【Research Article】

Training Learners of Writing to Use Online Vocabulary Tools
to Increase Lexical Richness
Tanya L. Erdelyi
Abstract
Lexical richness is often used as an indicator of productive language proficiency. Researchers regularly utilize avariety of digital tools (i.e., VocabProfile, RANGE) to analyze the lexical richness of a language learner’s written text.
However, many studies do not provide the opportunity for learners to use the same tools to analyze their own writing.
The following longitudinal mixed-methods study analyzed the written essays of 36 Japanese lower English proficiency
first-year university students, before and after they received training in the usage of VocabProfile, Corpus ofContemporary American English, and Google’s Ngram Viewer. The first and final drafts of the essays were analyzed
with RANGE and compared with a paired-samples t-test. Next, 65 randomly selected sentences containing
low-frequency words were analyzed with a 4-point scale for errors by six human raters. Finally, a self-efficacy
questionnaire completed by the students was compared with the statistical results. The results indicated a significant
increase in the error-free lexical richness of the language learners’ essays between the first and final draft.
Keywords: Academic writing, Lexical richness, VocabProfile

Learning vocabulary is a key component for developing language proficiency. Once the mostcommon high-frequency words in English have been obtained (i.e., the first 2000 most frequentwords), the next logical step is for language learners to continue increasing their academic andlow-frequency vocabulary knowledge, especially for language learners at the tertiary level of
education. Increasing a learner’s lexical richness can assist the learner in the reading comprehensionof academic texts, as well as enable them to mirror the language used in their field of study whilewriting.
Lexical richness is often used to determine the academic quality of a language learner’s
productive language. In terms of writing, generally speaking, lexical richness is determined byanalyzing a language learner’s written work for the density and variety of low-frequency words.
Furthermore, the words should be accurate in meaning, grammar, and word combinations or
frequent collocates (Nation & Webb, 2011). To measure lexical richness, some tools that are often
used are Lexical Frequency Profiles such as Tom Cobb’s VocabProfile for measuring lexicalfrequency (Abbasian & Shiri, 2011; Cobb, 2002.; Laufer & Nation, 1995) or RANGE (Heatley et al.,
2002; Kyle, 2019). Additionally, concordances found in the Corpus of Contemporary American
English (COCA) and Google’s Ngram Viewer are often used to check collocations and multiword
units. By using these tools to examine these factors in a written text, the lexical richness of said text
can be determined.
Data-driven learning (DDL) and Corpus-based learning (CBL) are common approaches forincreasing a language learners’ lexical richness. The focus of DDL is the use of computers as a tool
for language learners. CBL involves corpus-based tools, such as a concordancer, for language
learning. By providing learners with examples of linguistic performance through tools such as a
keyword-in-context concordance, the learners are encouraged to use their brains to decipher thecorrect way that language is used (Johns, 1991).

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Although there are a multitude of studies conducted on lexical richness, DDL, and CBL, there isa need for more empirical data on CBL and DDL (Gaskell & Cobb, 2004; Gries, 2015). Moreover,there are a few issues that should be explored more fully. Many of the studies have focused primarily
on intermediate to advanced language learners (Cobb, 2010; Gaskell & Cobb, 2004; Granger, 2012;
Henriksen & Danelund, 2015; Nesselhauf, 2005), with little focus on learners with lower language
proficiency. Moreover, various studies have been performed in one sitting (Cobb, 2010; Gaskell &Cobb, 2004; Gilmore, 2009) as opposed to a more longitudinal design, such as across an entire
semester. Additionally, most studies involve teacher-supplied resources or employ teacher-designed
worksheet-based methods derived from some of the previously mentioned vocabulary tools (Cobb,
2010; Gaskell & Cobb, 2004; Granger, 2012) instead of allowing the students to explore their own
writing by personally using these tools. Finally, although Lexical Frequency Profiles can measurelexical richness in terms of word frequency (Abbasian & Shiri, 2011), they cannot measure theaccuracy of word use in terms of grammar and semantics. Therefore, more research is required toaddress these issues.
As mentioned above, many researchers have made an effort to examine the vocabulary use oflanguage learners to acquire information about the complexity of their language. They employ a vastarray of tools designed to analyze and evaluate the vocabulary used by their participants.
Nevertheless, equipping these learners with the very tools used by researchers might yieldinteresting results. By allowing the learners to analyze and evaluate their own writing throughexplicit instruction in utilizing some of these vocabulary tools and discussing the benefits of the data
they receive, it is hoped that the lexical richness of the learners’ writing may improve.
The following study attempts to contribute to the research that has been conducted on lexicalrichness, DDL, and CBL. It was designed to explore the effects of teaching learners to use vocabularyrelated tools to increase the percentage of academic and low-frequency words in their academic
research papers, thus improving the paper’s lexical richness. An additional focus of the current study
is to check the effectiveness of providing tools to help reduce the number of errors when the learners
increase the number of academic and low-frequency words.
In order to address these issues, the following three research questions were created andanswered through document and statistical analysis:
1. What, if any, is the increase in coverage of academic words after receiving explicit instruction in
the use of vocabulary tools for analyzing written work?
2. What percentage of the academic and low-frequency words used to replace high-frequency words
was both grammatically and semantically correct?
3. How do errors in writing before and after the treatment instruction compare in both quantity andquality?

Literature Review
The following is a brief overview of literature related to the topic of study in this paper. A closer
look will be given to studies conducted on lexical richness, DDL, and CBL.

Lexical Richness
Several studies have provided results on the existence of lexical richness in language learners’
writing. Some studies have shown that lexical richness in undergrad students reflects their106

Training Learners of Writing to Use Online Vocabulary Tools to Increase Lexical Richness

pre-existing knowledge of vocabulary (Ha, 2019). However, much of the existing literature on lexicalrichness shows that there is a paucity of lexical richness in second language writing. Some studies(Henriksen & Danelund, 2015) have shown that language learners with a higher level of English
proficiency tend to rely heavily on comfortable and easier to use high-frequency vocabulary rather
than attempting to incorporate their low-frequency vocabulary.
One criticism of how lexical richness is measured is that Lexical Frequency Profiles such as
VocabProfile and RANGE only provide measures of vocabulary quantity and do not account forerrors in grammatical and semantic use. Therefore, although these Lexical Frequency Profiles
indicate lexical richness to some extent, they should be used in conjunction with other forms of
measure (Abbasian & Shiri, 2011). Although some studies (Stæhr, 2008) use human raters to analyze
student writing holistically for errors in grammar and vocabulary, many studies on lexical richness
focus solely on the number and types of lexical items (Laufer & Nation, 1995; Lei & Yang, 2020).

Data-Driven Learning (DDL) and Corpus-Based Learning (CBL)
Studies on DDL and CBL have uncovered many results concerning the use of online tools to
help improve language learner writing and recognize errors. According to several researchers, DDL
has successfully helped learners find their own solution to language problems using authentic
resources and tools (Boulton, 2009; Cobb, 2010; Gilmore, 2009; Granger, 2012; Johns, 1991).
Researchers have also discovered that CBL is useful for recognizing patterns in grammar and word
use to help with error correction (Cobb, 2010; Gaskell & Cobb, 2004; Gilmore, 2009). Moreover,studies have shown that concordance information is useful for intermediate and advanced learners
during writing activities (Cobb, 2010; Gaskell & Cobb, 2004).
Studies on DDL and CBL have employed a variety of methods. Given the inherent difficulty ofusing some of the available online tools (Lee & Lin, 2019), many of the studies have focused primarily
on intermediate to advanced proficiency level language learners to lessen the cognitive load (Cobb,
2010; Gaskell & Cobb, 2004; Granger, 2012; Henriksen & Danelund, 2015; Nesselhauf, 2005). Often
in response to the difficulty in using some of the tools, various studies have resorted to employing
teacher-supplied resources or worksheets designed by teachers that had been derived from theonline vocabulary tools (Cobb, 2010; Gaskell & Cobb, 2004; Granger, 2012) rather than asking thelanguage learners to use the tools themselves. Additionally, regarding the implementation of many
DDL and CBL studies, a common preference has been to conduct the research in one sitting (Cobb,
2010; Gaskell & Cobb, 2004; Gilmore, 2009) as opposed to a more longitudinal design, such as acrossan entire semester.
After reviewing the existing literature, several missing components were discovered. To begin
with, more studies that evaluate lexical richness in terms of grammatical and semantic accuracy areneeded. Additionally, there is a need for more studies on lexical richness with lower level proficiency
language learners. Finally, there seems to be a need for more longitudinal studies conducted over alonger period of time that involve the use of authentic online vocabulary tools. Therefore, the current
study has been designed to address these issues.

Methodology
The following section contains an explanation of the methodology for the current study. Included
are details about the context, research design, procedure, treatment, and questionnaire. It ends with107

外国語教育研究ジャーナル 第 3 巻（JOURNAL OF FOREIGN LANGUAGE EDUCATION AND RESEARCH, VOL. 3）

an explanation of how the data were analyzed.

Context
Research was conducted on the written texts of 36 Japanese first-year university students, 25
women and 11 men between the ages of 18 and 20, learning academic English writing at a private
Japanese university. This study was conducted in an academic reading and writing class that met
thrice a week for 10 weeks during the third and final term of the learners’ first year. At the beginningof the school year, the students wrote a TOEFL PBT for the purpose of placement. The scores ofthese learners range from 350 to 450, therefore the majority of the students in this study were in the
low-intermediate level of English proficiency.

Research Design
This mixed-methods longitudinal study was designed to analyze the lexical richness of academic
papers written by lower-proficiency English learners over one entire university semester. It was
designed to address some of the issues found in the literature by providing empirical evidence on
lexical richness, DDL, and CBL.

Procedure
The learners were asked to write an 8-paragraph secondary research paper comparing andcontrasting a topic of their choice in bioethics. The paper was written in sections during the 10-weekcourse. Two drafts of each section were written, with peer editing and revision conducted between
each draft and section. At the beginning of the course, the students wrote two drafts of a single
introduction paragraph. This was followed by two weeks devoted to the two paragraphs in Section 1.
Another two weeks were spent focusing on the two paragraphs in Section 2. The final section, which
comprised two main body paragraphs and the concluding paragraph, were written in two drafts over
two weeks. Table 1 shows the timeline of the language learners’ research paper.
Table 1Timeline of Research Paper and Treatment
Time

108

What Was Finished

Week 1

Topic Selection and Outline

Week 2Introduction Draft 1

Week 3Introduction Draft 2

Week 4

Section 1 Draft 1

Week 5

Section 1 Draft 2

Week 6

Section 2 Draft 1

Week 7

Treatment Instruction

Week 8

Section 2 Draft 2

Week 9Section 3 and Conclusion Draft 1

Week 10

Section 3 and Conclusion Draft 2

Training Learners of Writing to Use Online Vocabulary Tools to Increase Lexical Richness

Treatment
As shown in Table 1, the treatment occurred after the first draft of the second section, at the
midway point of the paper. This ensured that the first drafts of the introduction, Section 1, andSection 2 were not affected by the treatment.
For the treatment, the participants were introduced to the Academic Word List (AWL) and asked
to begin self-study of the words using various vocabulary-learning strategies such as word cards andpractice exercises such as cloze sentences available on the Internet. Then, the participants were
taught how to use various vocabulary-related tools to analyze their own writing. Following Nation’s(2009) guidelines for training students in learning strategies, each tool, over two lessons in computerequipped classrooms, was first modeled by the teacher. Next, the participants practiced the different
steps alone, and then with partners, reporting back to the teacher when problems arose. Occasional
feedback was provided by the teacher and fellow classmates in the peer editing sessions. Finally,further consultation was provided to individual learners during tutorial sessions in the teacher’s
office.
The learners were asked to analyze their papers with the tools and replace high-frequency
words with words from the AWL or low-frequency words. The AWL (Coxhead, 2000) is a list ofacademic words derived from a corpus consisting of over a million words from academic texts. It
contains the most frequent academic English words after West and West’s (1953) General Service
List (GSL) comprising the 2000 most high-frequency English headwords. The AWL encompasses 570
word families divided into 10 sublists according to frequency. Coxhead (2000) recommends that the
AWL should be taught explicitly, allowing for opportunities for the vocabulary to be met in meaningfocused reading and listening texts, and used productively in speaking and writing. Meant purely asa goal to help with motivation, the students were asked to aim for 10% AWL words because according
to Coxhead (2000), “The AWL accounts for 10% of the tokens in the Academic Corpus” (p. 222).
Following a few tutorial sessions where some of the participants showed concern on finding enough
AWL words to boost the percentage, the learners were encouraged not to ignore the low-frequency
alternatives that were not present in the AWL as well.
The first tool taught to the participants was Cobb’s (2002) VocabProfile, conveniently located on
his website, Lextutor. The version they used categorized the first 1000 and second 1000 words fromthe GSL, and the AWL words in their research papers. All other words were marked as “Off-list” (i.e.,
low-frequency words, proper nouns, non-English words, spelling errors). Once they had learned to
identify the high-frequency words, the participants were shown how to use the right-click functionsof both Google Docs and Microsoft Word that provide possible synonyms for the highlighted word inquestion. The participants were warned that, even though a potential replacement might have been
located, the word had to match the sentence in both grammar and meaning. To help check thereplacement words in the sentences, the participants were taught how to use the Google Ngram
Viewer (Michel et al., 2010) for checking which word combinations are used most often and thecolor-coded keyword-in-context concordance on the COCA website (Davies, 2010; Johns, 1991) forchecking possible collocations and recognizing possible patterns and how others use the language.
With these tools, the participants set out to improve the lexical richness of their research papers.

Questionnaire
On the final day of the course, when the final drafts of the research papers were due, the109

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participants were asked to complete a questionnaire about the tools they did or did not use while
improving the lexical richness of their writing. The timing of the questionnaire completion waschosen for maximum effect, as the probability of the participants using the tools prior to the class inorder to complete the assignment was high and therefore fresh in their minds. The questionnaire, as
seen in Appendix A, consisted of seven multiple-choice questions and one open-ended question for
qualitative purposes. The multiple-choice questions were asked to confirm which tools the students
used and found most useful and easiest to use and to learn if the students used other tools that were
not discussed in class. The open-ended question asked what was most difficult about changing thehigh-frequency words to more academic or low-frequency words. Thus, a complete picture of thetools that were used was obtained.

Data Analysis
There were three sources of data used in this study. The first was the research papers written by
the participants, 72 in total, with 36 first drafts and 36 final drafts. The next available data were fromthe questionnaire. The last source of data was individual sentences randomly chosen from the papers
that contained an AWL or low-frequency replacement word.
Using the steps involved in measuring lexical richness from Nation and Webb (2011, p. 256), thefollowing decisions were made:Steps Involved in Measuring Lexical Richness
1. Decide on the text to be analyzed (research papers)
2. Decide on the unit of counting (word families)
3. Decide what to do with errors (compare the original and replacements words)
4. Decide on how to measure lexical richness (multiple human raters) (p. 256)
For analyzing data to help answer the first research question concerning the coverage of AWL
words, the first and final drafts of all the participants were compared. To ensure that only theparticipants’ words were being analyzed, all direct quotes and proper nouns were removed from eachdraft. Word family counts for the AWL words were obtained from the RANGE program (Heatley,
Nation, & Coxhead, 2002). According to Durrant and Schmitt (2009), much can be learned byexamining and comparing individual scores, as these results are often hidden when only entire
corpora are compared. Therefore, the first drafts of each participant were compared with their final
drafts using a paired-samples t-test.
With regard to the data analysis used to answer the second research question regarding thegrammatical and semantic fit of the replacement words, 65 sentences from 20 participants wererandomly chosen from the fourth paragraph of the first and final drafts that contained academic or
low-frequency replacements of high-frequency words. The fourth paragraph was chosen as it camefrom Section 2 of the paper, where the treatment occurred, thus quite possibly representing the best
effort by the participants in using the tools learned during the treatment. Each word replacement
was presented in its original sentence and rated on a four-point scale as seen below. The capitalized
word was the low-frequency replacement word. The word in brackets was the high-frequency wordfrom the first draft.
Example:No prospect of UTILIZATION (using) alternative ways has yet emerged.

110Training Learners of Writing to Use Online Vocabulary Tools to Increase Lexical Richness• both grammar and word choice are correct• correct grammar, problems with word choice• correct word choice, problems with grammar• both grammar and word choice are incorrect
Each of the 65 sentences was rated by at least three of six human raters, all members of the same
applied linguistics doctoral cohort as the researcher. The raters were trained as a group to help
ensure inter-rater reliability. Inter-rater reliability was calculated by dividing the number of agreedupon items with the total number of items.
For the final research question concerning the quantity and quality of errors between the two
drafts, the same human raters and 65 sentences were used. Following each sentence, the raters wereasked to compare and assess each original and replacement word to determine which was morecorrect using the following four-point scale.
Example of rating scale:
For the previous sentence, which word was most correct?
• Both are equally correct• Word in ALL CAPS
• Word in (brackets)• Neither

Results
The following are the results for this study. Discussion of the results is presented in theConclusions section.

Questionnaire
Table 2 presents the results from the questionnaire that was completed by the participants. As
can be seen, nearly all participants used VocabProfile to analyze the word frequency of their papers,
as opposed to those few who reported using Ngram or the concordance.
Table 2
Data from Questionnaire
Used

Most UsefulEasiestMore Instruction

Most Problematic

VocabProfile

97.2%

83.3%

47.2%

50.0%

Matching meaning

COCA

11.1%

2.8%

2.8%

38.8%

41.6%

Ngram

13.8%

2.8%

2.8%

22.2%

Matching grammar

MSWord Syn

66.7%

27.7%

41.6%

16.6%

16.6%

Google Syn

36.1%

13.8%

5.5%

2.8%

Collocations

Grammarly

88.8%

5.5%

13.8%

0.0%

5.5%

Dictionary

11.1%

13.8%

0.0%

0.0%

Note. Grammarly and dictionary use were not taught by the teacher for this study.

Texts Analyzed
Table 3 presents the descriptive data of the texts that were analyzed. The numbers reflect the
number of words after removing the direct quotes and proper nouns.
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Table 3
Descriptive Data of Texts Analyzed (Adapted from Durrant & Schitt, 2009)
DraftsNumber of
Texts

Number of
Writers

Total Words

Mean
Words/ Text

Writers’ L1

Academic
argumentative

First

36

36

46,684

1,297

Japanesesecondary research
papers written by
first-year Japanese
university students withlower-intermediate
English proficiency
studying English
academic reading andwriting
Paper topic - bioethics

First

36

36

55,255

1,534Japanese

Description

Individual Scores Between the First and Final Drafts
A paired-samples t-test was conducted to evaluate whether the number of AWL word families
would increase between the first and final drafts after a treatment on the use of vocabulary tools. The
results indicated that the mean AWL count for the final draft (M = 61.86, SD = 25.68) was significantly
greater than the mean AWL count for the first draft (M = 42.78, SD = 20.11), t(35) = -3.51, p = .0004.
The standardized effect size index, d, was .83. The 95% confidence interval for the mean difference
between the two ratings was 35.97-70.55 (Green & Salkind, 2013).

Percentage of Grammatically and Semantically Correct Replacements
Table 4 displays the percentages assigned to the replacements as determined by the humanraters. The majority ruling for the rating of each replacement word was used in the analysis. Theinter-rater reliability was 86%. It should be noted that the highest percentage was obtained by word
choices that were both grammatically and semantically correct.
Table 4
Analysis of the Replacement Words
Grading Criteria

Percentages

Both grammar and word choices are correct

56.9%

Correct grammar, problems with word choice

35.4%

Correct word choice, problems with grammar

6.2%

Both grammar and word choice are incorrect

1.5%

Comparison of Errors
Finally, a comparison between the original high-frequency word used in the first draft wascompared with the replacement academic or low-frequency word to determine which word, if any,
was more correct. Table 5 exhibits the percentages of the error judgments made by the raters. The
scores used for the analysis were the ratings that received the majority vote from the raters for each112

Training Learners of Writing to Use Online Vocabulary Tools to Increase Lexical Richness

pair of words that were analyzed. The rater reliability for this was also 86%. Of particular interest isthat 90.9% of the replacements were considered to be more correct by the human raters.
Table 5
Analysis of the Errors
Grading Criteria

Percentages

Both are equally correct

55.4%

The academic replacement is correct

35.4%

The original high-frequency word is correct

7.7%

Neither is correct

1.5%

Conclusions
This discussion is presented to help analyze the results, situate the findings in the existing
literature, and answer the following three research questions: 1) What, if any, is the increase incoverage of academic words after receiving explicit instruction in the use of vocabulary tools foranalyzing written work? 2) What percentage of the academic and low-frequency words used toreplace high-frequency words was both grammatically and semantically correct? 3) How do errors in
the first and final drafts compare in both quantity and quality?

Findings
Although many of the new tools, such as Ngram and the COCA, were not used by the majorityof the participants, as indicated in Table 2, all but one participant used the VocabProfile. The fact thatthe more difficult Ngram and COCA were underutilized coincides with the findings from Lee and Lin(2019). In addition, many students also used the synonym functions in Microsoft Word and Google
Docs. Therefore, as shown by the results of the t-test, there was a significant increase in the coverageof academic words from the first to the final draft. As there was no control group, it cannot be
definitely said that the increase in coverage was due solely to the explicit instruction of the vocabulary
tools. However, the learners appear to have gained awareness of the frequency level of their
vocabulary just by using VocabProfile. Should they continue to use the tools, it could potentially
increase their lexical richness in future papers.
Additionally, the second research question was created to address issues about the lack ofattention given to the accuracy of vocabulary use especially regarding grammar and meaning, as
discussed in studies such as that of Abbasian and Shiri (2011). Human raters analyzed the language
learners’ written work, similarly to Stæhr (2008). Ultimately, 56.9% of the low-frequency replacements
were deemed both grammatically and semantically correct by the raters. Furthermore, the resultsfrom the raters coincided with the qualitative data collected from the questionnaire, as can be seen in
Table 2. At 41.6%, the participants reported that matching the meaning of the replacement word to
the original was the most difficult, whereas only 16.6% deemed grammar as the most difficult factor.
When compared with the results in Table 4, the sample replacement words had 35.4% errors in word
choice as opposed to 6.2% grammar errors. This might indicate a need for further instruction in the
Ngram and COCA concordance tools, and it also coincides with Lee and Lin’s (2019) findings.
Concerning the third research question, the raters determined that the replacement words, on
average, were more correct than the original high-frequency words. This might indicate that the113外国語教育研究ジャーナル 第 3 巻（JOURNAL OF FOREIGN LANGUAGE EDUCATION AND RESEARCH, VOL. 3）

tools were useful for replacing the high-frequency words, as they possibly provided the learners with
opportunities to analyze more closely the grammar and meaning of the words being used than theynormally would have. These results are consistent with some of the previous studies (Boulton, 2009;
Cobb, 2010; Gilmore, 2009; Granger, 2012; Johns, 1991).

Implications
This study provides more empirical data on lexical richness, CBL, and DDL that might help fill
the gaps in the literature discussed earlier. It is also a longitudinal study across an entire semester onlearners with lower English proficiency. However, the most important point about this study is that it
could possibly show the potential involved in encouraging the learner to use some of the vocabulary
tools generally used by teachers and researchers, especially VocabProfile. Because of the
improvements in lexical richness that occurred through the use of these vocabulary tools, teachers
might consider training their students to use vocabulary analysis tools more actively as part of theirstudents’ writing process.

Limitations and Future Study Recommendations
There are a few limitations and possible recommendations for future research that arose during
this study and should be addressed.
First, the learners were also asked to use low-frequency words that were off-list from the GSL
and AWL as replacements. With the removal of proper nouns from the participants’ research papers,
the remaining off-list words are presumably all low-frequency words. However, both RANGE andVocabProfile do not report the word family count for these off-list words. Moreover, according to theraw data, all 36 participants’ off-list word counts increased, at least as was indicated by the word types
percentage in RANGE. Thus, processing the two drafts of the papers through the British National
Corpus (BNC) version of RANGE to check for the frequency of the words used between the 1st -14th
1000 words in English was considered. However, it should be noted that the GSL and the BNC are
not completely analogous, thus introducing a limitation to the analysis of this study. The GSL/AWL
were chosen for their ease of comprehension and processing for the participants. Future researchers
might consider using the BNC for obtaining a more accurate account of the increase in low-frequency
word replacements.
Additionally, because of the low percentage of use with the Ngram and COCA for checking theappropriateness of the replacement words, and as indicated on the questionnaire, more instruction
should be given in the use of these valuable tools.
Furthermore, there is the possibility of distorted results from participants who did not use all of
the tools (Gilmore, 2009). However, a counter to this was attempted by comparing individual scores(Durrant & Schmitt, 2009; Granger, 2012).
Moreover, further confounding variables, such as the effects of peer editing, and other writing
tools not taught in the treatment, such as Grammarly, should be isolated or controlled for in futurestudies.
In addition, as Gilmore (2009) explained, there is a need for a control group. This study did nothave a control group. Potentially, a similar writing assignment from the previous year’s cohort at thesame university from the same academic reading and writing course could have been used as acontrol group for this study. However, due to time constraints and the need for obtaining the114

Training Learners of Writing to Use Online Vocabulary Tools to Increase Lexical Richness

permission of the previous year’s students, this could not be accomplished.
Finally, the placement of the treatment was handled acceptably for this particular research
paper. However, as this particular research paper was written in sections, the first drafts of Section 3
and the concluding paragraphs quite possibly were affected by the treatment. Therefore, futurestudies might elicit purer results if the treatment is placed between the first and second drafts of an
entire paper or essay.

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Appendix A
Vocabular y Questionnaire
Please answer these questions HONESTLY about your research paper and vocabulary. You may
check more than one answer if you need to:
1. Which vocabulary tools did you use?
□ VocabProfile□ COCA concordance□ Google Ngram Viewer□ Google Docs synonyms (right click and define)□ Microsoft Word synonyms (right click and synonym)
2. Did you use any other tools I didn’t teach you?
□ Dictionary – what kind? _____________________________________________□ Grammarly□ Only my brain□ Other(s) __________________________________________________________
3. Which tool(s) were the most useful?
□ VocabProfile□ COCA concordance□ Google Ngram Viewer□ Google Docs synonyms□ Microsoft Word synonyms□ Other(s) __________________________________________________________
4. Which tool(s) were the easiest to use?
□ VocabProfile□ COCA concordance□ Google Ngram Viewer□ Google Docs synonyms□ Microsoft Word synonyms□ Other(s) __________________________________________________________
5. Which tool(s) would you have liked more instructions/directions from the teacher?
□ VocabProfile□ COCA concordance□ Google Ngram Viewer□ Google Docs synonyms□ Microsoft Word synonyms□ Other(s) __________________________________________________________

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6. Did your peer editors help with your academic words?
□ Yes
□ No
□ I don’t know
7. Did you help your writing group members with their academic words?
□ Yes
□ No
8. What was most difficult about changing the vocabulary to more academic words?

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