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Chapter 1Intelligent CALL:

Using Pattern Matching to Learn English
John Blake
https://orcid.org/0000-0002-3150-4995
University of Aizu, Japan

ABSTRACTThis chapter shows readers the importance and application of pattern matching in learning languages;
specifically, the application of natural language processing to address specific problems of Japaneselearners of English at a public university. The chapter introduces the concepts of patterns, detection, anddetection methods. The author turns to the pedagogic application of pattern matching, first discussing
the relevant theory, then describing hacks developed by language teachers and learners. The final section describes and evaluates iCALL tools developed at the University of Aizu, including a mobile app
and the Pronunciation Scaffolder, a real-time presentation script annotator.

INTRODUCTION
Patterns permeate every aspect of life (Carbone, Gronov, & Prusinkiewicz, 2004) from predicting weather
based on cloud formations to distinguishing friends from foe based on behaviour patterns. Some of thesepatterns are learnt simply through exposure, but others may involve various combinations of intuitionand experience. Pattern recognition is also a key tenet in language learning and has been the focus ofscholarly articles for many years. In fact, almost a century ago, Sapir (1925) published an article focusing on sound patterns. Actively assisting language learners to notice and use patterns is thought tohelp learners master a new language more effectively and more efficiently (Ellis, 1994). More recently,
Hunston and Francis (2000) coined the term pattern grammar to describe the contextual usage patterns
associated with particular word senses.
Most children begin learning their first language through extensive listening (Asher, 1972). During
the receptive phase when children listen but are unable to speak, language patterns in the various interrelated language systems (e.g., phonemes, morphemes, lexis and grammar) are thought to be acquired.
Second language learning may differ from first language learning depending on various factors, such
DOI: 10.4018/978-1-7998-2591-3.ch001

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﻿Intelligent CALL

as the type of tuition, purpose of learning, relationship between mother tongue and second language,
and the age of the learner (Cook, 2016; Johnson, 2017). For example, adult learners already literate intheir mother tongue may first focus on reading a second language and start identifying visual language
patterns before audio language patterns. Learners of a second language with the same script as their first
language are more able to draw on their existing knowledge of orthographic patterns. Lexis in languages
that are linguistically close share more in common than lexis in language that are linguistically distant.
The greater the linguistic distance, the greater the difficulty to learn the second or additional language(Piske, MacKay, & Flege, 2001).
This chapter aims to show readers the importance and application of pattern recognition in learning
languages; specifically, it focuses on the application of natural language processing to assist Japaneselearners of English at a public university. These learners studied English for at least six years before
entering university and study for four more years during their university program. English language
learning was a compulsory school subject from the first year of junior high school when students areapproximately thirteen years old. The vast majority have extensive passive vocabularies, general knowledge of many grammatical forms, and detailed knowledge of some obscure grammatical oddities thatare often tested on university entrance exams. Despite their extensive knowledge of rules and words,
few Japanese university students can converse freely in English. King (2013), having observed thirty
English language classes in multiple Japanese universities concluded that there was “a robust trend, withminimal variation, toward silence” (p. 337).
This chapter begins by reviewing the origins of computer assisted language learning (CALL) andthe transition to intelligent CALL. This is followed by a discussion of patterns in language and patterndetection. The chapter then introduces the concept of discovery learning and describes the pedagogic
application of pattern matching. Pattern matching and detection tools that were developed specifically forlearning English are then introduced. Specifically, two iCALL tools, both of which were developed at the
University of Aizu are discussed in depth. The first iCALL tool is a mobile app called WordBricks andthe second, the Pronunciation Scaffolder, a real-time presentation script annotator. WordBricks (Purgina,
Mozgovoy, & Blake, 2019) was designed to help language learners acquire knowledge of syntactical
patterns through a gamified environment in which users attempt to piece together jigsaw-like texts. The
second tool is the Pronunciation Scaffolder (Blake, 2019) that targets learners of English who can readand understand texts but have difficulty in reading aloud. It was designed to help Japanese learners moreeasily read prepared presentation scripts using colour, size and symbols to visualize various pronunciation features. The chapter concludes by discussing future research directions and noting the increasing
importance of deep learning in iCALL.
The main objectives of this chapter are to:
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introduce the general field of computer-assisted language learning (CALL),situate intelligent computer-assisted language learning (iCALL) within the CALL paradigm,
define patterns by distinguishing between randomness, repetition and patterns,
describe, explain and exemplify patterns in language,
show how iCALL tools enhance discovery learning through pedagogic pattern matching,describe and evaluate two iCALL tools.

﻿Intelligent CALLBACKGROUND OF CALL
CALL originated in the 1960s and 1970s in the days of microcomputers. Early innovators had to overcome
significant resistance from teachers at the chalkface. The resistance was based on various misconceptions
and fears (Roberts, 1993), including the fear of being replaced by a computer. This fear of technology
threating jobs is not new. English workers in the textile industry in the early 19th century known as the
Luddites vehemently opposed the introduction of machines and fell victim to technological unemployment.
In the pre-CALL days, the main technology used in language learning was the audio cassette. Wellresourced schools often had a dedicated language lab where students could access audio materials. The
popularity of the audio-lingual method in the 1960s and 1970s led to a rapid increase in the number oflanguage labs. Language labs nowadays tend to be multimedia labs in which students can access audiovisual media locally and via the internet. In the 80s and 1990s, audio and video cassette players were
the technology that most classroom teachers of English as a foreign language (EFL) tended to use on adaily basis. In one of the early CALL books, (Kenning & Kenning, 1983) described how to write simple
programs in BASIC. The perceived necessity to learn a programming language was a barrier than many
language teachers did not attempt to overcome.
In a practical resource book for EFL teachers, Dudeney (2000) describes how to use the internet as alanguage learning resource. With ubiquitous access to the internet in many developed countries, there isa rapid trend to using online resources. Nowadays, language learners in Japanese universities often consult dictionaries and other language resources on their own web-enabled devices instead of asking theirteachers. Teachers who used to display materials to classes by writing on whiteboards may now present
materials using slideshow software, such as PowerPoint. This move helped classroom teachers realize
how technology could increase the efficiency of classroom teaching. As laptop computers became more
affordable and language learning websites became more user-friendly, CALL moved into the mainstream.
The prevalence of smartphones among learners has caused a shift away from accessing the internet
via a computer. Almost all university students in Japan possess a smartphone (Tateno et al., 2019). Thistransition to online learning using handheld devices enables learners to access the same kinds of functionalities and opportunities previously only available to computer users. CALL is, therefore, no longer
technically accurate. A new term mobile assisted language learning (MALL) appeared to catch this newtechnological and pedagogic development (for more details on CALL and MALL see Figueroa, 2020).
Yet, as Microsoft and others began to develop platforms that work on different platforms, the boundary
between mobile and computer interfaces is less distinct. Windows 10, for example, is designed to workwith touchscreen devices in any orientation (portrait or landscape) and on any viewport size (e.g., mobile,tablet, laptop or monitor). Technology-enhanced language learning is perhaps a suitable catch-all termthat does not limit its usage to one type of device.
The affordability and ubiquitous nature of smartphones in many countries has resulted in schools
adopting bring-your-own-device (BYOD) classrooms, in which students learn on their preferred webenabled device. This reduces the reliance on institutions to provide devices, but raises the issue of ensuring compatibility across the various platforms. Song and Kong (2017) provide a succinct description of
the affordances and constraints of BYOD on pedagogic practices. Technical troubleshooting tends to beleft to students themselves who solve connection, access and software problems on their own devices.

3﻿Intelligent CALLIntelligent CALL
The harnessing of natural language processing (NLP) to help language learners has taken CALL to a
new level, in which systems appear to make intelligent decisions. This use of NLP in CALL is termed
intelligent CALL (iCALL) (Finkbeiner & Knierim, 2008). Arguments in support of the use of artificial
intelligence in CALL have been made since the 1980s. Bailin first published on using artificial intelligence
in CALL in 1988, and later created a bibliography of iCALL (Bailin et al., 1989). To help create a sharedunderstanding of iCALL, Oxford (1993, as cited in Levy, 1997, p. 220) enumerated nine principles foran Intelligent CALL (iCALL) framework to ensure that the focus of developers and practitioners was
firmly on language, learners and learning rather than the technology.
In recent years, natural language processing with the help of artificial intelligence has made considerable
progress in being able to “understand” language. Axiomatically, computers do not actually understandlanguage, but follow algorithms to perform tasks, such as extracting information from “unstructured” rawlanguage and placing it into “structured” language. “Unstructured” language refers to natural languagethat is not compartmentalized into tables that could be housed in a spreadsheet or database. “Structured”
language refers to language that is compartmentalized. Each of the compartments can be referred tousing coordinates. This structured language (i.e., data in tables) can be easily accessed by computer
software while natural language is less easily accessed. The late Adam Kilgarriff an influential corpus
and computational linguist and co-founder of Sketch Engine (Kilgarriff et al., 2014), often argued that all
language is inherently structured by language systems, such as morphology and syntax (Kilgarriff, 2005).
Intelligent CALL can use natural language processing to search raw “unstructured” language (e.g.,
articles, books, essays) and categorize items. This categorization may help learners learn a language.
Computational linguists, generally speaking, are focused on publishing cutting-edge research, whichtends to mean creating a system that is more accurate, more powerful or faster than existing systems.
There are, however, some researchers with a strong interest in language acquisition who are working iniCALL. Elena Volodina and her colleagues in Scandinavia have also been working extensively in theiCALL field on Scandinavian languages rather than English. Volodina, Borin, Lofsson, Arnbjörnsdóttir
and Leifsson (2012) describe a system architecture that they developed to house iCALL resources andapplications designed for any Scandinavian language. Natalia Bogach and her team at Peter the Great St.
Petersburg Polytechnic University has been working on a computer-assisted prosody teaching tool thatuses pitch visualization technology in Android mobile application (Boitsova et al., 2018). In the UnitedStates, Evgeny Chukharev-Hudilainen harnesses natural language processing to design iCALL tools thatprovide feedback on writing (Chukharev-Hudilainen, 2019; Chukharev-Hudilainen & Saricaoglu, 2016)
and pronunciation (Qian, Chukharev-Hudilainen, & Levis, 2018). These tools are adaptive, scalable andeffective, but what is particularly notable about the tools is their basis on empirically-proven pedagogic
principles. Headway is also being made in Japan. The theme of the 2019 Conference of Japanese Association of Language Teachers Computer Assisted Language Learning Special Interest Group was
artificial intelligence and machine learning in language education. This no doubt will act as a catalyst to
researchers with proficiency in linguistics, education and computer science. Some language researchersin Japan have already developed tools that can be classed as iCALL. Gary Ross and his team at Kanazawa
University are working on web speech technology and are using AI to analyze data on spoken patterns
collected from an innovative online conversation practice platform in which users act out one-side of a
dialogue (Ross, 2018).

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Patterns
An individual instance cannot be considered a pattern. When instances are repeated, the repetitions areeither random or patterned. The concept of randomness simply means that no pattern is discernable.
Imagine that each word in a dictionary is written on separate slips of paper and placed in an opaque box.
The box is shaken, and then ten slips of paper are picked up one by one. The words are placed in orderof selection face up on a desk to create a sentence. There is a slim chance that a grammatically correctsentence is formed, but there is a substantially higher probability that the sentence is non-sensical andungrammatical. In this case the words were selected randomly with no bias. However, what if the paper
sizes differed with long words written on longer slips and short words on shorter slips? There was nobias in frequency, but this bias in surface area increases the odds that the selector can pick up large orsmall slips at will. Assuming a hand is used, the hand size may dictate which size pieces of paper are
easier to be picked up. Various factors can creep in and bias randomness. In this example, physicalfactors, such as thickness and curvature of the paper, can alter the outcome. Humans are adept at seeing patterns, but conversely are poor at creating randomness. When asked to choose random numbers,
random animals or create multiple choice quizzes in which the answers are randomized, we perform
less well than automated systems.
Repetition is a necessary precursor to a pattern, but repetition does not necessarily result in a pattern. The number of instances necessary to create a pattern that can be perceived varies depending on
its mode, medium and complexity. For example, when learning a language, some words occur more
frequently and some words occur less frequently. In English, the pattern of the most common word, thedefinite article the, is rather complex but despite the multiple reasons that can explain the usage of thedefinite article, the most noticeable pattern is that it is almost always followed by a noun, which may or
may not be the subsequent word.

Patterns in language
Being able to recognize which words are most frequently used can help learners prioritize which words
to learn first. For example, should a learner attempt to learn a language from reading a seminal book,such as learning Arabic from the Qur’an, Latin from Odes by Horace or Greek from the Odyssey byHomer. It would make little sense to focus on hapax legomena, that is words that only occur once in a
text. However, in authorship analysis hapax legomena help to provide a fingerprint for the lexical repertoire of a writer (Savoy, 2012). Language courses and coursebooks are frequently designed based on thefrequency with high frequency words and grammatical constructions being prioritised. This principle isfound in both books aimed at children learning their mother tongues and learners of second or additional
languages (Thornbury, 2002).
In an interesting twist, although a single instance of a particular word cannot be considered a pattern,
there is actually a pattern formed by all the single instances of words. This pattern is so reliable that, todate, each time this pattern has been observed, the predicted result was correct. The pattern is one of the
few scientific laws that relate to language, and is described by Zipf’s law (Altmann & Gerlach, 2014),
which is illustrated in Figure 1.

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Figure 1. Plot of the frequency of words in frequency order for the example sentence

Patterns permeate language (Kilgarriff, 2005). Arguably, language is patterns. Patterns can be foundat every level in the language system from phonemes and graphemes to morphemes to lexis to phases
to clauses to sentences to paragraphs to texts. Table 1 provides example items and patterns from threedifferent language systems.
Table 1. Examples of a few language systems
Language system

Example item

Example pattern• Phonemic• /k/• Realized by letters c, ck, k, ch• Morphemic• -ment• Verb to noun transitions (e.g., advertise, advertisement)• Lexical• Collocation• Black and….. [white, blue]

Words that frequently co-occur are said to collocate. The association strength of collocations canbe measured statistically and from this the probability of one word following another can be predicted(Evert, 2009). Collocation is the basis of predictive text that is a feature of not only short messaging
services (e.g., texting) but is now rolled out as a feature in Google’s email service, Gmail. Patterns
manifest themselves in many forms. Patterns related to word length and word frequency in English aredescribed to show how noticing patterns can help learners of English.

Word Length
Language learners pick up on prominent patterns. Learners differ in their abilities to perceive patterns
with some language learners are more adept at aural, visual or spatial patterns. More visually inclined6﻿Intelligent CALL

learners may notice that there is a pattern in English (and many other languages) that relates to word
length. There is a length pattern in many languages with comparatively short words occurring between
longer words. In English the shortest words consist of one or two letters. The grammatical classes forthe majority of these words are personal pronouns (e.g., I, he, it), indefinite articles (i.e., a/an) andprepositions (e.g., in, at, on). Personal pronouns are frequently used as the first word in simple sentences, especially sentences that occur in coursebooks aimed at beginners. Indefinite articles follow astrict pattern, occurring before singular nouns. Prepositions also occur before nouns, but their syntax is
not as strict as indefinite articles, and they may occur in other positions, such as when the preposition
collocates with a verb.
The length pattern is based on the part of speech (POS) of words. This pattern is easy to notice andvisualize. A simple script can be used that automatically annotates each word with its POS tag. Anotherscript can be executed that colorizes the words associated with each POS tag. The result would be thatwords in the same POS class will appear the same colour. This can help reveal patterns that learners
may not have discovered otherwise. One pattern that commonly occurs in scientific texts is the stackingof prepositional phrases (Benelhadj, 2019). Colorizing prepositions and their associated noun phrases
may help show language learners how details are packed into scientific texts using multiple prepositional
phrases.
The first sentence in the abstract of this chapter is reproduced below. Prepositions are shown in bold.
Noun phrases following prepositions are underlined.
This chapter aims to show readers the importance and application of pattern matching in learning languages; specifically, it will focus on the application of natural language processing to address specificproblems of Japanese learners of English at a public university.
The sentence with all prepositional phrases removed now reads:
This chapter aims to show readers the importance and application …; specifically, it will focus … toaddress specific problems ….
The general meaning of the sentence can still be inferred from the grammatical subjects, verbs andobjects, but the specific details are lacking. The prepositional phrases extracted from this sentence areplaced in alphabetical order here:
at a public university
in learning languages
of English
of Japanese learnersof natural language processingof pattern matchingon the application
The pattern of a short word, prepositions in these cases, preceding noun phrases can be seen. The
high frequency of prepositional phrases is typical of academic and scientific writing, and so the patternof stacking prepositional phrases should be noticed by those reading and writing such texts.
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Word Frequency
Word length is closely related to grammatical categories. Word frequency, however, trumps length interms of importance. Most language coursebooks present vocabulary in frequency order introducing
words that are more commonly used before those less commonly used words. Word frequency is a key
indicator that can help learners assess whether candidate words are worth the effort of learning. Learners of English soon realize that function words are used far more frequently that content words, and sothese grammatical words deserve more attention. Function words are the grammatical “glue” that holda sentence together. These words are often guessable. The same sentence is reproduced below without
the function words.
___chapter aims __ show readers ___ importance __ application __ pattern matching __ learning languages; specifically, it ___ focus __ __ application __ natural language processing __ address specificproblems __ Japanese learners __ English __ __ public university.
This time, the same sentence is reproduced below without the content words. These content words
are unlikely to be guessed. This is because function words belong to a closed set of words while content
words do not.
This ___ ____ to ___ ____ the ___ and ___ of ___ ____ in ___ ____; ___, ___ will ___ on the ___ of___ ____ ___ to ___ ____ ___ of ___ ____ of ___ at a ___ ____.
Table 2. Table of function and content words
Function words

Content words

•a

• at

• address• aims• application• chapter

• and

• in

• English• focus• learners• importance

• of

• on

• it

• Japanese• language• languages• the• this• learners• learning• matching• natural• to• will• pattern• problems• processing• public• readers• show• specific• specifically• university

Each word in the sentence is classified as a function or content word in Table 2.
It can be seen that the number of content words far outstrips the number of function words. In fact,
three of the function words are repeated, of (4), the (2) and to (2), while none of the content words arerepeated. Some content words are related semantically, but take different grammatical forms. Examplesof semantically-related words are given here:
specific (adjective) cf. specifically (adverb)
language (singular noun) cf. languages (plural noun)

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learning (-ing form, uncountable noun) cf. learners (plural noun)
When the words are listed in frequency order and plotted against frequency, the resultant graph is
non-linear and shows an inverse relationship between frequency order and frequency (i.e., the higher
the frequency, the lower the order). The graph shown in Figure 1 illustrates the non-linear pattern that is
described by Zipf’s law, which states that the frequency of a word is inversely proportional to its rank infrequency. This enables language users to draw upon a “miniature lexicon for efficient communication”(Kanwal, Smith, Culbertson, & Kirby, 2017). As predicted by Zipf’s law, the tail of the graph contains
a large proportion of hapax legomena, words occurring once, which are usually expected to comprise
between 40 to 60% of any corpus.
Word frequency lists can be created by using a script to count the instances of each word in a text.
There are a number of online text profilers providing easy-to-use interfaces that can be used to identify
not only a frequency list, but a breakdown of the words in a text according to published frequency lists,
including academic word lists. Relative word frequency can be visualized using word clouds. Word
clouds show the proportional frequency of content words in any text by using larger fonts for more frequent words and smaller fonts for less frequent words. To avoid function words dominating the cloud,stop lists of function words are usually utilized. Figure 2 shows a word cloud of a draft of this chapter.
Word clouds can be used to provide a lexical summary of a text or corpus, priming readers with not only
the specific words that appear in a text, but engaging readers to work out what the text will be about(by spotting a pattern in the words). The word cloud in Figure 2 is of a famous speech by Sir Winston
Churchill, a British politician. Those with a knowledge of British history will be able to immediately
recognize the speech from the word cloud.

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Figure 2. Word cloud of speech by Winston Churchill (1940)

Word frequency can also be used to work out which words tend to co-occur. Sketch engine, a tool
designed by Kilgarriff et al. (2014) provides word sketches using measures of association. Figure 3 shows
an extract of a word sketch for the word paper in a tailormade corpus of scientific research abstracts inthe field of information theory.

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Figure 3. Word sketch for the word paper in information theory abstracts

Pattern Detection
The innate ability to discover patterns helps humans learn how languages are “encoded, processed andmodified” for communicative purposes (Mattson, 2014, p.7). Good language learners are thought to begood pattern matchers, who can notice systematic variations in language features, such as syntax, verb
inflections and collocations (Sykes, 2015). It is only possible to learn from a pattern if the pattern is
noticed. The noticing hypothesis (Schmidt, 2010) is not a new concept as it has been discussed for overthirty years. Teachers of English as an additional language tend to foreground patterns making it easierfor language learners to focus on the target language. Substitution drills can be used for controlled practice to stimulate learners to recall the target pattern. Teachers may use generative situation to maximize
learners’ opportunity to practice particular structures.
For example, a teacher of intermediate English learners may get students to work in pairs and underline every instance of passive voice in a newspaper article. On completion, student groups could discuss
the reason for the use of passive voice for each instance. The teacher can then explain that passive voicetends to be used to focus on the action or process rather than the actor or the doer. This can be done
to hide the doer because inter alia they are unimportant or obvious. Following this focus on receptive
skills, the class could then move onto productive skills, possibly rewriting a narrative that is written in
active voice. This type of learning closely matches Nation’s four stands model (Nation, 2001; Nation

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& Newton, 2009) in which classroom activities are divided into meaningful input, meaningful output,
language-focused instruction and fluency practice.
Various simple patterns can be discovered using a corpus and a concordancer using the key word incontext (KWIC) function, which aligns instances of the same word to the center of the viewport (Scott& Tribble, 2006). The KWIC function is a simple feature commonly built into corpus software and helps
users identify collocations and colligations, that is the grammatical patterns associated with a lexical item.
The processing power of computers is so vast that they can discern patterns that humans are unable todetect. Subtle patterns, however, may necessitate the use of more sophisticated statistical models, suchas principal component analysis and factor analysis.

DISCOVERY LEARNING
Discovery learning is premised on the noticing hypothesis. Many language learners have experience ofnoticing a language feature and from that moment have a sense that they learnt it. This leads many toaccept the hypothesis despite the lack of experimental and empirical evidence. The hypothesis makes
sense and continues to underpin many language teaching approaches and methods. Skehan (2016) notesthat noticing needs to be followed by pattern detection to enable learners to generalize from the language noticed. Noticing involves the learner searching for particular language features. On discovering
the language features, learners have the opportunity to learn. Discovery learning is a type of inductive
learning, in which learners generate their own rules to explain discovered phenomena. Bruner (1961)
advocated inquiry-based instruction for language learning. Such learning is said to be engaging, motivating and promotes autonomy. Mayer (2004) later argues convincingly that a guided learning approach
is more effective than the pure discovery learning approach. DDL tends to make use of collections oftexts or corpora.
Data driven learning (Johns, 2002) puts students at the centre of the learning process. Feedback fromstudents using data driven learning approaches is not always positive. Common complaints are the difficulty of trying to understand ungraded language. As the proportion of unknown words increases inthe text or corpus that they are examining, the difficulty of the task increases. Cotos, Link and Huffman(2017) found that graduate students who undertook guided DDL using a specialist corpus were able toimprove the “rhetorical, formal, and procedural aspects of genre knowledge” (p. 104). Mizumoto andChujo (2015) provide a meta-analysis of DDL approaches in EFL in Japan, and showed that in primaryeducation focusing on vocabulary acquisition DDL was effective.
The use of corpora in lessons places the focus on authentic language and tends to adopt a frequentist
perspective (Boulton, 2017; Boulton & Cobb, 2017). Words can be ranked according to raw frequency,
disproportionate frequency as measured by keywords and simple KWIC concordance line results can beused. Students shift through language data for a particular purpose. Lee and Swales (2006) describe an
approach that raises rhetorical consciousness through technology-enhanced interactions using specialist and self-compiled corpora. Purposes vary, but searching for particular patterns of language, such as
collocation and colligation. Baker (2006) claimed that students were able to make generalizations about
linguistic patterns using authentic corpus examples. To discover collocations, students can load a specialist corpus into a concordancer, such as AntConc (Anthony, 2019). Colligation tasks include searching for the typical tense or voice that as associated with particular verbs. The verb bear, for example,
tends to be used in past simple tense and passive voice, as in “He was born yesterday.” To reduce the12﻿Intelligent CALL

cognitive overload, a number of researchers have tried to filter corpus results to manageable proportions(Frankenberg-Garcia, 2014; Huang, L.-S, 2008; Mizumoto & Chujo, 2015; Smart, 2014).

Pedagogic Pattern Matching
Another discovery learning approach, however, is to harness an iCALL tool that uses automatic patternmatching and visualization to focus learners on the most important patterns. Pattern matching and visualization can be used to help language learners notice and learn language features in context. The use ofpattern matching tools in language learning classes enables teachers to adopt learning approaches thatare data-driven or data-informed (Godwin-Jones, 2017). The pedagogic power of pattern matching ispredicated on the premise that to learn a particular language feature it is necessary to notice the language
feature. The advantage of using iCALL tools to learners is that the software development team has already selected the patterns worthy of attention, and so learners spend less time filtering out unnecessary
language, and can focus on the target language more easily than in typical DDL.
Intelligent CALL tools can involve both top-down move-analysis approaches and bottom-up linguisticfeature approaches (Cotos et al., 2017). By combining the power of natural language processing with the
behaviour and interaction properties of websites and applications, learners can be automatically guided
and assisted to discover patterns that were either preselected or can be discovered using an algorithm. The
automated mechanism to help learners see new patterns comprises two steps: matching and manipulating.
The matching, viz. the identification step, can be achieved via pattern matching or recognition. Regularexpressions (Regex) can be created to search texts for specific combinations or permutations of letters or
characters. Regex are, in the words of Christiansen and Torkington, (2003) akin to “mutant wildcards onsteroids” (p. 180). Regex are powerful search tools that can be used to identify predetermined patterns.
Once a particular pattern is matched, the discovered language features can be highlighted. This could
be achieved by using JavaScript to control the behaviour of elements in a webpage to emphasize thematched language feature by altering its colour or size. For example, software designed to focus on verb
phrases that users input could, on matching a verb phase execute a function that colorizes the matchedstring of words and provides a label stating the voice and tense of the verb phase.
As an example, consider the lexical set of quantifiers can be matched using simple regular expressions and classified into four distinct categories based on the grammatical category of nouns that they
can precede. The four categories (Q1 to Q4) are listed here:
Q1: quantifiers for singular nouns, e.g.{each, every, one}
Q2: quantifiers for plural nouns, e.g. {both, many, a number of, several}
Q3: quantifiers for uncountable nouns, e.g. {much, little}
Q4: quantifiers for uncountable and plural nouns, e.g. {all, a lot of, lots, most, plenty of, some}For each of these categories a separate regular expression can be created. When the regular expression
matches any of the elements, the matched word can be highlighted in bold. Each of the four categories
can be assigned a different background colour. When any element is matched, the assigned backgroundcolour can be displayed behind the matched string. The result will be an online text in which quantifiers
are colour-coded to highlight the grammatical categories associated with each quantifier. This manipulated text can be used by learners from absolute beginners through to upper intermediate. The colorized
texts helps learners differentiate between the four grammatical categories of quantifiers.
13﻿Intelligent CALLICALL TOOLS FOR LANGUAGE LEARNING
There are numerous iCALL resources that use pattern matching and detection. These include online
concordancing tools, word sketches and text profilers. In a Japanese university dedicated to computerscience, a number of online pattern-matching tools have been developed for language learning purposes.
WordBricks (Purgina et al., 2019) is a gamified mobile app designed to help learners acquire syntax
by experimenting with jigsaw-like word bricks to create syntactically accurate sentences. At the same
university, the Pronunciation Scaffolder was created to help undergraduate Japanese students deliver
scripted presentations. This iCALL tool uses natural language processing to automatically visualize
various pronunciation features (e.g., intonation, word stress, sounds) of texts submitted using colour,
size and symbols. The following subsections describe and evaluate each of these tools in turn.

WordBricks
WordBricks is designed to be able to work for any language, but was developed for Japanese learners ofEnglish. Although this language learning game uses pattern matching, the motivation for the tool was toprovide a gamified language learning experience by representing words or phrases in sentences as jigsaw
pieces and then allow players to fit the jigsaw pieces together in order. WordBricks allows sentences thatare syntactically accurate regardless of the semantics. This focus on form allows learners the freedom toplay with words in the same way as children can enjoy nursery rhymes with lines like “The cow jumped
over the moon …. And the dish ran away with the spoon.” Children acquiring language may replace thenouns and create their own semantically-incorrect sentences, such as “The monkey jumped over the sun.”Languages comprise syntactic elements that combine in specific orders. Some languages, such as
English, are fairly rigid in the order of words. Deviations from the expected order may be grammaticallypossible and treated as marked (i.e., less frequent) forms. Other deviations are treated as grammaticallyincorrect. WordBricks assigns syntactic elements to a particular class. Each class is attributed with a setof grammatical attributes that determine the elements that co-occur with it. For example, the verb “love”
is assigned as a transitive verb. The attributes assigned could include that the actor occurs to the left ofthe verb in the subject position, the receiver to the right in the object position, any adverb of manner isplaced after the receiver while adverbs of frequency can occur between the subject and the verb. WordBricks provides visual clues using shape and colour to show users how each syntactic element works.
This tool relies on pattern-matching. The possible permutations are derived on each placement ofa brick. A script is executed that checks whether the adjoining bricks allow the placement of a brick.
This can be visually deduced, since if the available position in a clause is rectangular, then only a brickthat is rectangular will be permitted. The user can match the pattern visually or linguistically. The idea
being that as the user gains familiarity with the bricks, mastery over the allowable syntactic combinations will increase.
WordBricks was positively received by both learners and teachers. When used for teaching grammar,
the simplistic rules described in typical textbook grammars mask the true complexity of natural language.
This simplification of grammar may be pedagogically justified, but does not sit well with computerscientists seeking to create expert systems that can deal with all possible use cases. Some grammaticalrules are difficult to implement in WordBricks because of vaguely defined categories, semantic concepts,
and the necessity for world knowledge to disambiguate different items.

14﻿Intelligent CALL

WordBricks is gamified, and so this can help to overcome the lack of inertia that demotivated learners of English may display. Teenage learners of English in Japan have spent many hours completing
grammar drills and rote learning vocabulary to pass university English entrance examinations. This
grammar game provides a novel way to help learners review or recall grammatical structures and honetheir knowledge about language, particularly the part-of-speech of words. Learners are encouraged toexperiment and construct syntactically accurate sentences. At the same time, they discover how word
choice affects brick type. Brick type is primarily determined by part-of-speech (POS), but the POS in turnaffects the compatibility of connecting bricks. For example, adjective bricks may follow copula verbs orprecede nouns, but most adjective bricks are unable to precede copula verbs or follow nouns. Descriptive grammars state various grammatical rules, but with WordBricks users experiment in a virtual lab
and try various positions for words, but only positions that are grammatically possible will be allowed.
WordBricks was tested with pre-intermediate Japanese learners who were studying grammar. At
the University of Aizu computer science majors enrolled in an EFL course were divided into a control
group (N = 11) and an experimental group (N = 10). All students were given pre-tests and post-tests on
two units both of which focus on uncountable and countable nouns, which are challenging for Japaneselearners of English. The post-test for countability involved identifying grammatical errors in sentences
while in the post-test for articles learners were asked to complete sentences with an appropriate noun
form. During the study all participants studied with the same teacher for four months. The control groupwere taught about countable and uncountable nouns using a teacher-centered, grammar-focused approachthat students were familiar with. The experimental group were provided with tablet PCs loaded with
WordBricks with predesigned exercises, based on the same course syllabus.
The results of this preliminary study (Park, Purgina, & Mozgovoy, 2016) were positive with the experimental group scoring relatively higher than the control group in both post-tests as shown in Table 3.

Table 3. Pronunciation features
Unit• First• Pre-test• Experimental• 10• 15.90• 4.43• Control• 11• 15.18• 5.04• Experimental• 10• 24.20• 4.02• Control• 11• 21.00• 5.80• Experimental• 10• 4.20• 2.57• Control• 11• 6.00• 2.72• Experimental• 10• 11.60• 2.84• Control• 11• 9.18• 4.17• Pre-test• Post-test

Mean score (M)Standard deviation
(SD)

Group

• Post-test• Second

Number of
students

Test

Descriptive statistics of the pre- and post-tests from the first unit show that the pre-test mean scoresin the experimental group (M = 15.90, SD = 4.43) and the control group (M = 15.18, SD = 5.04) weresimilar. In the post-test, the experimental group (M = 24.20, SD = 4.02) performed slightly better thanthe control group (M = 21.00, SD = 5.80). The pre-test scores differed more in the second unit with the15﻿Intelligent CALL

control group having a higher mean score (M = 6.00, SD = 2.72), which increased in the post-test (M =9.18 SD = 4.17), yet the post-test mean score in the experimental group almost tripled to 11.60 (SD =2.84). These results show that the experimental group outperformed the control group on the post-test.
Although there is a strong likelihood that students were engaged more deeply with WordBricks and thatwas the cause of the increase, sceptics could argue that other causal factors were at work.

Pronunciation Scaffolder
The Pronunciation Scaffolder (Blake, 2019) is designed to help learners of English who need to readpresentation scripts aloud. The impetus for the creation of this tool was to help Japanese undergraduate
students at the University of Aizu who need to deliver presentations in English to fulfil their graduation
requirements. However, for students who lack confidence and practice in speaking, this is particularly
difficult. Students tend to write the full script of their presentation to compensate for their lack of ability(or confidence) to produce grammatically accurate speech when reading notes. Despite having the exact
wording prepared in advance, students still struggle to read the words. The Pronunciation Scaffolder does
not address specific issues of public speaking, but focuses on basic reading aloud skills, such as whereto pause, how long to pause for, which syllables to stress and which words carry less stress.
The Pronunciation Scaffolder provides users with the choice of which features they want visualized.
There are three categories, six features and nine function buttons. Pronunciation features are divided
into three categories: core, optional and advanced. The core features are recommended for lower-level
learners and include pausing, intonation and content words. The optional features are word stress, soundsof the letter “s” and two graphemes, “-ed” and “th.” These features are designed to provide additional
assistance to Japanese learners with specific difficulties. The advanced feature is linking, which is subdivided into linking from consonant-ending words and vowel-ending words. The annotations are createdusing rule-based parsing to identify patterns. Once a pattern is identified, the pattern is annotated andvisualized. The visualization is realized by using JavaScript to colorize the text, alter its size or insert
symbols, such as arrow heads. Table 4 shows the pronunciation features that can be visualized.

Table 4. Pronunciation features#

FeaturePurpose of annotation•1• Pausing• show short, medium and long pauses•2• Intonation• show falling and rising intonation•3• Rhythm• stress content words more than function words•4• Word stress• locate syllable carrying primary stress•5• Sounds• disambiguate between letters realized by different sounds•6• Linking• shows elision, intrusion and linking

Figure 4 shows the political speech delivered by Sir Winston Churchill in 1940, which incidentallyis the same speech used for the word cloud shown in Figure 3. Visualization is shown for pausing, intonation, content words, word stress, -ed endings, th sounds and linking from consonant-ending words.

16﻿Intelligent CALL

Figure 5 shows the key to the annotations that uses colour and symbols. For example, word stress isshown by bold letters on a bright yellow background.
Figure 4. Speech annotated by the Pronunciation Scaffolder

Regular expressions parse for predetermined patterns to identify each pronunciation feature. One
straightforward pattern is to identify words ending in -ed. Once the word has been identified, the -ed
ending needs to be classified into one of the three possible pronunciations, namely voiced /d/, voiceless/t/ or vowel-added //ɪd/. Regular expressions were created for each of the three categories. This tool iscurrently unable to discriminate between words ending in -ed that have two possible pronunciations. For
example, the word learned as an adjective is pronounced differently from the word learned as a verb.
Should part-of-speech (POS) tagging be used prior to the rule-based parsing, this ambiguity could be
resolved by matching both the POS tag and the raw text.
Figure 5. Key to the annotations used17﻿Intelligent CALL

A preliminary study of effectiveness was undertaken with twenty university students enrolled in acompulsory English course at a private university in Thailand. To test the effectiveness of version 2 of
the Pronunciation Scaffolder students listened to a text-to-speech engine reading a presentation script,
and then practised reading aloud. Students selected the text, web-enabled device and recording software
themselves. They audio-recorded themselves reading both a raw script and an annotated script. Assessors (N = 5) who worked at the same university, but who were unaware of context or research aim wereasked to judge the quality of the delivery of both presentations. Assessors were asked “which recording(if any) sounds better?” The rather vague terms “better” and “sounds” were chosen to avoid a framingbias in the evaluation. Recordings were played in random order. For the higher proficiency students (N= 4) assessors rated both recordings as being the same, but for the less proficient students (N = 16) assessors rated the reading of the annotated script as being better. The Pronunciation Scaffolder improved
the quality of the pronunciation of the presentation for all lower-level students in this study. Although
this is a small-scale study, this adds weight to anecdotal evidence that lower-level students benefit fromusing this tool.

FUTURE RESEARCH DIRECTIONSThis chapter has shown the ubiquity of patterns, and how harnessing pattern matching and detection
can enhance data-driven language learning. The integration of NLP has brought traditional CALL into
a new era of intelligent CALL. The number of language learning platforms that integrate iCALL technology is increasing rapidly. The increased access to Wi-Fi combined with year-on improvements insoftware and hardware provide a firm foundation for iCALL to take a more prominent role in language
learning. NLP can be used to assist language learners analyze both native and learner language. This canbe achieved through sophisticated NLP pipelines or relatively simple rule-based algorithms. Advancesin artificial intelligence, such as neural networks and deep learning have already brought about radical
breakthroughs in machine translation, and appear set to radically alter the world of technology-enhanced
language learning.
Future research will focus on the development of iCALL tools, measuring their accuracy, speed andusability. These aspects are likely to be those foremost in the minds of computer scientists and computational linguists. However, the pedagogical side of iCALL tools is the primary consideration and so theeffectiveness and the efficiency of the tools needs to be evaluated through both empirical and experimental studies. With any experiment study involving humans, particularly those that focus on learning
processes, a particular challenge is to create a study from which statistically valid generalisations canbe made. The solution to this problem is beyond the remit of this chapter, but one which needs to beaddressed in order to evaluate the efficacy of iCALL.

18

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ADDITIONAL READING
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KEY TERMS AND DEFINITIONS
Algorithm: A set of rules to be followed.
Discovery learning: A theory in which learners reflect on their experiences to discover new ideas.
Hapax legomenon: Words that occur only once within a particular text or corpus of texts.
iCALL: iCALL stands for intelligent computer-assisted language learning, which refers to the useof natural language processing for language learning purposes.
Machine learning: When computers use artificial intelligence to learn from data or experience.
NLP: NLP stands for natural language processing, which uses computational methods to analyze
natural language.
Pattern: Pattern describes items that are organized regularly not randomly.
Regex: Regex and Regexp stand for regular expressions, which are powerful search expressions thatcan match characters, words and/or strings.
Rule-based parsing: A process that use rules related to syntactic structure to divide written texts
into components.
Zipf’s law: This law states that the frequency of a word is inversely proportional to the rank in frequency of the word.

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