Award is generally recognized as the highest distinction in computer science.
The earliest foundations of what would become computer science predate the invention of the modern digital computer. Machines for calculating fixed numerical tasks such as the abacus have existed since antiquity, aiding in computations such as multiplication and division. Algorithms for performing computations have existed since antiquity, even before the development of sophisticated computing equipment.
Wilhelm Schickard designed and constructed the first working mechanical calculator in 1623. In 1673, Gottfried Leibniz demonstrated a digital mechanical calculator, called the Stepped Reckoner. Leibniz may be considered the first computer scientist and information theorist, because of various reasons, including the fact that he documented the binary number system. In 1820, Thomas de Colmar launched the mechanical calculator industry when he invented his simplified arithmometer, the first calculating machine strong enough and reliable enough to be used daily in an office environment. Charles Babbage started the design of the first automatic mechanical calculator, his Difference Engine, in 1822, which eventually gave him the idea of the first programmable mechanical calculator, his Analytical Engine. He started developing this machine in 1834, and "in less than two years, he had sketched out many of the salient features of the modern computer". "A crucial step was the adoption of a punched card system derived from the Jacquard loom" making it infinitely programmable. In 1843, during the translation of a French article on the Analytical Engine, Ada Lovelace wrote, in one of the many notes she included, an algorithm to compute the Bernoulli numbers, which is considered to be the first published algorithm ever specifically tailored for implementation on a computer. Around 1885, Herman Hollerith invented the tabulator, which used punched cards to process statistical information; eventually his company became part of IBM. Following Babbage, although unaware of his earlier work, Percy Ludgate in 1909 published the 2nd of the only two designs for mechanical analytical engines in history. In 1914, the Spanish engineer Leonardo Torres Quevedo published his Essays on Automatics,
and designed, inspired by Babbage, a theoretical electromechanical
calculating machine which was to be controlled by a read-only program.
The paper also introduced the idea of floating-point arithmetic. In 1920, to celebrate the 100th anniversary of the invention of the arithmometer, Torres presented in Paris the Electromechanical Arithmometer, a prototype that demonstrated the feasibility of an electromechanical analytical engine, on which commands could be typed and the results printed automatically. In 1937, one hundred years after Babbage's impossible dream, Howard Aiken convinced IBM, which was making all kinds of punched card equipment and was also in the calculator business to develop his giant programmable calculator, the ASCC/Harvard Mark I,
based on Babbage's Analytical Engine, which itself used cards and a
central computing unit. When the machine was finished, some hailed it as
"Babbage's dream come true".
During the 1940s, with the development of new and more powerful computing machines such as the Atanasoff–Berry computer and ENIAC, the term computer came to refer to the machines rather than their human predecessors.
As it became clear that computers could be used for more than just
mathematical calculations, the field of computer science broadened to
study computation in general. In 1945, IBM founded the Watson Scientific Computing Laboratory at Columbia University in New York City.
The renovated fraternity house on Manhattan's West Side was IBM's first
laboratory devoted to pure science. The lab is the forerunner of IBM's
Research Division, which today operates research facilities around the
world.
Ultimately, the close relationship between IBM and Columbia University
was instrumental in the emergence of a new scientific discipline, with
Columbia offering one of the first academic-credit courses in computer
science in 1946. Computer science began to be established as a distinct academic discipline in the 1950s and early 1960s. The world's first computer science degree program, the Cambridge Diploma in Computer Science, began at the University of Cambridge Computer Laboratory in 1953. The first computer science department in the United States was formed at Purdue University in 1962.
Since practical computers became available, many applications of
computing have become distinct areas of study in their own rights.
Although first proposed in 1956, the term "computer science" appears in a 1959 article in Communications of the ACM,
in which Louis Fein argues for the creation of a Graduate School in Computer Sciences analogous to the creation of Harvard Business School in 1921. Louis justifies the name by arguing that, like management science, the subject is applied and interdisciplinary in nature, while having the characteristics typical of an academic discipline.
His efforts, and those of others such as numerical analystGeorge Forsythe, were rewarded: universities went on to create such departments, starting with Purdue in 1962.
Despite its name, a significant amount of computer science does not
involve the study of computers themselves. Because of this, several
alternative names have been proposed. Certain departments of major universities prefer the term computing science, to emphasize precisely that difference. Danish scientist Peter Naur suggested the term datalogy,
to reflect the fact that the scientific discipline revolves around data
and data treatment, while not necessarily involving computers. The
first scientific institution to use the term was the Department of
Datalogy at the University of Copenhagen, founded in 1969, with Peter
Naur being the first professor in datalogy. The term is used mainly in
the Scandinavian countries. An alternative term, also proposed by Naur,
is data science; this is now used for a multi-disciplinary field of data analysis, including statistics and databases.
In the early days of computing, a number of terms for the practitioners of the field of computing were suggested in the Communications of the ACM—turingineer, turologist, flow-charts-man, applied meta-mathematician, and applied epistemologist. Three months later in the same journal, comptologist was suggested, followed next year by hypologist. The term computics has also been suggested. In
Europe, terms derived from contracted translations of the expression
"automatic information" (e.g. "informazione automatica" in Italian) or
"information and mathematics" are often used, e.g. informatique (French), Informatik (German), informatica (Italian, Dutch), informática (Spanish, Portuguese), informatika (Slavic languages and Hungarian) or pliroforiki (πληροφορική, which means informatics) in Greek. Similar words have also been adopted in the UK (as in the School of Informatics, University of Edinburgh). "In the U.S., however, informatics is linked with applied computing, or computing in the context of another domain."
A folkloric quotation, often attributed to—but almost certainly not first formulated by—Edsger Dijkstra, states that "computer science is no more about computers than astronomy is about telescopes."
The design and deployment of computers and computer systems is
generally considered the province of disciplines other than computer
science. For example, the study of computer hardware is usually
considered part of computer engineering, while the study of commercial computer systems and their deployment is often called information technology or information systems.
However, there has been exchange of ideas between the various
computer-related disciplines. Computer science research also often
intersects other disciplines, such as cognitive science, linguistics, mathematics, physics, biology, Earth science, statistics, philosophy, and logic.
Computer science is considered by some to have a much closer
relationship with mathematics than many scientific disciplines, with
some observers saying that computing is a mathematical science. Early computer science was strongly influenced by the work of mathematicians such as Kurt Gödel, Alan Turing, John von Neumann, Rózsa Péter and Alonzo Church and there continues to be a useful interchange of ideas between the two fields in areas such as mathematical logic, category theory, domain theory, and algebra.
The relationship between computer science and software engineering is a contentious issue, which is further muddied by disputes over what the term "software engineering" means, and how computer science is defined. David Parnas,
taking a cue from the relationship between other engineering and
science disciplines, has claimed that the principal focus of computer
science is studying the properties of computation in general, while the
principal focus of software engineering is the design of specific
computations to achieve practical goals, making the two separate but
complementary disciplines.
The academic, political, and funding aspects of computer science
tend to depend on whether a department is formed with a mathematical
emphasis or with an engineering emphasis. Computer science departments
with a mathematics emphasis and with a numerical orientation consider
alignment with computational science. Both types of departments tend to make efforts to bridge the field educationally if not across all research.
Despite the word science in its name, there is debate over whether or not computer science is a discipline of science, mathematics, or engineering. Allen Newell and Herbert A. Simon argued in 1975,
Computer
science is an empirical discipline. We would have called it an
experimental science, but like astronomy, economics, and geology, some
of its unique forms of observation and experience do not fit a narrow
stereotype of the experimental method. Nonetheless, they are
experiments. Each new machine that is built is an experiment. Actually
constructing the machine poses a question to nature; and we listen for
the answer by observing the machine in operation and analyzing it by all
analytical and measurement means available.
It has since been argued that computer science can be classified as an
empirical science since it makes use of empirical testing to evaluate
the correctness of programs,
but a problem remains in defining the laws and theorems of computer
science (if any exist) and defining the nature of experiments in
computer science.
Proponents of classifying computer science as an engineering discipline
argue that the reliability of computational systems is investigated in
the same way as bridges in civil engineering and airplanes in aerospace engineering.
They also argue that while empirical sciences observe what presently
exists, computer science observes what is possible to exist and while
scientists discover laws from observation, no proper laws have been
found in computer science and it is instead concerned with creating
phenomena.
Proponents of classifying computer science as a mathematical
discipline argue that computer programs are physical realizations of
mathematical entities and programs that can be deductively reasoned through mathematical formal methods. Computer scientists Edsger W. Dijkstra and Tony Hoare
regard instructions for computer programs as mathematical sentences and
interpret formal semantics for programming languages as mathematical axiomatic systems.
Paradigms of computer science
A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics. Peter Denning's working group argued that they are theory, abstraction (modeling), and design.
Amnon H. Eden described them as the "rationalist paradigm" (which
treats computer science as a branch of mathematics, which is prevalent
in theoretical computer science, and mainly employs deductive
reasoning), the "technocratic paradigm" (which might be found in
engineering approaches, most prominently in software engineering), and
the "scientific paradigm" (which approaches computer-related artifacts
from the empirical perspective of natural sciences, identifiable in some branches of artificial intelligence).
Computer science focuses on methods involved in design, specification,
programming, verification, implementation and testing of human-made
computing systems.
As a discipline, computer science spans a range of topics from
theoretical studies of algorithms and the limits of computation to the
practical issues of implementing computing systems in hardware and
software.CSAB, formerly called Computing Sciences Accreditation Board—which is made up of representatives of the Association for Computing Machinery (ACM), and the IEEE Computer Society (IEEE CS)—identifies four areas that it considers crucial to the discipline of computer science: theory of computation, algorithms and data structures, programming methodology and languages, and computer elements and architecture.
In addition to these four areas, CSAB also identifies fields such as
software engineering, artificial intelligence, computer networking and
communication, database systems, parallel computation, distributed
computation, human–computer interaction, computer graphics, operating
systems, and numerical and symbolic computation as being important areas of computer science.
Theoretical computer science is mathematical and abstract in
spirit, but it derives its motivation from practical and everyday
computation. It aims to understand the nature of computation and, as a
consequence of this understanding, provide more efficient methodologies.
According to Peter Denning, the fundamental question underlying computer science is, "What can be automated?"
Theory of computation is focused on answering fundamental questions
about what can be computed and what amount of resources are required to
perform those computations. In an effort to answer the first question, computability theory examines which computational problems are solvable on various theoretical models of computation. The second question is addressed by computational complexity theory, which studies the time and space costs associated with different approaches to solving a multitude of computational problems.
Information theory, closely related to probability and statistics, is related to the quantification of information. This was developed by Claude Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data.
Coding theory is the study of the properties of codes (systems for converting information from one form to another) and their fitness for a specific application. Codes are used for data compression, cryptography, error detection and correction, and more recently also for network coding. Codes are studied for the purpose of designing efficient and reliable data transmission methods.
Programming language theory is a branch of computer science that
deals with the design, implementation, analysis, characterization, and
classification of programming languages and their individual features. It falls within the discipline of computer science, both depending on and affecting mathematics, software engineering, and linguistics. It is an active research area, with numerous dedicated academic journals.
Formal methods are a particular kind of mathematically based technique for the specification, development and verification of software and hardware systems.
The use of formal methods for software and hardware design is motivated
by the expectation that, as in other engineering disciplines,
performing appropriate mathematical analysis can contribute to the
reliability and robustness of a design. They form an important
theoretical underpinning for software engineering, especially where
safety or security is involved. Formal methods are a useful adjunct to
software testing since they help avoid errors and can also give a
framework for testing. For industrial use, tool support is required.
However, the high cost of using formal methods means that they are
usually only used in the development of high-integrity and life-critical systems, where safety or security is of utmost importance. Formal methods are best described as the application of a fairly broad variety of theoretical computer science fundamentals, in particular logic calculi, formal languages, automata theory, and program semantics, but also type systems and algebraic data types to problems in software and hardware specification and verification.
Computer graphics is the study of digital visual contents and
involves the synthesis and manipulation of image data. The study is
connected to many other fields in computer science, including computer vision, image processing, and computational geometry, and is heavily applied in the fields of special effects and video games.
Scientific computing (or computational science) is the field of study concerned with constructing mathematical models and quantitative analysis techniques and using computers to analyze and solve scientific problems. A major usage of scientific computing is simulation of various processes, including computational fluid dynamics,
physical, electrical, and electronic systems and circuits, as well as
societies and social situations (notably war games) along with their
habitats, among many others. Modern computers enable optimization of
such designs as complete aircraft. Notable in electrical and electronic
circuit design are SPICE, as well as software for physical realization of new (or modified) designs. The latter includes essential design software for integrated circuits.
Software engineering is the study of designing, implementing, and
modifying the software in order to ensure it is of high quality,
affordable, maintainable, and fast to build. It is a systematic approach
to software design, involving the application of engineering practices
to software. Software engineering deals with the organizing and
analyzing of software—it does not just deal with the creation or
manufacture of new software, but its internal arrangement and
maintenance. For example software testing, systems engineering, technical debt and software development processes.
Artificial intelligence (AI) aims to or is required to synthesize
goal-orientated processes such as problem-solving, decision-making,
environmental adaptation, learning, and communication found in humans
and animals. From its origins in cybernetics and in the Dartmouth Conference (1956), artificial intelligence research has been necessarily cross-disciplinary, drawing on areas of expertise such as applied mathematics, symbolic logic, semiotics, electrical engineering, philosophy of mind, neurophysiology, and social intelligence. AI is associated in the popular mind with robotic development, but the main field of practical application has been as an embedded component in areas of software development, which require computational understanding. The starting point in the late 1940s was Alan Turing's question "Can computers think?", and the question remains effectively unanswered, although the Turing test
is still used to assess computer output on the scale of human
intelligence. But the automation of evaluative and predictive tasks has
been increasingly successful as a substitute for human monitoring and
intervention in domains of computer application involving complex
real-world data.
Computer architecture, or digital computer organization, is the
conceptual design and fundamental operational structure of a computer
system. It focuses largely on the way by which the central processing
unit performs internally and accesses addresses in memory. Computer engineers study computational logic and design of computer hardware, from individual processor components, microcontrollers, personal computers to supercomputers and embedded systems. The term "architecture" in computer literature can be traced to the work of Lyle R. Johnson and Frederick P. Brooks Jr., members of the Machine Organization department in IBM's main research center in 1959.
Concurrency is a property of systems in which several computations
are executing simultaneously, and potentially interacting with each
other. A number of mathematical models have been developed for general concurrent computation including Petri nets, process calculi and the parallel random access machine model.
When multiple computers are connected in a network while using
concurrency, this is known as a distributed system. Computers within
that distributed system have their own private memory, and information
can be exchanged to achieve common goals.
Computer security is a branch of computer technology with the
objective of protecting information from unauthorized access,
disruption, or modification while maintaining the accessibility and
usability of the system for its intended users.
A database is intended to organize, store, and retrieve large amounts
of data easily. Digital databases are managed using database management
systems to store, create, maintain, and search data, through database models and query languages. Data mining is a process of discovering patterns in large data sets.
Discoveries
The philosopher of computing Bill Rapaport noted three Great Insights of Computer Science:
All the information about any computable problem can be
represented using only 0 and 1 (or any other bistable pair that can
flip-flop between two easily distinguishable states, such as "on/off",
"magnetized/de-magnetized", "high-voltage/low-voltage", etc.).
Corrado Böhm and Giuseppe Jacopini's insight: there are only three ways of combining these actions (into more complex ones) that are needed in order for a computer to do "anything".
Only three rules are needed to combine any set of basic instructions into more complex ones:
sequence: first do this, then do that;
selection: IF such-and-such is the case, THEN do this, ELSE do that;
repetition: WHILE such-and-such is the case, DO this.
The three rules of Boehm's and Jacopini's insight can be further simplified with the use of goto (which means it is more elementary than structured programming).
Programming languages can be used to accomplish different tasks in different ways. Common programming paradigms include:
Functional programming,
a style of building the structure and elements of computer programs
that treats computation as the evaluation of mathematical functions and
avoids state and mutable data. It is a declarative programming paradigm,
which means programming is done with expressions or declarations
instead of statements.
Imperative programming, a programming paradigm that uses statements that change a program's state.
In much the same way that the imperative mood in natural languages
expresses commands, an imperative program consists of commands for the
computer to perform. Imperative programming focuses on describing how a
program operates.
Object-oriented programming,
a programming paradigm based on the concept of "objects", which may
contain data, in the form of fields, often known as attributes; and
code, in the form of procedures, often known as methods. A feature of
objects is that an object's procedures can access and often modify the
data fields of the object with which they are associated. Thus
object-oriented computer programs are made out of objects that interact
with one another.
Service-oriented programming,
a programming paradigm that uses "services" as the unit of computer
work, to design and implement integrated business applications and mission critical software programs.
Many languages offer support for multiple paradigms, making the
distinction more a matter of style than of technical capabilities.
Conferences are important events for computer science research.
During these conferences, researchers from the public and private
sectors present their recent work and meet. Unlike in most other
academic fields, in computer science, the prestige of conference papers is greater than that of journal publications.
One proposed explanation for this is the quick development of this
relatively new field requires rapid review and distribution of results, a
task better handled by conferences than by journals.
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