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Comparing First-Term Students’ English Language Proficiency at a  

Canadian Polytechnic Institute 

 

Nathan J. Devos 

British Columbia Institute of Technology 

 

Abstract 

 

Canadian post-secondary classrooms are linguistically diverse. This diversity comes from 

immigration, more access to higher education for marginalized and minority groups, and 

international student recruitment. An institute’s language prerequisites serve as tools to help 

admissions decide who is linguistically prepared for study in English. Despite these language 

prerequisites, some students still need support with their language development to succeed in 

intense, two-year diploma programs at polytechnic institutes. The objective of this study, 

therefore, was to explore first-term students’ language proficiency and compare differences 

based on language and education variables. The long-term goal of the project is to create local 

language diagnostics to identify and support students during their diploma programs. Participants 

were recruited from four different programs at a large polytechnic institute in western Canada. 

Reading, writing, and vocabulary tests were accompanied by a 15-item survey about language 

and education backgrounds. In total, 437 first-term students participated in the tests and survey. 

The results suggest that there are statistically significant differences between the first-term 

participants with an English background and those who do not have an English background. In 

addition, the range of scores on the tests is wider for participants with non-English backgrounds. 

I conclude that this range cannot be captured by language prerequisites pre-admissions. With 

locally developed, post-admission language assessments, diploma-crediting institutes could 

identify students at the lower end of the range and allocate necessary resources to provide them 

with early and individual language support so that they can succeed in their programs.  

 

Background 

 

Canadian post-secondary classrooms are increasingly linguistically diverse. This diversity 

reflects Canada’s already multicultural society. Canada’s pluralistic society alone gives reason 

for multicultural, multinational, and multilingual post-secondary classrooms, filled with domestic 

students who have rich and complex language backgrounds, facilitated by waves of immigration 

over the decades (Fox & Artemeva, 2017). In addition, higher education has become more 

accessible to minority and marginalized groups (Read, 2016). In Canada, for instance, federal 

and provincial governments are funding post-secondary institutes to better support Aboriginal 

students, students in government care, and students with accessibility challenges so they can 

attend and graduate with higher education credentials.1 Finally, international student recruitment 

has grown almost 120% from 2010 to 2017 (ICEF Monitor, 2018). By the end of 2017, almost 

half a million international students were enrolled in institutions and schools across Canada. 

These factors have led to richly diverse language differences in post-secondary classrooms; 

                                                           
1 However, whether these broad support programs can have the right effect is debatable. Fox and Artemeva (2017), 

for instance, have reservations about the generic nature of academic success programs and writing centres. 

Meanwhile, Gallop and Bastien (2016) question the success of programs for Aboriginal students in Canadian post-

secondary institutes. 

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however, this linguistic diversity, which should be treated as a major asset to internationalization 

and globalization, is blanketed by one major issue—the language of instruction is strictly 

English.  

 

As a result, Canadian post-secondary institutes largely require applicants to meet English 

language prerequisites to confirm that they have the language abilities essential to take on the 

demands of higher education in English. Mostly, post-secondary institutes look for academic 

achievements and credentials met at various levels of education or scores reached on global 

standardized tests. These requirements provide evidence of some background of education in 

English; however, education background, such as the number of years a non-native speaker 

spends in an English-speaking high school or studying for a standardized test, may not provide 

enough evidence about stages of language proficiency development.  

 

Read (2016) states that despite second language (L2) students meeting language 

prerequisites, institutes still cannot assume all students entering post-secondary programs are 

“adequately prepared to cope with the language and literacy demands of degree studies through 

the medium of English” (p. 4). Evidence for this can be found, for example, in Fox (2005). Fox 

investigated whether the time L2 students spend in an English-medium high school makes a 

difference in academic achievement in the first years of post-secondary. She found that only 

about 15% of the students who had spent three, four, or five years in an English-medium high 

school were on track to meet minimum post-secondary standards. She concludes, therefore, that 

language residency requirements should be revisited as a prerequisite for post-secondary 

admission.  

 

Furthermore, Murray (2018) points out that many students still struggle with language 

despite meeting entry requirements on standardized tests. He argues that gatekeeping tests such 

as IELTS and TOEFL “are being misused in that entry thresholds are being set too low, or that 

the tests are not really measuring what they need to be measuring or are only measuring part of 

what they need to measure” (p. 6). This may be in part because to achieve desired test scores, 

some students engage in short yet intense bouts of language learning of the four main skills so 

they can successfully achieve minimum bands commonly required for undergraduate study (e.g., 

IELTS 6.0) (Mueller, 2015). However, short and intense test preparation does not foster the 

larger, more holistic task of long-term language proficiency development and preparation for 

post-secondary instruction in English.  

 

Rather, substantial and intense input and output is needed for learners to not only traverse 

through the early stages of acquisition (Buyl & Housen, 2015; Pienemann, 1998), but also to 

allow students to gain linguistic competence through “interaction and engagement with genres 

and registers through which content knowledge is expressed” (Llinares, Morton, & Whittaker, 

2012, p. 17). This gaining of competence includes acquiring the basic vocabulary and grammar 

needed to engage with technical content, spontaneously conversing within the social and learning 

space of a post-secondary institution, expressing complex thoughts and ideas in writing, and 

focusing on advanced levels of academic literacy (Llinares, Morton, & Whittaker, 2012). In sum, 

education background prerequisites such as test scores, years in an English-speaking high school, 

or credit points from a previous post-secondary institute may not be good indicators of the range 

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of language abilities that students bring to the classrooms, nor are they good predictors of 

academic success for post-secondary study.  

 

To understand students’ language abilities in English, it may be beneficial to explore their 

language backgrounds. Research in second language acquisition (SLA) has shown that individual 

learner differences affect the rate and pathway of language development (Lightbown & Spada, 

2013). Variables such as age of initial language acquisition, length of stay in the target language 

country, first language (L1), and language upbringing are all factors that have been rigorously 

explored in language research (Bifuh-Ambe, 2011). For instance, research shows that somewhere 

between the ages of 6 and 17 people slowly lose the ability to learn an L2 implicitly (DeKeyser, 

2000; Hartshorne, Tenenbaum, & Pinker, 2018). The quality and quantity of language input and 

output are important during this “critical period” if native-like proficiency is the ultimate goal. 

Although the number of years spent in a target-language country is not linear to higher levels of 

language proficiency, the length of stay in a target language country can also be a factor in the 

acquisition of Basic Interpersonal Communication Skills (BICS) and Cognitive Academic 

Language Proficiency (CALP) (Cummins, 1981). The length of stay may prepare students in 

their BICS, while their exposure, language training, as well as quality and quantity of input and 

output can prepare students for their CALP, which is considered important for success in 

academic situations (Baker, 2011; Lyster, 2007). The age of initial arrival, motivation, and 

aptitude are also confounding variables that effect increased language proficiency, especially in 

adult learners (DeKeyser, Alfi-Shabtay, & Ravid, 2010). Finally, L1 background and confidence 

in the language of instruction are likely important factors for students’ post-secondary success 

(Bifuh-Ambe, 2011). L1 exposure influences L2 abilities as learners “draw on the patterns of 

other languages they know as they try to discover the complexities of the new language they are 

learning” (Lightbown & Spada, 2013, p. 57), while Telbis, Helgeson, and Kingsbury (2014) note 

that international students who are high in their language confidence are also high in their 

confidence to complete their program. Clément, Baker, and MacIntyre (2003) also suggest that 

language confidence is connected to how willingly and frequently people use the language. 

Understanding language backgrounds may, therefore, provide valuable insights into how to best 

support students who are struggling to meet post-secondary standards. 

 

Local, post-entry language diagnostics may be the institute’s best early indicator of the 

range of first-term students’ language abilities. Research on early alert in higher education 

suggests that student support services should begin within the first three weeks and up to six 

weeks into the semester (Hanover Research, 2014). However, instructors often first notice 

students who are at the most risk of falling behind or failing because of their language skills later 

than this period of time. Support for post-entry diagnostic assessment comes from Fox, 

Haggarty, and Artemeva (2016), who suggest:  

 

The global movement of students, the linguistic and cultural diversity of university 

classrooms, and mounting concerns about retention and program completion have 

prompted the increased use of post-entry diagnostic assessment, which identifies students 

at risk and provides them with academic support. (p. 43) 

 

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It should be made clear that the purpose of post-admission language assessment should be to 

function as a local test that provides an early alert for those students who may be in danger of 

struggling or failing in their programs. Fox, von Randow, and Volkov (2016) agree: 

 

There has been a dramatic increase in the use of post-entry diagnostic assessment to 

identify entering, undergraduate students at-risk and provide concomitant targeted 

supplementary learning support early enough in their initial university studies to prevent 

initial failure and foster academic success. (p. 267) 

 

Ginther and Yan (2017) similarly note that “as entry-level language proficiency requirements 

fall, requirements for post-entry language proficiency testing and enrollment in EAP language 

support programs tend to increase” (p. 278). The diversity of Canadian society and the multiple 

pathways into post-secondary programs suggest that testing all incoming students is necessary.  

 

Read (2016) recommends testing all incoming students because social diversity does not 

provide clear lines between “domestic” and “international” or “native speakers” and “non-native 

speakers,” thus assessing students post-admissions based on these divisions is problematic and 

may be discriminatory. Knoch, Elder, and O’Hagan (2016) additionally argue that as access to 

higher education increases, students who may, based on pre-entry student records, be considered 

“domestic” students from either English or non-English backgrounds “may also be linguistically 

at risk in their academic study—even more so in some cases than their international 

counterparts” (p. 24). Therefore, any local post-admission language testing should include all 

enrolled students, without inclusion or exclusion criteria.  

 

The purpose of the present research project, therefore, was to assess all first-term 

students’ English language proficiency and investigate to what extent polytechnic students’ 

education and language backgrounds affected the differences in their test scores. The goals were 

to explore the diversity of the language and education backgrounds of first-term students and 

determine the range of abilities on four different language tests and if there were significant 

differences between students based on these variables.  

 

Because of the growing diversity and multiple pathways to meet language prerequisites at 

polytechnic institutes, research about the general English language proficiency of first-term 

students post-admission can provide empirical insights into language differences and range of 

abilities. Using a tailored instrument to assess all students regardless of their residency status, 

this study sought to contribute more empirical evidence to discussions around language abilities, 

standards, and thresholds at the institute. The long-term goal of this project was to initiate the 

first phase of a language diagnostic and support model so students who may struggle 

academically can be identified early and properly assisted during the course of their studies.  

 

The research questions addressed in the present study are:  

 

1. How diverse are first-term students at a polytechnic institute in their language and 

education backgrounds?  

2. What differences exist in general English reading, writing, and vocabulary between 

groups of first-term students based on their language and education backgrounds?   

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Data collection took place in the first two weeks of September 2018, during the students’ 

orientation week. The institute’s Research Ethics Board also approved this study in October 

2017.  

 

The Study 

 

Setting 

 

The polytechnic institute where this study took place is on the west coast of Canada. In 2016-

2017, 18,755 full-time day school students were enrolled in its programs, with 15,039 students 

enrolled at the campus where the research took place. Full-time students are registered in four 

different areas: technology, trades and technical studies, technology degrees, or apprenticeships.  

 

Four programs from four different schools participated in the study: Financial 

Management (FinMan), which includes Finance, Financial Planning, and Accounting, as well as 

Risk and Insurance Management; Electrical and Computer Engineering Technology (ECET); 

Architectural and Building Technology (ABT); and Computer Systems Technology (CST). 

These programs were chosen because they are relatively large programs (their first-term 

population size was about 770 students), they represent four different areas of study, and they 

had a representative overall percentage of international enrolment (18%).2  

 

In 2016-2017, 1,092 international students had enrolled in full-time programs at the 

institute. Fifty-two percent of the international students study in the School of Business, 11% in 

the School of Computing & Academic Studies, and 16% in the School of Construction & the 

Environment. In 2017, 51% of the first-term students had enrolled at the institute within one year 

of high school graduation.  

 

Participants 

 

This study recruited from all first-term students in the four abovementioned programs. That is, 

there were no inclusion or exclusion criteria. For the purpose of the study, I avoided tagging 

participants with preconceived labels of residency status, language background, prerequisites 

met, and so on. This was based on the assumption that student records can only provide limited 

information about factors that influence students’ language abilities and because there are no 

clear lines between ‘domestic’ and ‘international’ in terms of their language learning levels or 

abilities.   

 

Methods 

 

This study took a descriptive, cross-sectional approach to investigate the proficiency of first-term 

students after they had been admitted into a program. Following a psychometric tradition, this 

                                                           
2 The population size and percentage of international students are based on the target numbers by program from 

August 2017. Eighteen percent international student enrolment is 2% above the average total of international first-

term enrollment and 1% above province-wide estimation of international students in post-secondary schools 

(Statistics Canada, 2016). However, due to estimated growth across the province and at the institute, 18% may now 

be below these averages.  

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quantitative study aimed to test the influence of different variables on language test scores 

(Nunan & Bailey, 2009). That is, this study sought to investigate whether language background 

or past education had a stronger impact on the language test scores of first-term students at a 

polytechnic institute.   

 

Instruments 

 

To survey and test the general English proficiency of the study participants, an online test and 

survey was created and hosted by the institute’s Learning Management System (LMS). This 

software provided quiz functions that were combined to create a four-part general language 

proficiency test. The test included four areas: reading (30 cloze items), writing (two open tasks), 

vocabulary (100 multiple-choice items), and grammar (24 correct/incorrect items).3 Participants 

were allowed 15 minutes for reading, 45 minutes for writing, 30 minutes of vocabulary, and five 

minutes for grammar. In addition, a 15-item survey was embedded as a SurveyMonkey® 

weblink at the end of the test to collect data on the participants’ language and education 

backgrounds.  

 

 Survey. Participants completed a 15-item survey asking them about their language and 

education backgrounds. This survey helped answer the first research question: “How diverse are 

first-term students in their language and education backgrounds?”  

 

 Reading test. To test reading comprehension, tailored cloze tests were developed for 

each of the individual programs. Tailored tests for each program ensured that the reading 

passages were at participants’ expected proficiency and maturity levels, as well as of interest to 

them (Brown, 2014; Grabe, 2009). Cloze tests for measuring reading ability have been used for 

both native speakers and non-native speakers and are able to measure reading abilities on both a 

sentential and intersentential level, overall meaning, or grammatical sensitivity, depending on 

which words have been deleted from the passage (Alderson, 2000; Trace, Brown, Janssen, & 

Kozhevnikova, 2017; Brown, 1993). Developing the tests consisted of following Brown’s (2013) 

steps for developing tailored cloze tests and piloting five different versions with first-term 

students the previous semester. Item analyses of each version using classic test theory helped to 

identify the best functioning items. That is, items that showed the best item facility and 

discrimination for the certain blank in the passage were kept. The chosen blanks included 

between 30-40% content words and 60-70% function words in each test (Chae & Shin, 2015). 

Furthermore, a Tukey comparison of means showed no significant difference between the final 

versions of the tests in their development. The final versions for each of the four programs 

contained 30 items. Items were analyzed for internal reliability using both classic test theory and 

item response theory (IRT). Guttman’s L2 showed ≥0.7, while an item analysis using Rasch 

modelling showed item reliability scores of ≥0.93.4  

 

 Writing tasks. Two constructed-response writing tasks were developed by a focus group 

of faculty members based on the idea of functional adequacy (Kuiken & Vedder, 2016). Kuiken 

                                                           
3 Implicit grammar knowledge was also originally tested, but the test was low in inner reliability (KR-20 < .40) and 

will not be discussed in this paper.   
4 For validation of the reading tests, reading scores were correlated with VST scores, indicating a significant 

relationship (r = 0.595, p < .001).  

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and Vedder (2016) define functional adequacy as the successful fulfillment of the task as 

interpreted by the reader. They understand “functionally adequate” as being dependent on the 

task the writer has been given and how well the reader has received the writer’s message. In their 

words, “the adequacy of the message transmitted by writer A is understood and interpreted by 

reader B with respect to the quantity, relation, manner, and quality of the message by A” (Kuiken 

& Vedder, 2016, p. 323). Therefore, the emphasis is not on language use per se, but rather on the 

function of the written message. This approach aligned with first-term expectations at the 

institute as students’ writing tasks are largely technical, business, or workplace writing 

assignments, which focus on “getting the job done” and “clear and concise communication.” To 

rate these two tasks, the focus group amended Kuiken and Vedder’s (2016) analytic rating scale 

to include six elements that were scored between 0 (incomplete) to 5 (very good).5 By using a 

multiple trait rating scale, I sought to collect more detailed information for each of the features of 

the tasks (Eckes, Mueller-Karabil, & Zimmerman, 2016; Fulcher & Davidson, 2007).  

 

To reduce the risk of drawing general conclusions about the writing abilities of the 

respondents, two authentic writing tasks from different genres were developed (Eckes, Mueller-

Karabil, & Zimmerman, 2016). The first task was a persuasive writing task that involved writing 

a blog post to peers about balancing a healthy lifestyle while engaging in the rigours of post-

secondary study. The situation was a real-world scenario based on the institute’s own blog that 

allows students to share their experiences with their peers. Expectations for the blog were 

collected from the blog’s editor at the institute, making the task feel as “real world” as possible. 

This task required participants to write at least 250 words in 25 minutes. The second task was an 

informational task and asked participants to write an email to a fictitious boss, describing and 

commenting on two charts that presented the gender pay gap in Canada and western provinces. 

These charts were created based on numbers from a 2017 Statistics Canada report (Statistics 

Canada, 2017). Participants were asked to write at least 150 words in 20 minutes.  

 

To score the written responses, four non-language expert raters were hired. Non-language 

expert raters were chosen because of the focus on the functional adequacy and not language use 

per se. According to Kuiken and Vedder (2014), language expert raters may rate writing samples 

that focus on functional adequacy too hard or too easy. In addition, to reduce negative rater 

effects outlined by Eckes, Mueller-Karabil, and Zimmerman (2016), such as leniency, central 

tendency, halo, or rater bias, raters were trained over a four-week period using piloted responses 

and the rating scales. During the marking process, efforts were taken to ensure that responses 

contained no identifying, biographical information. Scoring of the final responses involved a 

three-step process:  

 

1. Two raters and the principal researcher followed a “divide and conquer” approach (J. 

Fox, personal communication, August 8, 2018). Groups of writing samples were 

categorized into: at-risk, average, and excellent. This was done holistically as a sorting 

and ranking procedure (Fulcher & Davidson, 2007). If any responses were not agreed 

upon, then these were openly discussed until 100% interrater agreement was met.  

2. Two raters individually scored each response using the functional adequacy rating scale.  

3. The parity model was used to operationalize the scores (Johnson, Penny, Gordon, & 

Fisher, 2005).   

                                                           
5 The rating scale for the persuasive task can be found in Appendix A. 

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Spearman was applied to measure how close the raters ranked the written responses. The 

results showed high levels of reliability between the raters (≥ 0.8, p < .05).    

 

Vocabulary size. To test vocabulary size, Nation’s monolingual Vocabulary Size Test 

(VST) Version A was used. This test version is available on his website and can be used for 

testing and research purposes (https://www.victoria.ac.nz/lals/about/staff/paul-nation#vocab-

tests). The VST can be used to measure both L1 and L2 speakers’ written receptive knowledge 

(Elgort & Coxhead, 2016). The VST contains 100 multiple-choice questions that include the 

presentation of the word (i.e., the stem), plus the word used in a sentence so that the test-taker 

knows how the word can be used in context; this is followed by four possible answers, one 

correct and three distractors (Coxhead, Nation, & Sim, 2015). A reliability analysis of the 100 

items showed Cronbach’s 0.91 and McDonald’s 0.91. 

 

Criticism of this test includes its development from corpora that may not be ideal for 

developing learners (Pinchbeck, 2016). In addition, the word frequencies that the test is based on 

come from the BNC / COCA, so some words may not be familiar to a Canadian test-taker. Also, 

the word frequencies do not provide a good measure of word difficulty, as high frequency words 

may be more difficult than low frequency words. Finally, because the VST is a test of written 

receptive vocabulary size, it can only give us a rough estimate about the test-taker’s reading 

skills. It also does not tell us if or how well a test-taker could use these words in speaking or 

writing (Nation & Beglar, 2007). Further testing and development is undergoing to address these 

issues for future projects.  

 

Analysis 

 

The survey results were collected in SurveyMonkey® and exported as MS Excel files for 

analysis. The survey results were analyzed for frequencies and proportions. The reading and 

vocabulary parts of the test were collected and scored automatically by the LMS, whereas the 

writing samples were extracted from the LMS to be scored by the raters. All the test results were 

compiled in MS Excel tables for data validation, clean up, and filtering. Once these tables were 

completed, test results were entered into Minitab 18 for descriptive and inferential statistical 

analyses and WINSTEPS for Rasch analysis.  

 

 To compare differences of means between two factors, independent samples t tests were 

run. To compare differences of multiple means, one-way ANOVA was applied. When 

assumptions for parametric tests to compare means were not met, nonparametric equivalent tests 

were used. Either Cohen’s d after t tests or omega squared () after ANOVA were used to test 

the strength of association between the variables. Omega squared is considered by some as a 

more conservative measure than partial eta squared (ŋ2) when measuring effect sizes for 

ANOVA (Grace-Martin, 2019). For t tests, Plonsky and Oswald’s (2014) suggestion for 

interpreting the between group effect size of Cohen’s d in language testing research were used: 

0.40 (small effect), 0.70 (medium effect), 1.00 (large effect).  

 

To explain the range of test scores, the interquartile range (IQR) and standard deviations 

(SD) were analyzed. The IQR represents the middle 50% of the collected data and shows a larger 

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or smaller range in test scores relative to its size. Additionally, the SD explains to what extent the 

scores of a group differ on average from the central tendency (i.e., the average) (Green, 2013).  

 

Results 

 

Survey Results 

 

In total, information from 437 participants was collected through a survey. The number of 

participants from each program is displayed in Table 1.  

 

Table 1  

Participants by Program 

Program n 

Financial Management 199 

Architectural and Building Technology 98 

Electrical and Computer Engineering Technology 72 

Computer Systems Technology 68 

 

 Language background. To answer the first research question, participants were asked 

about their language and education backgrounds. 

 

Language confidence. The survey asked participants to choose which language they 

were most confident in at the time of the testing. Survey questions that ask students about their 

first language, native language, or mother tongue can lead to confusion or misunderstanding. 

Therefore, by asking participants to choose the language they felt most confident using, I hoped 

to mitigate misunderstandings of first, second, native, and mother tongue language acquisition 

because I had no opportunity to follow up on the responses (Dornyei, 2003).  

 

Seventy-three percent of the participants (n = 319) responded that they were most 

confident using English, whereas 27% (n = 118) responded that they were most confident in a 

language other than English. The survey uncovered 22 different languages in which participants 

were most confident. The top four most frequent languages other than English included 

Mandarin (25%), Korean (22%), Vietnamese (13%), and Cantonese (7%).  

 

Age of initial English acquisition and language upbringing. To get an idea of language 

upbringing, participants were surveyed on whether their parents or guardians had spoken English 

with them since infancy and at what age they started learning English. The survey results showed 

that 55% of the participants had spoken English to at least one of their parents or guardians since 

they were babies, while 40% said that they had not. The remaining 5% responded with “I don’t 

know.” In addition, 62% of the participants began learning English between birth and five years 

old, 32% started learning English between 6 and 17 years old, and the remaining 5% began 

learning English after the age of 17.   

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Number of years in Canada. The survey asked participants for how many years they had 

lived in Canada. The majority of the respondents had spent 11 or more years in Canada (65%). 

Eleven percent had spent between 5 and 10 years in Canada. Meanwhile, 24% of the respondents 

had spent 4 or fewer years in Canada.  

 

 Education background. Participants were also asked about their education background. 

Three areas were of particular interest because they related to different pathways students can 

take to gain entry into the institute: taking a standardized language test, years spent in an 

English-medium high school, and previous attendance at a post-secondary institute.   

 

Standardized language tests. Most of the participants (75%) had not taken a standardized 

language test (e.g., TOEFL, CAEL, IELTS, PTE, etc.). Of the 17% (n = 75) who had written a 

standardized language test, 55% had written an IELTS test, 7% had written TOEFL (iBT), and 

another 7% had written CAEL. The remaining participants fell in the category “other” because 

they specified English 12 or Provincial 12 as being a standardized language test. This group was 

not considered in subsequent analyses. A follow-up question for participants who had taken a 

test was how long ago it had been since they had written the test. Sixty-seven percent said it was 

less than one year ago, 29% said they had taken the test 1-2 years ago, and 4% had taken it 3-6 

years ago.  

 

Years in English-speaking high school. When asked about how much time they had 

spent at an English-speaking high school, 39% of the participants (n = 173) had spent five or 

more years in high school, 26% spent five years in an English-speaking high school, and 19% 

spent 3–4 years in an English-speaking high school. On the other hand, 5% spent only 1–2 years 

in an English-speaking high school and 10% spent less than one year or no time at an English-

speaking high school. In addition, when asked about where they received their English 12 

prerequisites to gain entry to the institute, 69% said from a public high school in the province. 

Private high schools represented 7% of the responses, while international colleges within the 

province received 4% of the responses. Institutions outside the province, either public, private, or 

international, made up 6% of the total responses. The remaining 15% of the respondents 

responded with either “none” or “other.”  

 

Previous post-secondary experience. A possible language prerequisite at the institute 

includes transferring credits from language-related courses (e.g., Anthropology, Law, English 

Literature, Psychology, etc.) taken at other post-secondary schools; therefore, the survey asked 

participants about whether they had previous experience as post-secondary institutes. Almost 

half (49%) of the participants had not previously attended a post-secondary institute, whereas 

44% had attended one post-secondary institute before taking the survey. Six percent of the 

respondents had attended two post-secondary institutes and the remaining 1% had attended three 

prior to starting at the institute.  

 

Of those participants who had previously attended post-secondary (n = 257), 30% had 

been to the two largest universities in the province, and 55% had been to other institutes across 

the province. Another 9% had attended post-secondary elsewhere in Canada, and 6% had been to 

post-secondary outside of Canada.  

 

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Test Results 

 

The following sections present the results of the tests organized by the variables identified in the 

survey. These results are divided into two main categories: language background variables and 

education background variables. The categories were created to answer the second research 

question: “What differences exist in reading, writing, and vocabulary proficiency between 

groups of first-term students based on their language and education backgrounds?” The results 

are presented in percentages and in mean scores for each of the three tests.  

 

Age of initial English acquisition and language upbringing. In this section, two factors 

related to language upbringing will be presented: Having English spoken to the participants since 

infancy and age of initial English acquisition. The reason for looking at the latter as a variable 

comes from research that suggests age of initial acquisition is a robust factor in how L2s are 

learned. Figure 1 illustrates the results when looking at the first factor: the age of initial English 

acquisition.  

 

 
Figure 1. Comparison of Test Scores by Age of Initial English Acquisition 

 

Figure 1 shows that there is a statistically significant difference between participants who began 

learning English between birth and five years old and the other two groups. Three one-way 

ANOVAs resulted in the following: reading F(2, 409) = 35.69, p < .001; writing F(2, 403) = 

37.90, p < .001; vocabulary F(2, 402) = 68.57, p < .001. However, the Tukey pairwise 

comparison showed that there was no significant difference between the reading scores of the 

after 17-year-old group and the between 6 and 17 year-old group (p > .05). The difference was 

also not as significant between these two groups in the vocabulary scores (p = .03). On the other 

hand, the post hoc comparison showed that there were statistically significant differences 

between all the groups in the writing scores (p < .001).  

 

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 Furthermore, the effect of age of initial English acquisition was the largest between the 

vocabulary scores (= .25), suggesting that participants’ initial age of English acquisition 

impacts their vocabulary size more than the other two measures (Figure 1). However, the effect 

size is still considered large in reading and writing.  

 

 It is of interest to also look at the range of scores within these groups. To do this, I looked 

at the IQR and SD. These often provide a simple yet reliable way to identify which groups 

showed a wider range of results than others. Table 2 illustrates how the IQR is smaller in each of 

the tests for the birth to 5 year-old group, suggesting a tighter range around the average for the 

group. Although this is the largest of the groups (reading, n = 256), the IQR is still smaller than 

the other two groups. As the table shows, the range and IQR are larger for the other two groups, 

suggesting a wider range of scores on the tests.  

 

Table 2  

Interquartile Range and Standard Deviations by Age of Initial Acquisition 

Test Age of Acquisition n M SD IQR 

Reading from birth to 5 years old 256 60.38% 16.66 23.33 

 between 6-17 years old 134 46.94% 19.21 30 

 after 17 years old 22 37.69% 22 30.8 

Writing from birth to 5 years old 255 62.92% 12.38 15 

 between 6-17 years old 132 54.22% 14.62 23.13 

 after 17 years old 19 40% 18.91 35.83 

Vocabulary from birth to 5 years old 251 72.54% 9.4 11 

 between 6-17 years old 132 59.65% 13.21 20 

 after 17 years old 22 53.14% 12.67 17 

 

In particular, the SD in the vocabulary and writing tests jump from the birth to 5 year-old group 

to the between 6 and 17 year-old group. This increase in the SD underscores the different range 

of abilities between these two groups.  

 

 In addition to age of acquisition, I analyzed the test scores between the group of students 

who responded “yes” to having one parent or guardian speak English to them since birth and 

those who responded “no” to this item. The results of this analysis are illustrated in Figure 2.  

 

 Independent samples t tests showed that the differences between the test scores are 

statistically significant: reading t(299) = -6.79, p < .001; writing t(297) = -5.61, p < .001; 

vocabulary t(277) = -9.43, p < .001. Using Cohen’s d to measure the effect sizes, it is again the 

vocabulary scores that were most effected by whether or not the participant had been spoken to 

in English since infancy.  

 

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Figure 2. Comparison of Test Scores by English with Parents 

 

 The “no” group also displayed a larger range of scores in all three tests, suggested by the 

larger IQR and SD. For instance, the IQR of the “yes” group in their vocabulary was only 12.25, 

whereas it was 20 for the “no” group. The writing scores also showed much different IQR, 8.5 

for the “yes” group and 14.5 for the “no” group. The difference between the groups’ SD is also 

displayed in Figure 3.  

 

 
Figure 3. Line Plot of Standard Deviations of Test Scores by English with Parents  

 

Number of years in Canada. In this study, participants were divided into three groups 

based on their number of years in Canada: 11+ years, 5–10 years, and ≤4 years. The test results 

of these three groups are presented in Figure 4.  

 

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Figure 4. Comparison of Test Scores by Number of Years in Canada 

 

The results of the one-way ANOVAs for each test showed a statistically significant difference 

between the 11+ years group and the other two groups in all the tests: reading F(2, 409) = 39.09, 

p < .001, writing F(2, 402) = 25.50, p < .001, vocabulary F(2, 401) = 81.10, p < .001. The post 

hoc pairwise comparison, however, indicated no statistically significant difference between the 

means of the test scores of the other two groups. The probability values for the post hoc t tests 

between these groups were: reading (p = .595), writing (p = .706), and vocabulary (p = .730).   

 

 Looking at the IQR to examine the range of the scores within these groups, we again 

observe wider ranges in the groups that have been in Canada for 10 or fewer years (Figures 5–7). 

The dispersion from the central tendency is the tightest for the vocabulary scores for the 11+ 

with an IQR of 10 versus the 5–10 year group with an IQR of 23.  

 

 
Figure 5. Boxplot of Reading Scores by Number of Years in Canada  

11+ years5-10 years≤4 years

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Figure 6. Boxplot of Writing Scores by Number of Years in Canada 
 

 
Figure 7. Boxplot of Vocabulary Scores by Number of Years in Canada 

 

The SD also tell us about the range of test-takers’ abilities, and the differences between these are 

clearly observed in the line plot found in Figure 8.  

 

 
Figure 8. Line Plot of Standard Deviations by Number of Years in Canada 

 

11+ years5-10 years≤4 years

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In sum, there is a statistically significant difference between participants who had lived in 

Canada for 11+ years and those who had been in Canada for 10 or fewer years. However, there 

was no statistical difference between the average scores if the participant had spent 5–10 years or 

4 or fewer years in Canada. Furthermore, the range of scores is much larger in the 5–10 year 

group and the 4 or fewer group. This again suggests a wider range of abilities within these 

groups of participants.  

 

Language confidence. For the purpose of this study, I considered the question of 

language confidence synonymous to the participant’s preferred L1. I avoided asking participants 

for their L1, native language, or mother tongue because I expected these to be complex and may 

have undergone dynamic change in the participants’ lives. The majority of the participants (56%) 

reported in the survey that they could read and write in two or more languages. This perhaps 

confirms the approach to ask for language confidence because sometimes bi- and trilinguals have 

difficulties deciding which language is their “first” language or “native” language (Baker, 2011).  

 

Asking for language confidence also provides information about how linguistically 

prepared participants feel before begin their post-secondary careers. I compared the scores on the 

language tests between those students who felt most confident using English (the language of 

instruction) against those who reported feeling most confident in languages other than English 

(Figure 9).  

 

 
Figure 9. Comparison of Test Scores by Language Confidence 

 

The results of independent samples t tests also showed the statistically significant difference 

between these two groups: reading t(177) = 9.53, p <.001, vocabulary t(158) = 13.00, p <.001, 

writing t(160) = 7.96, p <.001. As seen in the previous analyses, the largest effect size can be 

found between the vocabulary scores (d = 1.53), which is considered a very large effect size.  

 

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 Similar to the other results, the boxplots found in Figures 10-12 show a clear difference 

in the range of scores between the two groups. The differences in the ranges were most 

noticeable between the vocabulary and writing scores.  

 

 
Figure 10. Boxplot of Reading Scores by Language Confidence 

 

 
Figure 11. Boxplot of Vocabulary by Language Confidence 

 

OtherEnglish

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Figure 12. Boxplot of Vocabulary Scores by Language Confidence 

 

The increased dispersion from the mean is also visible in the line plot displayed in Figure 13.  

 

 
Figure 13. Line Plot of Standard Deviations by Language Confidence 

 

The increase in SD in the vocabulary and writing scores underscore the results shown in the 

above boxplots and provide additional evidence for the range of abilities between the test-takers 

who felt confident in a language other than English.  

 

Education Background 

 

This project also compared the participants’ education backgrounds based on their responses to 

the survey. The following sections present the results of comparisons between: participation in 

standardized language tests or not, number of years in an English-speaking high school, and 

previous attendance at a post-secondary institute. Noteworthy are the smaller effect sizes of these 

variables on the differences between the scores in comparison to the language background 

variables as well as the range differences between groups, which look different from the results 

of the previous sections.  

 

OtherEnglish

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 Standardized language tests. As a possible language prerequisite to attend the 

polytechnic institute, participants can use their scores on a number of accredited global language 

tests such as IELTS, TOEFL, CAEL, and PTE. These tests are considered gatekeeping tests as 

the test taker must achieve a certain minimum score in order to gain entry into an institute and a 

program. The programs in the current study required a minimum of English 12 (67%), which 

was considered equivalent to: IELTS 6.5 (overall score), TOEFL 86 (overall score), CAEL 70 

(overall band score), 60 (minimum on all subsets), PTE 60 (overall score). Only one of the 

participating programs implemented a “competitive entry” process that ranked the averages of 

the applicants’ Math and English scores, taking the most qualified applicants based on this 

ranking. In the following analysis, all the participants in the “yes” group have scored equal to or 

above the minimums on whatever test they took.  

 

 A comparison of the test scores between those participants who had taken a standardized 

test and those who had not can be seen in Figure 14. The results of the t tests to test for 

significance show that the difference is the most substantial between the groups in their 

vocabulary scores: t(106) = 5.36, p < .001. The difference is less significant in reading, t(106) = 

3.22, p < .05, and writing, t(125) = 2.84, p < .05, however. Because of the large difference in the 

group sizes, a nonparametric equivalent of the t test was run (i.e., Mann-Whitney U) and showed 

that the difference between the medians was significant in vocabulary (p < .001) but less 

significant in writing (p = .005) and reading (p = .014). The effect sizes of the reading (d = .23) 

and writing (d = .34) scores are small, while the effect on the vocabulary score (d = .71) is 

medium.  

 

 
Figure 14. Comparison of Test Scores by Participation in Standardized Language Tests 

 

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 An analysis of the ranges of the groups indicates that in particular the SD between the 

groups are closer than the SD in the language background variables (see Table 2, for example). 

These results are displayed in Table 3.  

 

Table 3 

Descriptive Statistics of Language Test Scores by Participation in Standardized Language Tests 

Variable Test n M SD Median Range IQR 

Reading No 311 56.30% 18.65 56.60% 89.9 23.3 

   Yes 68 49.31% 21.1 49.93% 86.57 35.8 

Writing No 302 60.13% 14.29 63.33% 79.17 16.67 

   Yes 67 55.05% 14.15 57.50% 65 21.67 

Vocabulary No 305 68.26% 12.17 70% 72 14.5 

   Yes 68 59.84% 14 61% 55 20.75 

 

The more similar SD suggest that the groups’ average distances do not disperse as far from the 

means as when I looked at the other language background variables. This might suggest that the 

standardized language test variable does not show as clearly the range of abilities as the language 

background variables.  

 

Number of years in English-speaking high school. The English requirements at the 

institute consist of completing two years of full-time education in English in an English-speaking 

high school and English 12. In this study, I undertook two steps while analyzing the participants’ 

test scores and the number of years they had spent in an English-speaking high school. First, I 

used descriptive statistics to calculate the means of all participants based on the number of years 

they had spent at an English-speaking high school. The results are presented in Figure 15.  

 

 
Figure 15. Line Plot of Mean Test Scores by Number of Years in an English-Speaking High 

School 

 

Multiple one-way ANOVAs indicated that the differences between these scores were also 

significantly different (p < .001). The pairwise comparison showed that the differences were 

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most significant between the >5 year and 5-year group and the no time and <1 year group (p ≤ 

.001). An analysis of the SD found the largest dispersion from the means in the <1 year, 1-2 

years, and 3-4 years groups (Figure 16). The higher the SD the larger the range of scores in the 

group. This range in abilities was also reflected in an analysis of the IQR.  

 

 
Figure 16. Line Plot of Standard Deviations by Years in an English-Speaking High School 

 

The IQR were the highest for the 1–2 years (IQR = 33) and 3–4 years (IQR = 37) groups in 

reading, which compared to the 5-years group (IQR = 20). In writing, the no time (IQR = 23) and 

the <1 year (IQR = 25) differed the most from the 5 year group (IQR = 14). Meanwhile, in 

vocabulary, the <1-year group (IQR = 19) and the 3–4 year group (IQR = 18) showed the widest 

range of abilities, which compared to the 5 year group that had an IQR of only 9.  

 

The second analysis of these groups involved looking at the differences in scores between 

non-English background participants who had chosen a language other than English as their most 

confident, had spent 5 or fewer years in an English-speaking high school, and lived in Canada for 

10 or fewer years. To analyze the results from these remaining students, a nonparametric 

equivalent of the one-way ANOVA was used, the Kruskal-Wallis Test. The results presented in 

Table 4 show that there are no significant differences in the test scores regardless of the number 

of years the participant had spent in an English-speaking high school.  

 

Table 4  

Kruskal-Wallis Test of Difference Between Non-English Background Participants Based on 

Number of Years in an English-Speaking High School 

Test df H p 

Reading (n = 92) 4 2.60 .626 

Writing (n = 88) 4 5.46 .243 

Vocabulary (n = 81) 4 5.93 .204 

 

These findings support the abovementioned study by Fox (2005) in which she 

investigated the academic performance of L2 students in a Canadian university based on the 

amount of time they had attended English-medium high schools. She found that there was no 

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significant difference in academic performance between L2 students who had spent three, four, 

or five years at English-medium high schools. In this study, I only investigate the performance 

on language tests based on years in an English-speaking high school; however, the results are 

similar in that they suggest that the increased number of years do not improve participants’ 

scores on the language tests.  

 

Previous post-secondary experience. The final analysis of education background 

included whether or not participants had previously attended a post-secondary institute before 

beginning their program at the polytechnic. A possible language prerequisite for participants 

includes them showing the successful completion of three credits in an English, humanities, or 

social science related course. The applicant must receive the equivalent of 67% in that course 

when the program requirement is 67% in English 12. In the analysis, I looked at those students 

who had previously attended a post-secondary and those who had not. The null hypothesis was 

that there would be no statistically significant difference between the test scores. The results of 

this analysis are displayed in Figure 17.  

 

 The independent samples t tests showed that, for the reading and vocabulary tests, I had 

to reject the null hypothesis and accept the alternative hypothesis. That is, there were statistically 

significant differences between the reading and vocabulary test scores based on whether or not 

the participant had previously attended a post-secondary institution. The results of the 

independent samples t tests were as follows: reading t(383) = -5.34, p < .001; vocabulary t(383), 

p < .001. However, I accepted the null hypothesis for the writing test scores, as the difference 

was not statistically significant: writing t(380) = -1.63, p = .104. Comparing to the previous 

variables, the effect sizes for each of these tests are much smaller, suggesting that previous post-

secondary experience has a relatively small effect on the differences between the scores.  

 

 
Figure 17. Comparison of Test Scores by Previous Post-secondary Experience 

 

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 Regarding the range of scores in the test between the groups, the SD show minimal 

difference, as illustrated in Figure 18.  

 

 
Figure 18. Line Plot of Standard Deviations by Previous Post-secondary Experience 

 

The IQR of the groups also do not display the same differences that were observed when looking 

at language background variables. For example, the IQR for the reading scores was 26.63 for “no 

previous attendance” and 25 for “yes previous attendance.” The largest difference between IQR 

was in the writing scores, with the “no previous attendance” (IQR = 20.52) and “yes previous 

attendance” (IQR = 17.92). 

 

Discussion 

 

The first-term participants in the current study are linguistically diverse and have complex 

language backgrounds, making it difficult to use dichotomous labels such as “international” or 

“domestic” to define who the English language learners (ELLs)6 are and who are not. Most 

institutes use residency status to define “international” versus “domestic” students. However, the 

participants’ age of initial English acquisition, whether or not their parents had spoken English 

with them since infancy, their length of stay, years in an English-speaking high school, and their 

language confidence are all complexities that are not captured by this status. Many participants 

in this study may have been defined at an admissions level as “domestic” students. In fact, the 

target average international enrolment for the participating programs was 18%, but the 

percentage of participants who identified a language other than English as their most confident 

was 27%. When looking at their language test results, some of these students could, therefore, be 

considered ELLs and benefit from additional language support. This underscores the notion that 

there are no clear lines between “international” and “English language learner,” so these terms 

cannot be used synonymously.    

 

 The number of languages (n = 22) uncovered in this study is also noteworthy. From 118 

participants, 21 languages other than English were identified. For a traditionally technical and 

trades institute, this language diversity is comparable to that of larger universities in Canada. For 

                                                           
6 A definition of ELLs is: “[S]tudents who are learning the language of instruction at the same time as they are 

learning the curriculum and developing a full range of literacy skills” (Ontario Ministry of Education, 2009, p. 2).  

 

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instance, Fox (2005) reported 43 different languages other than English in first-year programs in 

a study of 265 participants at Carleton University, while McDonough, Neumann, and Hubert-

Smith (2018) cited 30 different languages in a study of 409 participants in an EAP program at 

Concordia University. Having a linguistically heterogeneous student population is, on the one 

hand, a positive indication of internationalization and globalization that adds value to the 

diversification of the institute; on the other hand, it can be a daunting task for educators as each 

student with a different language background other than English has a different trajectory in their 

language progress as L1 transfer helps or hinders their development at different stages (Ellis, 

2008; Jarvis, 2015). For instructors who know little about language development, stark language 

differences between their students may be confusing or frustrating. Educating content instructors 

through professional development workshops about language development and individual 

learner differences may help stem these frustrations from being projected back on the language 

learner.  

 

The second research question in this study was about whether or not there were 

differences between groups of students on the tests. The findings suggest that there are 

statistically significant differences between students using a variety of language and education 

background variables. Naturally, limitations to the testing instruments exist as they only tested a 

small number of subskills and the test did not include important skills such as speaking and 

listening. Also, the sample group was self-selected because participation in the study was 

voluntary, meaning any particular group may be underrepresented (Dornyei, 2003). Responses 

about demographic information could also not be corroborated by admissions because the 

institute does not collect data on the language backgrounds of its students (Golder, Reeder, & 

Fleming, 2011). Nevertheless, the purpose of the project was to provide some empirical evidence 

about first-term students’ language proficiency, in part, to counteract or confirm anecdotal 

evidence at the institute about students’ abilities to use English. For instance, I did confirm that 

there are significant differences between students who are confident in English and those who 

are confident in another language, but there were no differences between the foreign languages 

themselves.   

 

It may also come as a surprise to some that a post-secondary student who has 

successfully gained entry into a program and spent 5–10 years living in Canada might still score 

at the same level on a language test as a student who has spent 4 or fewer years in Canada. 

Because language learning is not a perfectly linear process but rather a winding journey that is 

psychological, cognitive, and social, the pathway to language learning may come easy for some 

but be seemingly never-ending for others. The correlation between length of stay in a country 

and language development is not necessarily a strong one (Bifuh-Ambe, 2011). Knowing that the 

acquisition of BICS takes two to three years, and CALPs takes five to seven years to acquire 

(Cummins, 1981; Roessingh, 2006), then some lower level students will likely not get close to 

the language levels of their native-speaking counterparts during their two-year diploma 

programs. Unlike longer degree programs, any language gaps become particularly magnified 

during intense two-year diploma programs where there is little opportunity for students to focus 

on the form and function of language, which is required if language development is to take place 

in the context of purposeful learning (Lucas, Villegas, & Freedson, 2008; Lightbown & Spada, 

2013). 

 

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The results also suggest that language prerequisites cannot capture the range of language 

abilities students bring to their first-term programs. In the present study, this became obvious 

when looking at the language background variables yet became weaker when looking at the 

variables related to language prerequisites. This suggests that the differences that instructors 

experience in the classroom can be better explained by the students’ language upbringing, age of 

initial English acquisition, language confidence, and length of stay than by whether or not they 

fulfilled language prerequisites or their status as “international” or “domestic” students. For 

example, large global proficiency tests may provide information about general academic 

proficiency, but as they are first-past-the-post measures, especially when only overall scores and 

not subskills are considered, they only inform admissions about applicants’ levels of general 

academic proficiency. In fact, using tests that have prepared students for general academic 

proficiency may be even less adequate measures for technical institutes where business, 

technical, and other workplace writing skills are required. Knoch and Macqueen (2017) posit:  

 

It could therefore be argued that these tests [IELTS and TOEFL] are only suitable to 

make predictions about university study but not beyond to possible performance in the 

workplace. Any use of tests beyond the purpose they were designed for casts serious 

doubt on the validity of the score-based interpretations made based on the assessment. (p. 

294) 

 

This argument is underscored by the range of abilities found in the writing and reading tests, 

which were tailored to the expectations for workplace communication. In sum, technical and 

trades institutes should have a special interest in developing local tests that reflect the actual 

workplace language needs of the students (Knoch & Macqueen, 2017). 

 

Looking forward, groups who had English backgrounds had smaller SD and IQR on the 

tests, suggesting that their scores clustered more tightly around the average. On the other hand, 

groups that had non-English background showed much higher SD and IQR in reading, writing, 

and vocabulary, suggesting that they had a much wider range of abilities. As a result, a small 

portion of this latter group fell far below the overall group averages. When I calculated the 

overall proportion of students who fell one SD or more below the group average on each of the 

tests, I identified about 14% (n = 70) in the vocabulary test, 19% (n = 90) in the reading tests, 

and 17% (n = 75) in the writing test. A follow-up study is underway to investigate whether or not 

these groups are actually at risk of failing, dropping out, or repeating in their programs. The 

range of scores highlights the need to look at students’ language abilities with more local tests 

that are tailored to the expectations of the institute.  

 

Implications 

 

Students who do not have an English upbringing are expected to meet language prerequisites 

before entering post-secondary institutes in Canada. Many students invest resources into meeting 

these prerequisites, and they, and the institutes, believe they are starting their studies with the 

linguistic competency needed to succeed. However, there are statistically significant differences 

in English language proficiency between students with an English background and those who do 

not. As a result, some students in this study lagged far behind their English-speaking peers in 

writing, vocabulary, and reading skills. Therefore, if institutes could improve local, post-

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admission assessment screening and diagnostic tools to identify these students both fairly and 

early, then essential resources could be properly allocated to them and they can receive the 

support they need to succeed academically. Regardless of the accuracy and specificity of locally 

created diagnostic tools, any language support model must be directly coupled with the specific 

needs of the learners. A precise and individual support model may be worth pursuing. For 

instance, similar to “precision medicine” in healthcare (Timmerman, 2013), a “precision 

language education” model could seek to customize language teaching to individual students in 

short yet intense diploma programs. This model could include options of: small-group tutorials, 

one-on-one advising, online modules, peer learning, and others. The selection of these methods 

would be based on not only the students’ language learning profile, including consideration of 

their language and cultural background, but also on the needs of their specific programs (e.g., 

ESP and ESAP). Doing this might vastly improve ELL’s success and learning experience in 

diploma programs.  

 

Acknowledgements 

 

This project was supported by BCIT’s Institute Research Funds and the Professional 

Development committee in SoCAS. I would like to thank the research assistants, Jessica Wang, 

Katie Conconi, and Serianna Schroh, for their tireless efforts. Also, I would like to acknowledge 

all my great colleagues who graciously volunteered their time.  

 

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Appendix A: Rating Scale: Persuasive Task (Adapted from Kuiken & Vedder, 2016) 

 

Content: Is the number of information units provided in the text adequate and are they relevant? 

0 1 2 3 4 5 

Zero relevant 

information 
units are included 

or they’re 

included but have 

no connection to 
healthy living. 

Only 1-2 

somewhat 

relevant 

information 

units are included 
and the examples 

are somewhat 

relevant. 

1 information 
unit and the 

examples have to 

do with the 

healthy living. 

2 different 

information 
units are included 

and the examples 

have to do with 

the healthy 
living. 

3 different 

information units 
are included in the 

response and all the 

examples have to 

do with the 

healthy living.  

More than 3 

different 

information 
units are included 

in the response 

and all the 

examples have to 

do with healthy 

living. 

 

Persuasion: To what extent has the message been persuasive? 

0 1 2 3 4 5 

The message is 

not persuasive.  

 

The reader is not 
convinced by any 

of the writer’s 

suggestions for a 

healthy lifestyle.  

The message is 

barely 

persuasive.  
 

The reader is only 

just convinced 

that any of the 
writer’s 

suggestions for a 

healthy lifestyle 

would be helpful.  

The message is 

somewhat 

persuasive.  
 

The reader is 

somewhat 

convinced by 
some of the 

writer’s 

suggestions for a 

healthy lifestyle.  

The message is 

mostly 

persuasive.  
 

The reader is 

mostly 

convinced by 
some of the 

writer’s 

suggestions for a 

healthy lifestyle.  

The message is 

persuasive.  

 

The reader is 
convinced by the 

writer’s 

suggestions for a 

healthy lifestyle.  

The message is 

very persuasive.  

 

The reader is 

completely 
convinced by all 

of the writer’s 

suggestions for a 

healthy lifestyle.  
 

 

Genre: How many elements of a blog post has the author used to make it successful? 

0 1 2 3 4 5 

The response 

reads like 
anything but a 

blog post (e.g., 

academic writing, 

high school essay, 
text message, 

etc.) 

The response 

doesn’t really 

read like a blog 
post to peers 

because it doesn’t 

include any of 

the following 
elements: (1) 

speaks from the 

writer’s point of 

view, (2) shares 
personal life 

events, (3) talks 

about your day-

to-day activities, 
(4) invites 

comments from 

others.  

The response 

reads somewhat 
like a blog post to 

peers because it 

includes similar 

elements to: (1) 

speaks from the 

writer’s point of 

view, (2) shares 
personal life 

events, (3) talks 

about your day-

to-day activities, 
(4) invites 

comments from 

others.  

 

The response 

reads like a blog 

post to peers 
because it 

includes one or 

two elements of: 

(1) speaks from 

the writer’s point 

of view, (2) 
shares personal 

life events, (3) 

talks about your 

day-to-day 
activities, (4) 

invites comments 

from others.  

 

The response reads 

like a blog post to 

peers because it 

includes three 

elements of: (1) 

speaks from the 
writer’s point of 

view, (2) shares 

personal life 

events, (3) talks 
about your day-to-

day activities, (4) 

invites comments 

from others.  
 

 

The response 
really reads like a 

blog post to 

peers because it 

includes all four 

elements: (1) 

speaks from the 

writer’s point of 

view, (2) shares 
personal life 

events, (3) talks 

about your day-

to-day activities, 
(4) invites 

comments from 

others.  

 

 

  

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Tone: How well does the message match the feeling that’s required for a blog post?  

0 1 2 3 4 5 

The blog post is 

far from feeling 
honest, positive, 

and/or not 

appropriate for 
0000’s website.  

The blog post 

somehow doesn’t 
feel honest, 

positive, and/or 

not suitable for 

0000’s website. 

The blog post 
feels somewhat 

honest, positive, 

and not suitable 

for 0000’s 
website. 

The blog post 

feels mostly 
honest, positive, 

and suitable for 

0000’s website. 

The blog post feels 

honest, positive, 

and professional 

for 0000’s website. 

The blog post 

feels very honest, 
positive, and 

professional for 

0000’s website. 

 
Comprehensibility: How much effort is required to understand the text’s purpose and ideas? 

0 1 2 3 4 5 

The response is 

not at all 

comprehensible. 

 

Ideas are 
unclearly stated 

and you can’t 

understand large 

parts despite your 
efforts. 

The response is 

barely 

comprehensible. 
 

The message is 

not clear and you 

struggle to 

understand the 

writer’s ideas.  

The response is 

somewhat 

comprehensible. 

 

Multiple parts 

are hard to 

understand at a 

first reading 
because of 

sentence 

structure, 

grammar, and/or 
spelling errors. 

The response is 

comprehensible. 

 

Some parts 

might need to be 

read twice 
because of 

sentence 

structure, 

grammar, and/or 
spelling errors 

 

The response is 

easily 

comprehensible 
and reads fluently. 

 
Some parts may be 

unclear but are 

understood 

without too much 
effort. 

The response is 

very easily 

comprehensible 
and interesting to 

read.  

 
You only need to 

read the message 

once and do not 

need to reread 
any sentences for 

understanding.  

 

Cohesion: How well has the author linked ideas together using a mix of phrases and words? 

0 1 2 3 4 5 

The message is 

not cohesive and 

ideas are not 

linked together 

at all.  

 

No transitional 

phrases, words, or 
conjunctions are 

used, 

and ideas seem 

completely 
unrelated. 

The message is 

barely cohesive 

and only uses 

simple transition 

words to link 

ideas together 

(e.g., “and” , 

“but”) 
 

Ideas are not 

well linked by 
transitional 

words, phrases, or 

conjunctions; 

these are rarely 
used. 

The message is 
somewhat 

cohesive and 

links a couple of 

sentences in the 

blog post.  
 

Some transitional 

words are used, 
but they are 

mostly 
conjunctions. 

The message is 

cohesive and 

links a few 

sentences in the 

blog post. 
 

It is limited in its 
use of transitional 

phrases, 

parallelism, 

transitional 
words, 

conjunctions, and 

sequence words 

to connect 
sentences. 

The message is 

cohesive and links 

sentences together 

in parts of the 

blog post.  
 

It uses transitional 

phrases, 
parallelism, 

transitional words, 

conjunctions, and 

sequence words to 
connect sentences. 

The message is 

very cohesive by 

linking sentences 

and paragraphs 
together 

throughout the 

whole blog post.  

 

It uses any 

number of 

transitional 
phrases, 

parallelism, 

transitional 

words, 
conjunctions, and 

sequence words to 

connect 

sentences. 

 

 
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