Computational linguistics

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Computational Linguistics
Definition and Nature-
We live in the age of information. We are surrounded by newspapers, magazines, radio,
loudspeakers, TV and computer screens. The main part of this information has the form of
natural language texts. Our ancestors invented natural language many thousands of years ago.
Now we are trying to use natural language to exchange information with a creature of a totally
different nature – the computer. Human are trying the automation of natural language processing.
People now want assistance not only in mechanical but also in intellectual efforts. They would
like the machine to read an unprepared text, to test it for correctness, to execute the instructions
contained in the text, or even to interpret or comprehend a text. The processing of natural
language has become one of the main problems in information exchange. The rapid development
of computers in the last two decades has made possible the implementation of many ideas to
solve the problems.
 Definition - What does Computational Linguistics mean?
Computational linguistics involves looking at the ways that a machine would treat natural
language, or in other words, dealing with or constructing models for language that can allow for
goals such as accurate machine translation of language, or the simulation of artificial
intelligence.
1) “Intelligent natural language processing is based on the science called
computational linguistics.”
2) “ The branch of linguistics in which the techniques of computer science are
applied to the analysis and synthesis of language and speech.”
Computational linguistics is closely connected with applied linguistics and linguistics in general.
Computational linguistics is the scientific and engineering discipline concerned with
understanding written and spoken language from a computational perspective, and building
artifacts that usefully process and produce language, either in bulk or in a dialogue setting. To
the extent that language is a mirror of mind, a computational understanding of language also
provides insight into thinking and intelligence. And since language is our most natural and most
versatile means of communication, linguistically competent computers would greatly facilitate
our interaction with machines and software of all sorts, and put at our fingertips, in ways that
truly meet our needs, the vast textual and other resources of the internet.
Generally, computational linguistics involves looking at the nature of a language, its
morphology, syntax, and dynamic use, and drawing any possible useful models from this
observation in order to help machines to handle language. Experts point out that the evolving
models for computational linguistics are much more difficult than the uses to which computers
were first applied, namely, the handling of quantitative data.

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Practical applications of computational linguistics include text-to-speech software, which
attempts to understand what someone is saying in order to translate it into digital text. There are
also various models that have been worked out that allow for using machines to understand how
language is acquired. However, the end goal of enabling machines to really simulate human
linguistic responses that go along with "higher-level thought" has been a continuing evolution
that still leaves a lot of room for improvement. One theory is that machines will eventually
acquire the ability to simulate real human conversation, and with it, will become generally more
intelligent through heuristic models and other processes that go far beyond the mere acquisition
of data and quantitative computations of that data.
Computational linguistics means automatic processing of natural language with the
help of computers. The main task of computational linguistics is the construction or
creation of computer programs to process words and texts in natural language. Computer
Assisted Language Learning (CALL) is the field concerned with the use of computer tools in
second language acquisition. A linguist Weaver had proposed the use of computers for
translation, thus initiating research in Machine Translation (MT).
Computational linguistics is an interdisciplinary field concerned with the statistical or
rule-based modeling of natural language from a computational perspective, as well as the study
of appropriate computational approaches to linguistic questions.
Traditionally, computational linguistics was performed by computer scientists who had
specialized in the application of computers to the processing of a natural language. Today,
computational linguists often work as members of interdisciplinary teams, which can include
regular linguists, experts in the target language, and computer scientists. In general,
computational linguistics draws upon the involvement of linguists, computer scientists, experts
in artificial intelligence, mathematicians, logicians, philosophers, cognitive scientists, cognitive
psychologists, psycholinguists, anthropologists and neuroscientists, among others.
Computational linguistics has theoretical and applied components. Theoretical computational
linguistics focuses on issues in theoretical linguistics and cognitive science, and applied
computational linguistics focuses on the practical outcome of modeling human language use.
The Association for Computational Linguistics defines computational linguistics as:
“The scientific study of language from a computational perspective.” Computational
linguists are interested in providing computational models of various kinds of linguistic
phenomena.
Origins-
Computational linguistics is often grouped within the field of artificial intelligence (AI), but
actually was present before the development of artificial intelligence. Computational linguistics
originated with efforts in the United States in the 1950s to use computers to automatically
translate texts from foreign languages, particularly Russian scientific journals, into
English. Since computers can make arithmetic calculations much faster and more accurately
than humans, it was thought to be only a short matter of time before they could also begin to
process language. Computational and quantitative methods are also used historically in attempted
reconstruction of earlier forms of modern languages and sub grouping modern languages into
language families. Earlier methods such as lexicostatistics and glottochronology have been

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proven to be premature and inaccurate. However, recent interdisciplinary studies which borrow
concepts from biological studies, especially gene mapping, have proved to produce more
sophisticated analytical tools and more trustful results.
When machine translation (MT) (also known as mechanical translation) failed to yield accurate
translations right away, automated processing of human languages was recognized as far more
complex than had originally been assumed. Computational linguistics was born as the name of
the new field of study devoted to developing algorithms and software for intelligently
processing language data. The term "computational linguistics" itself was first coined by David
Hays, founding member of both the Association for Computational Linguistics and
the International Committee on Computational Linguistics. When artificial intelligence came
into existence in the 1960s, the field of computational linguistics became that sub-division of
artificial intelligence dealing with human-level comprehension and production of natural
languages.
In order to translate one language into another, it was observed that one had to understand
the grammar of both languages, including both morphology (the grammar of word forms)
and syntax (the grammar of sentence structure). In order to understand syntax, one had to also
understand the semantics and the lexicon (or 'vocabulary'), and even something of
the pragmatics of language use. Thus, what started as an effort to translate between languages
evolved into an entire discipline devoted to understanding how to represent and process
natural languages using computers.
Nowadays research within the scope of computational linguistics is done at computational
linguistics departments, computational linguistics laboratories, computer
science departments, and linguistics departments.Some research in the field of computational
linguistics aims to create working speechor text processing systems while others aim to
create a systemallowing human-machine interaction. Programs meant for human-machine
communication are called conversational agents
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ELIZA- A computer programme
In a now famous paper published in 1950 Alan Turing proposed the possibility that
machines might one day have the ability to "think". As a thought experiment for what might
define the concept of thought in machines, he proposed an "imitation test" in which a human
subject has two text-only conversations, one with a fellow human and another with a machine
attempting to respond like a human. Turing proposes that if the subject cannot tell the difference
between the human and the machine, it may be concluded that the machine is capable of
thought.[24] Today this test is known as the Turing test and it remains an influential idea in the
area of artificial intelligence.
One of the earliest and best known examples of a computer program designed to
converse naturally with humans is the ELIZA program developed by Joseph
Weizenbaum at MIT in 1966. The program emulated a Rogerian psychotherapist when
responding to written statements and questions posed by a user. It appeared capable of
understanding what was said to it and responding intelligently, but in truth it simply followed a
pattern matching routine that relied on only understanding a few keywords in each sentence. Its
responses were generated by recombining the unknown parts of the sentence around properly

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translated versions of the known words. For example, in the phrase "It seems that you hate me"
ELIZA understands "you" and "me" which matches the general pattern "you [some words] me",
allowing ELIZA to update the words "you" and "me" to "I" and "you" and replying "What makes
you think I hate you?". In this example ELIZA has no understanding of the word "hate", but it is
not required for a logical response in the context of this type of psychotherapy.
This type of work is specific to computational linguistics, and has applications which
could vastly improve understanding of how language is produced and comprehended by
computers making human-computer interaction much more natural.
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SHRDLU- A computer programme
In 1971 Terry Winograd developed an early natural language processing engine capable
of interpreting naturally written commands within a simple rule governed environment.
The primary language parsing program in this project was called SHRDLU, which was capable
of carrying out a somewhat natural conversation with the user giving it commands, but only
within the scope of the toy environment designed for the task. This environment consisted of
different shaped and colored blocks, and SHRDLU was capable of interpreting commands such
as "Find a block which is taller than the one you are holding and put it into the box." and asking
questions such as "I don't understand which pyramid you mean." in response to the user's
input.While impressive, this kind of natural language processing has proven much more difficult
outside the limited scope of the toy environment. Similarly a project developed
by NASA called LUNAR was designed to provide answers to naturally written questions about
the geological analysis of lunar rocks returned by the Apollo missions. These kinds of problems
are referred to as question answering.
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Natural Language Processing
Definition - What does Natural Language Processing (NLP)mean?-
Natural Language Processing (NLP) is an area of research and application that explores how
computers can be used to understand and manipulate natural language text or speechto do
useful things. Natural language processing (NLP) is a method to translate between
computer and human languages. It is a method of getting a computer to understandably read a
line of text without the computer being fed some sort of clue or calculation. In other words, NLP
automates the translation process between computers and humans.
The field of study that focuses on the interactions between human language and computers is
called Natural Language Processing, or NLP for short. It sits at the intersection of computer
science, artificial intelligence, and computational linguistics
“Natural Language Processing is a field that covers computer understanding and manipulation of
human language, and it’s ripe with possibilities for newsgathering,”

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What is Natural Language Processing?-
NLP is a way for computers to analyze, understand, and derive meaning from human
language in a smart and useful way. By utilizing NLP, developers can organize and structure
knowledge to perform tasks such as automatic summarization, translation, named entity
recognition, relationship extraction, sentiment analysis, speech recognition, and topic
segmentation.
NLP is used to analyze text, allowing machines to understand how human’s speak. This human-
computer interaction enables real-world applications like automatic text
summarization, sentiment analysis, topic extraction, named entity recognition, parts-of-speech
tagging, relationship extraction, stemming, and more. NLP is commonly used for text
mining, machine translation, and automated question answering.
NLP is characterized as a hard problem in computer science. Human language is rarely precise,
or plainly spoken. To understand human language is to understand not only the words, but the
concepts and how they’re linked together to create meaning. Despite language being one of the
easiest things for humans to learn, the ambiguity of language is what makes natural language
processing a difficult problem for computers to master.
NLP researchers are trying to gather knowledge on how human beings understand and
use language so that appropriate tools and techniques can be developed to make computer
systems understand and manipulate natural languages to perform the desired tasks. NLP is used
in the fields like computer and information sciences, linguistics, maths, electrical and electronic
engineering, artificial intelligence and robotics, psychology, etc. Applications of NLP include a
number of fields of studies like machine translation, natural language text processing and
summarization, user interfaces, multilingual and cross language information retrieval (CLIR),
speech recognition, artificial intelligence, etc.
Traditionally, feeding statistics and models have been the method of choice for interpreting
phrases. Recent advances in this area include voice recognition software, human language
translation, information retrieval and artificial intelligence. There is difficulty in developing
human language translation software because language is constantly changing. Natural language
processing is also being developed to create human readable text and to translate between one
human language and another. The ultimate goal of NLP is to build software that will analyze,
understand and generate human languages naturally, enabling communication with a computer as
if it were a human.
Natural language processing gives machines the ability to read and understand human
language. A sufficiently powerful natural language processing system would enable natural
language user interfaces and the acquisition of knowledge directly from human-written sources,
such as newswire texts. Some straightforward applications of natural language processing
include information retrieval, text mining, question answering and machine translation. A
common method of processing and extracting meaning from natural language is
through semantic indexing. Although these indexes require a large volume of user input, it is

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expected that increases in processor speeds and decreases in data storage costs will result in
greater efficiency.
Machine perception is the ability to use input from sensors (such as cameras,
microphones, tactile sensors, sonar and others) to deduce aspects of the world. Computer
vision[ is the ability to analyze visual input. A few selected sub problems are speech
recognition, facial recognition and object recognition.
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Machine translation-
Machine translation (MT) is a sub-field of computational linguistics that investigates the
use of software to translate text or speech from one language to another. Machine translation
is the process of translating from source language text into the target language. The
term machine translation (MT) is used in the sense of translation of one language to another.
The ideal aim of machine translation systems is to produce the best possible translation without
human assistance. Basically every machine translation system requires programs for translation
and automated dictionaries and grammars to support translation.
The translation quality of the machine translation systems can be improved by pre-editing the
input. Pre-editing means adjusting the input by marking prefixes, suffixes, clause boundaries,
etc. Translation quality can also be improved by controlling the vocabulary. The output of the
machine translation should be post-edited to make it perfect. Post-editing is required especially
for health related information. Machine translation systems that produce translations between
only two particular languages are called bilingual systems and those that produce translations for
any given pair of languages are called multilingual systems. Multilingual systems may be either
uni-directional or bi-directional. Multilingual systems are preferred to be bi-directional and bi-
lingual as they have ability to translate from any given language to any other given language and
vice versa.
MT performs simple substitution of words in one language for words in another, but that alone
usually cannot produce a good translation of a text because recognition of whole phrases and
their closest counterparts in the target language is needed. Solving this problem
with corpus statistical, and neural techniques is a rapidly growing field that is leading to better
translations, handling differences in linguistic typology, translation of idioms, and the isolation
of anomalies. Therein lies the challenge in machine translation: how to program a computer that
will "understand" a text as a person does, and that will "create" a new text in the target language
that sounds as if it has been written by a person.
One of the oldest MT (Machine translation)systems is SYSTRAN, Google Translate . As
mentioned, Google Translate currently uses phrase-based MT, with English serving as an
interlingua for the majority of language pairs. Microsoft's Bing Translator employs dependency
structure analysis together with statistical MT. Other very comprehensive translation systems
include Asia Online and WorldLingo. Many systems for small language groups exist as well, for
instance for translating between Punjabi and Hindi (the Direct MT system), or between a few
European languages (e.g., OpenLogos, IdiomaX, and GramTrans).

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Translations remain error-prone, but their quality is usually sufficient for readers to grasp
the general drift of the source contents. No more than that may be required in many cases, such
as international web browsing (an application scarcely anticipated in decades of MT research).
Also, MT applications on hand-held devices, designed to aid international travellers, can be
sufficiently accurate for limited purposes such as asking directions or emergency help,
interacting with transportation personnel, or making purchases or reservations, When high-
quality translations are required, automatic methods can be used as an aid to human translators,
but subtle issues may still absorb a large portion of a translator's time.
Following is a list of challenges one has to face when attempt to do machine translation.
1.Not all the words in one language have equivalent words in another language. In some cases a
word in one language is to be expressed by group of words in another.
2.Two given languages may have completely different structures. For example English has SVO
structure while Tamil has SOV structure.
3.Sometimes there is a lack of one-to-one correspondence of parts of speech between two
languages. For example, color terms of Tamil are nouns whereas in English they are adjectives.
4.The way sentences are put together also differ among languages.
5.Words can have more than one meaning and sometimes group of words or whole sentence may
have more than one meaning in a language. This problem is called ambiguity.
6.Not all the translation problems can be solved by applying values of grammar.
7.It is too difficult for the software programs to predict meaning.
8.Translation requires not only vocabulary and grammar but also knowledge gathered from past
experience.
9.The programmer should understand the rules under which complex human language operates
and how the mechanism of this operation can be simulated by automatic means.
10.The simulation of human language behavior by automatic means is almost impossible to
achieve as the language is open and dynamic system in constant change. More importantly the
system is not yet completely understood.
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Document retrieval -
Information retrieval has long been a central theme of information science, covering retrieval of
both structured data such as are found in relational databases as well as unstructured text
documents. Retrieval criteria for the two types of data are not unrelated, since both structured
and unstructured data often require content-directed retrieval. For example, while users of an

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employee database may wish at times to retrieve employee records by the unique name or ID of
employees, at other times they may wish to retrieve all employees in a certain employment
category, perhaps with further restrictions such as falling into a certain salary bracket. This is
accomplished with the use of “inverted files” that essentially index entities under their attributes
and values rather than their identifiers. In the same way, text documents might be
retrieved via some unique label, or they might instead be retrieved in accord with
their relevance to a certain query or topic header. The simplest notion of relevance is that the
documents should contain the terms (words or short phrases) of the query. However, terms that
are distinctive for a document should be given more weight. Therefore a standard measure of
relevance, given a particular query term, is the tf–idf (term frequency–inverse document
frequency) for the term, which increases (e.g., logarithmically) with the frequency of occurrences
of the term in the document but is discounted to the extent that it occurs frequently in the set of
documents as a whole. Summing the tf-idf's of the query terms yields a simple measure of
document relevance.
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Artificial Intelligence
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated
by machines. It is in contrast to the natural intelligence (NI) present in humans and other
animals. In computer science AI research is defined as the study of "intelligent agents": any
device (machine) that perceives its environment and takes actions that maximize its chance of
successfully achieving its goals.] Colloquially, the term "artificial intelligence" is applied when a
machine mimics (copies) "cognitive" functions that humans associate with other human minds,
such as "learning" and "problem solving".
Since the invention of computers or machines, their capability to perform various tasks
went on growing exponentially. Humans have developed the power of computer systems in
terms of their diverse working domains, their increasing speed, and reducing size with respect to
time.
A branch of Computer Science named Artificial Intelligencepursues creating the
computers or machines as intelligent as human beings.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and
engineering of making intelligent machines, especially intelligent computer programs”.
Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a
software think intelligently, in the similar manner the intelligent humans think.
AI is accomplished by studying how human brain thinks, and how humans learn, decide, and
work while trying to solve a problem, and then using the outcomes of this study as a basis of
developing intelligent software and systems.
Philosophy of AI-
While exploiting the power of the computer systems, the curiosity of human, lead him to
wonder, “Can a machine think and behave like humans do?”

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Thus, the development of AI started with the intention of creating similar intelligence in
machines that we find and regard high in humans.
Goals of AI-
 To Create Expert Systems − The systems which exhibit intelligent behavior, learn,
demonstrate, explain, and advice its users.
 To Implement Human Intelligence in Machines − Creating systems that understand,
think, learn, and behave like humans.
Artificial intelligence is a science and technology based on disciplines such as Computer
Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI
is in the development of computer functions associated with human intelligence, such as
reasoning, learning, and problem solving.
Out of the following areas, one or multiple areas can contribute to build an intelligent system.
The scope of AI is disputed: as machines become increasingly capable, tasks considered
as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI
effect Capabilities generally classified as AI as of 2017 include successfully understanding
human speech, competing at the highest level in strategic game systems (such
as chess), autonomous cars, intelligent routing in content delivery network and military
simulations.
Artificial intelligence was founded as an academic discipline in 1956.For most of its history, AI
research has been divided into subfields that often fail to communicate with each other. These
sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or
"machine learning"),3]the use of particular tools ("logic" or "neural networks"), or deep
philosophical differences. Subfields have also been based on social factors (particular institutions
or the work of particular researchers).[
The traditional problems (or goals) of AI research include reasoning, knowledge
representation, planning, learning, natural language processing, perception and the ability to
move and manipulate objects. General intelligence is among the field's long-term
goals. Approaches include statistical methods, computational intelligence, and traditional
symbolic AI. Many tools are used in AI, including versions of search and mathematical
optimization, neural networks and methods based on statistics, probability and economics. The
AI field draws upon computer science, mathematics, psychology, linguistics, philosophy and
many others.
The field was founded on the claim that human intelligence "can be so precisely described that a
machine can be made to copy it". This raises philosophical arguments about the nature of
the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues
which have been explored by myth, fiction and philosophy since antiquity. Some people also
consider AI to be a danger to humanity if it progresses unabatedly. Others believe that AI, unlike
previous technological revolutions, will create a risk of mass unemployment.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent
advances in computer power, large amounts of data, and theoretical understanding; and AI
techniques have become an essential part of the technology industry, helping to solve many
challenging problems in computer science.

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***
Parsers-
Definition - What does Parser mean?
The term parsing comes from Latin word ‘pars’, meaning ‘part’ (of speech). A parser is a
compiler or interpreter component that breaks data into smaller elements for easy translation into
another language. Parsing means syntax analysis or syntactic analysis is the process of
analyzing a string of symbols, either in natural language, computer languages or data structures,
conforming (following) to the rules of a formal grammar.
A parser takes input in the form of a sequence of tokens or program instructions and usually
builds a data structure in the form of a parse tree or an abstract syntax tree. A parser is commonly
used as a component of an interpreter or a compiler. The overall process of parsing involves
three stages:
1. Lexical Analysis: A lexical analyzer is used to produce tokens from a stream of input
string characters, which are broken into small components to form meaningful
expressions.
2. Syntactic Analysis: Checks whether the generated tokens form a meaningful expression.
This makes use of a context-free grammar that defines algorithmic procedures for
components. These work to form an expression and define the particular order in which
tokens must be placed.
3. Semantic Parsing: The final parsing stage in which the meaning and implications of the
validated expression are determined and necessary actions are taken.
A parser's main purpose is to determine if input data may be derived from the start symbol
of the grammar. If yes, then in what ways can this input data be derived? This is achieved as
follows:
 Top-Down Parsing: Involves searching a parse tree to find the left most derivations of an
input stream by using a top-down expansion. Examples include LL parsers and recursive-
descent parsers.
 Bottom-Up Parsing: Involves rewriting the input back to the start symbol. This type of
parsing is also known as shift-reduce parsing. One example is a LR parser.
Parsers are widely used in the following technologies:
 Java and other programming languages
 HTML and XML
 Interactive data language and object definition language
 Database languages, such as SQL
 Modeling languages, such as virtual reality modeling language
 Scripting languages
 Protocols, such as HTTP and Internet remote function calls

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It is an analysis by a computer of a sentence or other string of words into its constituents. It
results further in a parse tree showing their syntactic relation to each other, which may
also contain semantic and other information.
Parser development software-
Some of the well known parser development tools are ANTLR, Bison, JavaCC, Lemon.
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Computer-assisted language learning-
Computer-Assisted Language Learning (CALL) is defined by Levy as "the search for and study
of applications of the computer in language teaching and learning." The main aim of CALL is to
find ways for using computers for the purpose of teaching and learning the language. More
specifically, CALL is the use of computer technologies that promote educational learning,
including word processing, presentation packages, guided drill and practice, tutor, simulation,
problem solving, games, multimedia CD-ROM, and internet applications such as e-mail, chat
and the World Wide Web (WWW) for language learning purposes.
Computer Assisted Language Learning (CALL) is often perceived, somewhat narrowly, as an
approach to language teaching and learning in which the computer is used as an aid to the
presentation, reinforcement and assessment of material to be learned, usually including a
substantial interactive element. Levy defines CALL more succinctly and more broadly as "the
search for and study of applications of the computer in language teaching and learning". Levy's
definition is in line with the view held by the majority of modern CALL practitioners.
The field of CALL involves the use of a computer in the language learning process. CALL
programs aim to teach aspects of the language learning process through the medium of the
computer. CALL programs can be (and have been) developed for the many parts of the language
learning process. Some of the factors that determine the characteristics of any CALL program
include: · the language taught, · the language of instruction, · the language writing system (both
roman and non-roman character based), · the level of the language to be taught (from absolute
beginners to advanced), · what is to be taught (grammar, informal conversation and
pronunciation) and · how it is to be taught. CALL straddles the fields of computing and language
learning. One of the criticisms that language teachers generally have about CALL programs is
that they are generally driven by the technology (or by those who have mastered the technology).
They argue that in the rush to use the latest “great feature”, pedagogical considerations are often
ignored. Just because a computer can endlessly drill a student about subjunctive verbs in Spanish
does not mean that it is the correct way to teach them. Even if a computer can have several
different flashing images on the screen at once to make a screen “more interesting”, it does not
mean that it enhances the learning process.
Computer Assisted Language Learning (CALL) grew out of the field of Computer Assisted
Instruction (CAI) and draws on other related fields such as Educational Psychology, Artificial
Intelligence (AI), computational linguistics, instructional design, Human Computer Interaction
(HCI) and SLA (Second Language Acquisition). More recently, it has been impacted by
developments in the field of WBI (Web Based Instruction). Indeed, there is a lot of crossover
between CALL programs and WELL (Web Enhanced Language Learning) programs.

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The current philosophy of CALL puts a strong emphasis on student-centred materials that allow
learners to work on their own. Such materials may be structured or unstructured, but they
normally embody two important features: interactive learning and individualised learning. CALL
is essentially a tool that helps teachers to facilitate the language learning process.
Why to use CALL?-
The reasons why ELT (English Language) teachers use CALL:
o Computers can do some of the work of the teacher and provide great assistance to the
learner even without the presence of the teacher.
o New technologies have seen computers become smaller, faster, and easier for the teacher
to use. At present, well-designed CALL software is readily available to the teacher.
o Technologies allow computers to do multimedia applications, incorporating video, sound,
and text, and this capacity allows the learner to interact with both the program and other
learners.
o The computer offers great flexibility for class scheduling and pacing of individual
learning, choosing activities and content to suit individual learning styles.
o The computer can provide a meaning-focused, communicative learning environment,
which serves the purposes of communicative language teaching.
History -
CALL dates back to the 1960s, when it was first introduced on university mainframe computers.
The PLATO project, initiated at the University of Illinois in 1960, is an important landmark in
the early development of CALL. The advent of the microcomputer in the late 1970s brought
computing within the range of a wider audience, resulting in a boom in the development of
CALL programs and a flurry of publications of books on CALL in the early 1980s. CALL's
origins can be traced back to the 1960s. Up until the late 1970s CALL projects were confined
mainly to universities, where computer programs were developed on large mainframe computers.
The PLATO project, initiated at the University of Illinois in 1960, is an important landmark in
the early development of CALL (Marty 1981). In the late 1970s, the arrival of the personal
computer (PC) brought computing within the range of a wider audience, resulting in a boom in
the development of CALL programs and a flurry of publications. Early CALL favoured an
approach that drew heavily on practices associated with programmed instruction. This was
reflected in the term Computer Assisted Language Instruction (CALI), which originated in the
USA and was in common use until the early 1980s, when CALL became the dominant term.
There was initially a lack of imagination and skill on the part of programmers, a situation that
was rectified to a considerable extent by the publication of an influential seminal work by
Higgins & Johns (1984), which contained numerous examples of alternative approaches to
CALL. Throughout the 1980s CALL widened its scope, embracing the communicative
approach and a range of new technologies. CALL has now established itself as an important
area of research in higher education:
Advantages of CALL -

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1. Interest and Motivation- It is often necessary, in a language learning classroom, to provide
repeated practice to meet important objectives. Because this can be boring, painful, and
frustrating, many students lose interest and motivation to learn foreign languages. CALL
programmes present the learner with a novelty. They teach the language in different and more
interesting, attractive ways and present language through games, animated graphics and
problem-solving techniques. As a result even tedious drills become more interesting. In fact,
CALL motivates the students to go beyond the point of initial mastery and practice activity until
they become automatic.
2.Individualisation- Many students need additional time and individualised practice to meet
learning objectives. The computer offers students self-instructional tasks that let them master
prerequisite skills and course objectives at a speed and level dictated by their own needs.
Besides, additional programmes can be made available for students who master objectives
quickly. These additional programmes can provide more intense study of the same objectives,
proceed to higher objectives, or integrate the objectives covered in the unit with other objectives.
In this manner, a computer gives individual attention to the learner and replies immediately to
questions or commands. It acts as a tutor and guides the learner towards the correct answer while
adapting the material to his performance.
3.A Compatible Learning Style- Students differ in their preferred styles of learning. Many
students seem to learn much more effectively when they are able to use a compatible learning
style than when they are forced to employ an incompatible one. Serious conflicts may arise when
a teacher employs a style that is incompatible with a student's. In this regard, the computer can
be used for adapting instruction to the unique styles of individual students. To cite an instance,
the computer can provide an exciting rapid-fire drill for one student and a calm, slow-paced
mode of presentation for another.
4.Optimal Use of Learning Time- By using the computer, students are often able to use their
Academic Learning Time (ALT) more fruitfully. Academic Learning Time (ALT) is the amount
of time a student spends attending to relevant academic tasks while performing those tasks with a
high rate of success. For example, not all the time officially scheduled for studying a foreign
language is likely to be allocated to it. If an hour is assigned to working on a topic, but the
teacher devotes five minutes at the beginning of the session to returning papers and five minutes
at the end to reading announcements, then only fifty minutes have been allocated to working on
the topic. Scheduled time merely sets an upper limit on allocated time. Likewise, allocated time
merely sets the upper limit to engaged time, which refers to the amount of time students actively
attend to the subject matter under consideration. Even though fifty minutes may be allocated to
studying a topic in French class, students may stare out the window or talk to their neighbours
instead of pursuing the assigned activity. Therefore, even when they are actively engaged in
studying the foreign language, students learn effectively only when they are performing at a high
rate of success. This smaller amount of time is the factor that is most strongly related to the
amount of learning that takes place. Computers enhance second/foreign language academic
learning time by permitting learners to acquire specific information and practice specific skills
and by helping students develop basic tools of learning which they can apply in a wide variety of
settings. This also subverts the relationship between time and traditional instruction. Traditional
instruction holds time constant and allows achievement to vary within a group. Computer-

14
assisted learning reverses this relationship by holding achievement constant and letting the time
students spend in pursuit of the objectives vary.
5.Immediate Feedback- Learners receive maximum benefit from feedback only when it is
supplied immediately. Their interest and receptivity declines when the information on their
performance is delayed. Yet, for various reasons, classroom feedback is often delayed and at
times denied. A deferment of positive feedback, though important to act as encouragement and
reinforcement, may not harm the progress of the learners. Nonetheless, any delay in offering
negative feedback, the knowledge that one is wrong, will become crucial. A blissfully ignorant
student may continue mispronouncing a word or applying a misconception before discovering
the nature of this error. In such case, the computer can give instantaneous feedback and help the
learner ward off his misconception at the initial stage itself. In addition to this, the computer can
look for certain types of errors and give specific feedback, such as, "It looks as if you forgot the
article." It is generally agreed that the provision of (almost) immediate feedback is beneficial for
the learner . Again, in the traditional classroom setting, it may not be possible to provide
immediate feedback to each individual learner. However, the computer can give feedback at the
touch of a button. Thus, learners can test their knowledge and learn from their mistakes. It is
important that errors are corrected before they are converted into part of the learner’s “language
knowledge”. CALL programs can not only correct errors but also reinforce the knowledge shown
in correct answers
6.Error Analysis - Computer database can be used by the instructor to classify and differentiate
the type of general errors as well as errors committed by learners on account of the influence of
the first language. And thus determine the most common errors cross-linguistically and more
specifically, the particular form of a particular error type within a particular language group. One
such study conducted reveals interesting findings, for example, that in subject-verb agreement
errors the base form of verb was over generalised incorrectly more often than the -s form by all
speakers. Also, Chinese writers typically omitted the articles a/an more often than the (Dalgish
1987:81-82). A computer can thus analyse the specific mistakes the student has made and can
react in a different way from the usual teacher--this leads the student not only to self-correction,
but also to understanding the principles behind the correct solution.
7.Guided and Free Writing - A word-processor in the computer can be very effective in
teaching guided/free writing activities. The ability to create and manipulate text easily is the
principle on which the word-processor programmes are founded. In this manner, the word-
processor encourages practice in guided or free writing activities together with a number of sub-
skills which comprise the writing process. Aspects of paragraphing, register, style, cohesion,
rhetorical structure, lexical choice and expression can all receive attention without requiring the
user to learn different programmes. The advantage is that the teacher can direct the student's
writing without exerting total and rigid control, allowing for freedom of expression within
certain bounds. Insights into grammar, vocabulary, punctuation, can also be developed.
8. Pre-determined to Process Syllabus- One major advantage in using a microprocessor is that
it can enhance the learning process from a predetermined syllabus to an emerging/process
syllabus. Even the ordinary 'fill-in-the-blanks' type of monotonous exercise on paper can be
made an exciting task on the screen in the self-access mode, where the students themselves

15
choose their own material. CALL thus facilitates the synthesis of the preplanned syllabus and
learner syllabuses "through a decision making process undertaken by teacher and learners
together" (Breen 1986:51).
9.Other Prospects- As students and teachers become more sophisticated in their use of such
CALL software, more complicated use of these packages become possible. For instance, the
ability of the computer to handle data, and allow the students to become computational linguists,
is very powerful (Hardistry 1988:42- 43). The experiential use of Wide Area Network (WAN)
and Local Area Network (LAN) can reveal unexplored teaching materials and untouched
learning methods. By effective use of linking computer with internet, authentic material can be
brought directly into the classroom. A reading text can be done using that day's news item or
weather forecast than using a news clipping of the previous year. The topicality of the issue can
generate lot of interest and create authenticity of purpose. Correspondingly, the facility of LAN
can be very useful for the practising of writing pithy telegraphic and telex messages. Of course,
the joy and the excitement involved in the online communication process, both local and
international, is an additional increment one gets from screen-based learning!
9.Learner Autonomy -Probably the most important benefit is that of Learner Autonomy.
Learner Autonomy has been discussed in section 2.5, p24. With a CALL program, learners can
work at their own pace. The learner can spend more time on those topics that are causing
difficulty. Information can be reviewed and tasks can be repeated until the learner is happy to
move on to a new topic. The learner feels in control, which usually enhances satisfaction levels
with the learning process. Successful language learners assume responsibility for their own
learning.
10. Privacy- Another benefit of CALL programs is the private environment it offers for self-
conscious language learners (Brett, 1996). Many learners are shy in a traditional classroom
setting, not participating as much as they would like, for fear of making mistakes and being the
object of ridicule. The computer offers a forum where learners can lose their self-consciousness.
The computer will not expose them when they make any mistakes (although the errors may be
stored for review). The learners can learn within the sheltered, protected confines of the CALL
program. Krashen (1985) notes that this may serve to lower affective filters.
11. Motivation- It is an important factor in language learning (Gardner, 1983; Scarcella and
Oxford, 1992; Okada et al., 1996). Motivation encourages greater learner effort and thus greater
language performance (Clément et al., 1977; Samimy and Tabuse, 1991). When looking at
motivation in the field of language learning, consideration is given to the difference between
foreign and second language learning (Au, 1988). Foreign language (FL) learning occurs when
the language being learnt is not used as the medium of communication (e.g. learning French in
Ireland). Second language (SL) learning occurs in an environment where the language being
learnt is that used in everyday communication (e.g. learning English in Ireland). In the FL
situation, the learner has to seek opportunities to engage in the target language.
12. Another benefit of CALL is the control over access to information. A CALL program has the
potential to provide more information to the learner (via links to electronic dictionaries, more
detailed screens and links to other sites) (Egbert and Hanson-Smith, 1999), while conversely,

16
learners can avoid information overload if they feel they are being overwhelmed. They can leave
a program to give themselves time to absorb the new knowledge. In a traditional classroom
setting, students cannot usually leave if they feel overloaded. They must wait until the class has
ended, possibly not paying attention to what the teacher is saying and missing out on the topic
being taught. With a CALL program, the user can leave when s/he wishes and come back to
where s/he left off and start again. Thus, users have more control over the cognitive load they
bear during a lesson.
Apprehensions of CALL –(Demerits/Disadvantages)
1.Man versus Machine- In spite of its glaring merits, the prospect of computer-assisted
language learning has troubled teachers more. Perhaps, the major cause of their worry might
have developed from the basic problem of accessibility. Often the computers have been kept in
Science or Maths department causing a real and psychological distance in the minds of the Arts
faculty. Nevertheless, many see computer as a threat not only in terms of its power to replace the
traditional skills, which the language teachers promote, but also its eventual replacement of the
teacher himself. Furthermore, shifting the control centre from the authoritarian teacher to the
need-based learner and accepting the humble role of a facilitator/moderator instead of being a
veritable dictator does not come easy for the traditionally clad chalk-talk teacher. In addition, the
computer-student interactive learning not only allows the possibility of role changes, but also the
potential for role-reversal, endangered by physical reversal by students. That is, the students
literally turn their back to the teachers, and silence is now on the part of the teacher until called
for assistance. Yet this role reversal can be exploited, since, it allows the classroom to become
far more "learning centred" (Hardistry 1988:39). This term rather than learner-centred, has been
used, to indicate that the central aim of the language lesson is to enable students to learn.
2.The Language Lab versus Computer- Another reason why teachers and sanctioning
authorities alike are uncertain about the use of computers in language learning is that computers
too, like language lab and other technological innovations, despite large investments, may
remain unused and stored in some dark and abandoned room. After all, language laboratories in
many countries fell into disuse, as they were too tied to one particular form of methodology,
which limited the awareness of the potential. One real danger is that the computer could be used,
like the language lab, as an instrument of Skinnerian behaviourism to facilitate the structuralist
approach with an emphasis on "correctness," negating its flexibility and potential as a teaching
aid to liberate the imaginations of the learners (Moore 1986:18-19). In this perspective, often
CALL courseware has been restricted to drill and practice, with the screen equivalent to the
textbook. Much software, like a textbook, is static both in presentation and in content. Another
major criticism of CALL software is the lock-step design of the lessons. This, in turn, means that
CALL software is missing a chance to exploit the computer's potential, with the result that
computer power is not released to the student adequately.
3.CALL versus TALL - Computer-Assisted Language Learning(CALL) contrasted with
Textbook-Assisted Language Learning (TALL), demands certain extra-skills such as
typography, graphic design, or paper making and the lack of which panics the teacher and the
taught alike. For instance, an inadvertent typographical error on the part of the student input may
be classified wrong although the grammar of the student's answer is correct. Further, in terms of

17
communication of ideas, a book is a means of communication between the author and the reader.
In the same way, the computer is a means of communication between the programmer and the
user. However, in this analogy, the author and the programmer do not mostly share similar
concerns. While the author is bound to be a subject expert, the programmer is mostly a
technician combined with the likely motives of a businessman. This gap between the author and
the programmer is responsible for inappropriate lesson content, poor documentation, errors in
format and content, improper feedback, etc. Likewise, in most software, there is little chance for
the teacher to add to or modify the existing programmes, even if he wishes too, since most of it is
locked to prevent pirating. And for the few of those who develop their own material, the time
spent on programming and typing in the lessons can be quite lengthy.
4.PROBLEMS OR CHALLENGES? - Yet, these apprehensions should be seen in the
backdrop of a developmental stage of computerisation of individuals and institutions and as a
temporary phenomenon. The next generation of teachers and learners will be part of a computer
generation. They will take for granted the skills demanded by computer technology and handle it
as coolly as switching on a taperecorder or watching a television. Similarly, the pupils will need
no readjustment of attitude when faced with a computer in a classroom and their familiarity and
frequent association with the machine would replace the sense of awe and alienation felt by older
people. Then planning pre-, actual and post-computer activities would be easily possible. The
teachers would ensure that they are the ones in control of educational software by becoming
involved in the development process and rejecting those programmes which do not serve their
needs. For that reason, the onus is on the present CALL-disposed teachers that in order to
convince the CALL-deposed teachers about the potentiality of CALL courseware, they must
prove that it is not only perfect in every way, but that it is far better than any other existing
teaching aid.
4. Limited Availability of Resources - CALL is an emerging discipline. Research points out
many of the current and potential benefits of CALL. However, in many learning institutions, the
availability of CALL resources is limited. Limited resources include time and money for
development of CALL materials (Levy, 1997), finance to purchase computers and lack of
teacher knowledge. Sometimes there is a mismatch between the CALL program and the users
and/or the setting. Often, the teacher has just one computer available and the teacher must try to
maximise the benefit of a CALL program for a group of students. User resources must also be
taken into consideration. Does the program assume access to speakers and a microphone? What
if the installation does not have access to the Internet? Obviously, if someone tries to use a
tandem learning program on a stand-alone PC with no connection to the Internet, it will either be
impossible or very difficult to fully use the features of the program. In this case, the program
should clarify user expectations. It should make clear to the user what resources it requires and
point out the limitations if these resources are not available. It should also try to provide alternate
ways of interacting with the user. For example, if a program allows the users to select an option
via a microphone, but one is not available, the user should be able to interact with the computer
via the mouse or keyboard.
5. Anti-Social Behaviour - CALL programs may promote anti-social behaviour (Pennington,
1996). Learners may get “wrapped-up” in the program and focus on learning the language in
isolation. Except in certain situations (learning a language for reading purposes only or for the

18
pure mental stimulation of doing so), the whole reason behind learning a language is to be able to
communicate with others. If someone learns a language for 43 the purposes of interacting with
another human in the same language and yet s/he only “speaks” to a computer, surely that is
missing the whole point of learning the language. Although the computer cannot force learners to
speak with other speakers of the language, it can suggest to learners that they practise with other
speakers at various points throughout the program.
6. Learning Content - Another possible problem with CALL programs is that sometimes
misleading, oversimplified explanations are provided. Not only will this waste the students’ time,
it will confuse them and will not meet their learning needs. Care must be taken to ensure that this
is avoided in the design process. One further issue to consider is correctness – it is important that
the linguistic elements of the language are reviewed with a native speaker to ensure correctness.
This may be more difficult in the CALL situation than when dealing with more traditional
learning media as the content provider may be more removed from the courseware production
process than may be the case in the traditional production process.
7. Ineffective Deployment- If there is a mismatch between the perceived and the actual setting
of a CALL program, its effectiveness may be limited. Is it for a single user or for group use?
Will the program be a tutor or a tool? Hubbard (1996) points out the importance of effective
deployment of CALL programs. While the design of a CALL program can try to encompass as
many different learning situations as possible, it will not be possible to cater for every situation.
Limitations in the deployment of CALL materials -There are still some drawbacks that
exist in terms of the deployment of CALL materials. These will have to be addressed and
include: · slow access, · server complications, · end-user configuration unknown, · potential need
for plug-ins, · technophobic students/teachers.
***
USES OF CALL IN ENGLISH LANGUAGE TEACHING
1. Computer as drill and practice-
In this use of CALL, computers are viewed as a tool for saving time with the immediate
feedback. The learning principles behind Drill and Practice is the Behaviorism Learning Theory
and the Audiolingual approach language to teaching. The main aim of Drill and Practice is to
review the content / background knowledge, and to assist the learners to master separate
language skills (such as reading, listening, etc.)
Drill and practice consists of three steps: Providing stimulus; Receiving active response from the
learner; and Giving immediate feedback.
There are several types of drill and practice activities (exercises) such as Paired Associate
(Matching); Sentence Completion; Multiple Choice; Part Identification; True-False; and Short-
Answer questions.

19
Well-designed Drill and Practice programs can record the learner’s progress and scores and the
time a student spends on each exercise. Some programs add timing features to help the learner to
control their speed while practicing. Drill and practice CALL programs in the early years
focused on practicing language skills and components separately (such as vocabulary, grammar
(such as irregular verbs, past tense, articles), reading, and translation. A lot of drill and practice
exercises were produced by classroom teachers. There are several limitations of Drill and
Practice exercises such as the lack of interaction and content materials which are not authentic,
meaningful, and contextualized (Felix, 1998). As a result, the receptive language drill and
practice programs of the 1960s –1970s did not produce enough authentic communication for the
learners.
Another type of Drill and Practice is so called "contextualized activities" such as gap filling,
reconstructing texts, etc. Examples of these programs are those developed in early 1980s such
as Cloze exercises, Text reconstruction, and Eclipse (by Higgins), etc. A key authoring program
used to generate text reconstruction is Storyboard, written by John Higgins (Levy, 1997).
2.Computer as tutor-
The role of the computer as tutor is to present to the learners the content of the lesson as text
graphics, video, animation, or slides, including learning activities, drills and practice. The
computer serves as a means for delivering instructional materials.
The program consists of the following stages: Introduction stage (stating aims, background
knowledge), Presentation of the content, exercises and/or testing; and Giving the feedback.
Computer used for simulation / problem solving
Simulations and problem solving is used to foster analysis, critical thinking, discussion and
writing activities. The computer is not used much for tutorial purposes. The program is designed
to create language interaction through problematic situations, conditions or problems challenging
for the learner to solve. Many simulation programs are problem solving games, which are
entertaining and educational ("edutainment")
3.Computer as game-
The main principle behind computer gaming is that "Learning is Fun." The main aim is to create
a pleasurable learning environment , and to motivate the language learner. However, good
educational games should have clear educational objectives.
CALL games and simulation games are similar in that both are designed to motivate students to
learn through entertainment. However, they are different in certain ways. Simulation games
always use simulations (real life situations) in the presentation of a game, while CALL games
focus on providing fun, but challenging environment to the learner. Though CALL games have
clear learning objectives, they are different from Tutorials and Drill and Practice. The main

20
function of CALL games is not so much to present the language content as tutorials do but to
provide entertainment to the learner.
4.Word Processors-
The most common tool used by teachers and learners in CALL is probably word processors.
Word Processors are tools for creating documents for making handouts, sheets, desktop
publishing, letters, and flyers for language teaching and learning. There is a variety of word
processors available, ranging from high quality programs such as Microsoft
Word <http://www.microsoft.com .
5.Spelling Checkers-
Spelling checkers are tools for ELT teachers and learners for conducting spelling check. Most
high quality word processing programs such as Microsoft Word, Word Perfect have built in
spelling checkers. However, there are separate spelling checking programs available such
as Spell it Deluxe (1997).
6.Grammar Checkers-
ELT teachers can use grammar checker programs to check and point out grammatical problems
in writing. Like spelling checkers, grammar checkers can be a separate program such
as Grammatik or built-in programs such as the Grammar Check in Microsoft Word. However,
these grammar checkers still have limited abilities and are intended for native speakers. So they
are not recommended for ESL/EFL learners since they may be confusing.
7.Electronic mail (E-mail)- Computer-mediated communication makes it easy for ELT
learners to have direct authentic communication with the teacher, other learners or interested
people around the world by using e-mail. E-mail is an excellent method for teaching interactive
writing. One of its advantages is that it provides interaction with native speakers through pen-pal
correspondence. E-mail writing is considered to be more personal and meaningful than
classroom writing activities.
ADVANTAGES AND LIMITATIONS OF CALL-
Advantages of CALL
1) Learner’s Factors-
o CALL can adapt to the learners' abilities and preferences.
o CALL can adapt to the learners’ cognitive and learning styles.
o CALL can adapt to the learner’s self-paced learning. CALL can be used for remedial work
for slow learners and to accelerate learning for fast learners.
o CALL offers individualized and private learning.

21
o CALL, with branching capability, provides choices and paths for learning, allowing learners
to work independently.
o CALL allows learners to control their own learning process and progress.
2. Motivation and Attitudes-
o CALL provides strong motivation for learning. Students will often do on a computer what
they are reluctant to do in a textbook or paper-pencil.
o Some CALL features such as graphics, sounds, animation, video, audio are interesting and
motivating for many learners.
o CALL can improve learners’ attitudes towards learning English.
o CALL (internet) provides authentic communication that motivates students to use language
outside language classroom.
3.Feedback and Progress Record-
o CALL can provide immediate responsiveness and feedback.
o CALL provides accurate records of the learner’s performance and progress.
Teacher’s Roles and the Relationship with the Learner
o CALL can change the relationship between teacher and student.
o The teacher becomes a facilitator rather than a person who controls the learning environment.
o CALL is predictable and non-judgemental.
4.Mastery Learning-
o CALL provides opportunities for mastery-learning language skills.
o CALL can lower the amount of time required to master some materials.
5.Co-operative Learning-
o CALL (e.g.simulation games) encourages learners to work cooperatively in problem solving.
o CALL allows learners to learn cooperatively as a result of working together (such as group
works, and discussion.)
6.Communication-
o CALL (e.g. games and puzzles) create information gaps which provide learners a need to
communicate or interact with each other or with the program.
o CALL (e.g. e-mail, chat, moos) promote direct communicative skills for the learners.

22
o CALL (e.g. e-mail, chat, moos) provides authentic, real communication with native speakers
of English outside the classroom.
7.Access to Information and Cultures-
o CALL (e.g. CD-ROM and the internet) can increase access to information to the learners.
o CALL (CD-ROM and the internet) allow learners to acess to cultures around the world.
8.Learning Environment-
o CALL is a neutral medium. Compared to teachers, computers do not lose patience, get angry,
or play favourites as some teachers do. This creates a safe learning environment.
o CALL can provide an active and positive learning environment.
o Integration of a variety of multimedia such as texts, graphics, sound, animation, and video,
allowing for creating authentic meaningful language learning environments.
o CALL (the internet) has no limitations regarding different time zones and places.
Limitations of CALL-
1.Cost-
o Schools may lack funds for CALL implementations. Some CALL hardware and software are
very expensive. It is problematic in schools that have limited funding.
o The design of good CALL software needs expensive equipment and cooperative team work.
o Not all students can access CALL (e.g. the internet). In many developing countries, there is a
problem of "have" and "have not" internet between the rich and the poor.
2.Teacher's Attitudes and Anxiety-
o ELT teachers may have negative attitudes towards CALL.
o There is fear that CALL might replace teachers.
o Many ELT teachers are anxious about CALL because they have limited skills and experience
in CALL theory and delivery.
o There is fear that the computer might isolate students from social activities.
3.Training-
o A lot of ELT teachers still lack training and skills in using the CALL, and training costs are
high.
o Training learners to use computers takes students’ time away from other educational
activities.

23
o ELT teachers may lack the necessary computer-related skills.
4.Hardware, Compatability, and Technical Support-
o Computer hardware is difficult to install and maintain for classroom teachers.
o Spontaneous language production (e.g. speaking) is still limited by the hardware capabilities
such as voice-recognition and voice recording.
o Graphics and sounds provided on the computer are sometimes unrealistic and
incomprehensible.
o CALL presentation is sometimes restricted by the capabilities of the hardware (e.g. not
enough RAM to run big CD-ROM programs).
o Disk space is still problematic for storing large multimedia files.
o CALL (e.g. CD-ROMs) are sometimes not suitable for all computers, platforms and
hardware.
o Web pages appear differently on different computer platforms (e.g. Windows, Mac). It
sometimes makes students confused.
5.Software-
o There are many poor CALL software programs due to the lack of programmers with
linguistic knowledge, language teaching approaches, and experiences.
o A lot of CALL software (e.g. Drill and Practice type) focus on teaching separate, discrete
language skills and component, ignoring discourse, contexts, and cultures.
o Some CALL (e.g. the internet) does not support face to face communication (e.g. E-mail,
chat) well, though some present technologies can provide sounds and pictures during
communication there are some limitations with speed, sound and picture quality.
o A lot of CALL activities (e.g. Behavioristic CALL) are limited to certain types of exercises
such as multiple choices, true false, matching, ignoring question-answer interactions.
o There are a lot of web pages of poor quality. There is a lot of junk on the internet. Teachers
need to evaluate internet web pages with great care before downloading or assigning the
students to access them.
o At present CALL software still lacks ability of abstract reasoning and problem-solving
processes.

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