Practice-Oriented Paper
Using Data-Driven Learning to
Discover Patterns of Grammar
Sarah Miyoshi Deutchman
Waseda University
Data-driven learning (DDL) is a powerful tool that can help students find patterns in languageon their own. Unlike a teacher or professor, corpora are available to students whenever theywish to consult them. In fact, students can feel empowered by using corpora. Additionally,
corpora can help to clarify notions found in a language. However, corpus consultation is not
something that is often taught to learners. This might be because using corpora to search forpatterns in language can appear intimidating at first. More user-friendly websites such as
Sketch Engine for Language Learning (SKELL) and Contemporary Corpus of American
English (COCA) have made corpus consultation more accessible to students. With sometraining, students can gain information that will help them improve their language skills.
This paper gives an overview of grammatical patterns found in corpora, discusses how to do
basic searches, and suggests some activities that can be used in class.

Corpora can be defined as "a collection of naturally occurring, computer-readable
texts, often comprising many millions of words, which is considered more or lessrepresentative of a particular domain of language use" (Hyland, 2013, p. 248).
Although corpora can arguably be made from any source of information fromEnglish for Specific Purposes (ESP) to academic writing, it is necessary to choose
corpora based on the needs of the course. Using corpora allows students to
consult with language as it naturally exists, which is important because sometimes
language used in textbooks and other resources may not be used in a moreinteractive context (Coxhead et al., 2017). Thus, if a student encounters words
outside of class, there is a possibility that they may not recognize it as a word thatthey already know. This is because different areas of study can use highly frequentwords in a specialized way. However, it may take a considerable amount of time21Deutchman

to create and compile one's own corpora, which could be too complicated forstudents or some teachers who are new to corpus linguistics.
However, it is possible to find ready-made corpora online available for free,such as Corpus of Contemporary American English (COCA) or Sketch Engine
for Language Learning (SKELL). Even though these corpora allow students tointeract with authentic language, that interaction is pointless if students cannot
comprehend what they are reading. Thus, it is important to decide which corpus
to choose depending on students' levels. It is also necessary to consider how andwhere the corpus is going to be used (Charles, 2018). COCA allows users tolook up genre, keyword in context (KWIC), compare words, see collocates, andlook for historical patterns (Davies, 2020). COCA also has a word functionthat displays many of these features on one page. Because of the abundance of
information that can be found on COCA, it may take a bit more training tolearn how to use it. On the other hand, SKELL is a bit more user-friendly inthat it only requires users to type in a single word. It, like COCA, can display
the word in a sentence (i.e., concordance lines). However, unlike COCA, thelines on SKELL are not truncated. Hirata and Hirata (2019) found that clean
concordance lines do not overwhelm learners with too much information
and are more user-friendly. Additionally, it is possible to use the word sketch
function to look for collocates (i.e., words associated with each other) and see
how those words are used together in a sentence. The downside of using SKELL
is that it works best with more frequently occurring vocabulary. For a word suchas uxorious, only the concordance lines will appear.
Students can analyze corpora using data-driven learning (DDL), which isan approach that uses computers as an intermediary to help students discoverlanguage patterns on their own ( Johns, 1991); it works well for learning
vocabulary items, basic grammar items, and verb phrases (Mizumoto & Chujo,2015). Because DDL relies on users to notice the patterns within the corpora,it requires teachers to act as directors to aid students in their research ( Johns,
1991). One of the main tools used in DDL is a concordancer, which is a toolthat shows how the word is used in a variety of contexts in various forms that canbe easily searched ( Johns, 1991). However, students will likely need guidance22

Using Data-Driven Learning, OCJSI 4, pages 21-31

when using a concordancer for the first time. Therefore, the teacher needs tochoose a question to find the appropriate information and then model the action
for the students (Cobb & Boulton, 2015). This paper aims to make corpora and
DDL more accessible to those who have not had experience with either before
by explaining the basic terminology used in DDL, talking about how to do basic
searches, and discussing how to plan activities that can be used in the classroom.

Grammatical Patterns Found in Corpora

DDL gives students the ability to interact with authentic language which makes
them more sensitive to its use and increases their metacognitive skills (Boutlon& Tyne, 2013). Moreover, DDL offers a flood of information that can be used tohelp learners deal with the fuzzy nature of authentic language (Boulton, & Cobb,
2017). Boulton and Pereiro (2008) have stated that neither traditional reference
sources nor intuition can helpfully disambiguate two similar words (e.g., almostand nearly). Therefore, using corpus consultation is beneficial. However, learners
need to be able to notice the patterns (Schmidt, 1990 as cited in Boulton & Cobb,
2017) found in corpora such as collocations, colligations, and lexical bundles.
When studying new words, it is important to understand which words
usually occur together (i.e., collocations). This is because understanding
collocations is part of knowing a word (Schmitt, 2010). The classic example ofcollocation use is strong coffee versus powerful coffee. Although there is nothing
grammatically wrong with powerful coffee, the phrase sounds unnatural to a native
speaker. This is because knowledge of when and where to use collocations may bedifferent between native speakers and non-native speakers of a language (Zhang,1993). An example of this is the word wear in English. In Japanese wear can betranslated to kiru, kaburu, haku, hameru, kakeru, shiteru, and chakuyou shiteiru
depending on what part of the body is being talked about. If one of the Japaneseverbs for wear was matched with the wrong body part, the writing would appear
unnatural, which could lead to a lower quality of writing. On the other hand,those who have a more accurate knowledge of collocations have a higher qualityof writing (Zhang, 1993). Therefore, teaching collocation use is important.
Another grammatical pattern that can be investigated by using DDL are23

Deutchmancolligations, environments in which a word is used, what words are connected toit, and what the grammatical patterns are (Sinclair, 1996, 1999, 2004 and Hoey,
1997a, 1997b, as cited in Hoey & O'Donnell, 2008). For example, despite + det+ adj + noun (e.g., Despite a serious injury; Despite a pristine record; Despitea decent cast). Colligations can confuse students if they are not aware of which
pattern goes with which word. An illustration of this is understanding how andwhen words should be used in context. While is usually used with a verb + ing
form (e.g., while she was dreaming). During is used with a period of time (e.g.,during the 1980s, during World War 2). The difference between using while andduring can become clearer when consulting corpora.
The final language pattern in this section is lexical bundles which can be
explained as extended collocations that frequently appear together (Biber et al.,1999, as cited in Hyland, 2012). It is important to study lexical bundles as they
can aid in writing proficiency. Understanding how these multi-word units areused helps improve fluency (Hyland, 2012). By using the KWIC on COCA it ispossible to search for strings, such as due to the fact where it can be seen that due
to the fact is more likely a reduced form of due to the fact that. This result shows
students what the full lexical bundle looks like and how to use it in context.

Basic Searches in COCA

The section addresses how to do some basic searches in COCA, such as
collocations, comparison, KWIC, and genre. When using the collocation
function, it is possible to adjust for how many words in front of or behind a word
should be examined. Thus, if one were to look at which adjective were connected
to a noun, it would make sense to look at one or two words in front of that nounand select adj for the collocate (Figure 1). Results for a typical search on the word
culture show the top ten most common collocates in order (Figure 2).
Comparison searches can show which nouns should go with much or many.
To do this search it is necessary to select nouns from the POS options and limit
them to one word behind much or many. This is done because nouns would
occur one space behind much and many in a sentence (Figures 3 and 4).

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Using Data-Driven Learning, OCJSI 4, pages 21-31

Figure 1. How to do an adjective collocate search culture + adj.
Note. It is necessary to push the (+) button to see the collocate search option. The POS can bechanged depending on what pattern is being examined.

Figure 2. Results of the collocation search.
Note. It can be seen that popular is most commonly associated with culture.

Figure 3. How to do a comparison search with much and many.

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Deutchman

Figure 4. Results of the comparison search of much and manyNote. Countable nouns occur with the word many (only countable nouns use the plural -s) anduncountable nouns occur with the word much.

By using these basic searches, it is possible to create a multitude of DDLbased activities that can be done by students.
KWIC can be used to show a single word or phrase together in context(Figures 5 and 6). It is possible to set options to choose different contexts (e.g.,
academic, blog, news, spoken).
Genre searches can be used to assist with academic writing as students will be

Figure 5. KWIC search with ‘Due to the Fact That’ limited to academic writing.

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Using Data-Driven Learning, OCJSI 4, pages 21-31

Figure 6. KWIC search with ‘due to the fact that’ limited to academic writing.

able to see if a word is academic or not (Figures 7 and 8). Students can also checkif a word is still being used.

DDL Based Activities

There are many activities learners can do that involve corpora from less planned
activities to more highly controlled activities (Boulton & Tyne, 2013). Multiple-

Figure 7. ‘A lot of ’ genre search.
Note. By using the chart function, it is possible to see where and when a word is used.

Figure 8. Results of ‘a lot of ’ genre search.
Note. A lot of would usually be used when speaking and would rarely be used in academic writing.

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Deutchmanchoice activities are an example of more controlled activities. With this activity,
students can be given idioms on one side and definitions of the idioms on theother side. By reading the sentences on COCA, students can get the information
to match the idiom to the appropriate definitions (Figure 9). Additionally, it ispossible to use coded feedback and have students look at corpus data to fix theirerrors (Tono et al., 2014). Teachers can look through students' essays and noticecommon patterns of error. For example, the chart function can be used to show
students which words would be used in academic writing.
Although there is no real limit to the number of activities that can be createdusing DDL, some practices should be used when planning these activities.
First, it is best to run the searches and have results printed up in case students’
results are not consistent. Corpora can be updated regularly and as a result,search results can change. Second, for beginners, it might be best to start withscreenshots of the searches and the results so students can use them for reference.
Third, it is important to consider why these tasks are being done (e.g., targeting
previous mistakes, showing academic vocabulary). Fourth, time constraints
should be considered. It is unlikely that students will have experience withcorpora. Therefore, it is necessary to consider how much time is needed to train
students. If the materials provided are relatively simple, such as using a gap-fillworksheet with pre-printed search results (Figure 10), students should be able

Figure 9. Example of a meaning-focused activity.
Note. In this activity, students can use the concordance lines to try to determine the meaning ofeach idiom.

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Using Data-Driven Learning, OCJSI 4, pages 21-31

Figure 10. Example of a gap fill activity.

to do the worksheet independently after the activity is modeled for them. With
more complex, less controlled activities it can take a couple of hours of training.
Fifth, and perhaps the most important, is to consider the students' language
proficiency level. DDL-based activities are something that should not be painful
to experience. If students are at a lower level, it is possible to use bilingual
corpora and have students compare patterns between their L1 and L2. Sixth,
it is important to remember that learning to consult corpora is a process. Even
if students have a lesson on using corpora it does not automatically mean there
will not be any errors in their writing. Lee et al. (2009) found that the writing
products of their students still had some errors which might have been due toincorrect concordancing, issues during training, or different ideas on grammar.
Thus, even though DDL cannot solve all writing problems, it is still helpful.

Conclusion

Although using corpora and DDL may feel daunting at first, with some training
both teachers and students can use them effectively. DDL allows students togain autonomy by being able to use corpora whenever they desire. Additionally,
students can understand which words are usually associated with each other.
In the future, DDL should be built into curriculums as it helps learners reach a29

Deutchmanhigher language proficiency.

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Author Bio

Sarah Miyoshi Deutchman teaches at Waseda University as a part-time lecturer.
She has taught English for over 10 years in three different countries: America,
South Korea, and Japan. Her areas of research include data-driven learning, corpus
linguistics, and vocabulary. sarah@aoni.waseda.jp
Received: October 31, 2021
Accepted: September 27, 202231