Do hours matter? The relationship between motivation and study intensity in learners of English in Scotland

The Social Science Journal 45 (2008) 457–475

Available online at www.sciencedirect.com

Do hours matter? The relationship between motivation andstudy intensity in learners of English in Scotland

Catherine Rice a,∗, Chihiro Udagawa b, Hilary Thomson c, Neil McGregor a

a Dundee Business School, University of Abertay Dundee, Bell Street, Dundee DD4 7JL, UKb Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge CB3 9EP, UK

c Research and Development, Housing Department, Perth & Kinross Council, 2 High Street, Perth PH1 5PH, UK

Abstract

This paper examines the impact of English language learners’ motivation on their choice of studyintensity using data from a national survey of learners in publicly funded English for Speakers of OtherLanguages (ESOL) courses across Scotland. In particular, the motivation of learners attending only afew hours per week (i.e. very part-time learners) is compared with those attending for more hours. Ourstudy found that there are differences in motivation and aspiration amongst very part-time and part-timeor full-time learners but also that employment and child care may have as much influence as motivationin causing learners to enrol on very part-time English classes. The findings relating to these learners inour study present a particular challenge to ESOL providers which offer only very part-time courses.© 2008 Western Social Science Association. Published by Elsevier Inc. All rights reserved.

1. Introduction

The demand for second language (L2) instruction for migrants continues to rise in devel-oped countries as migration rates rise. Low L2 proficiency impacts severely on employmentopportunities and earning capacities, as well as on the ability to settle comfortably in thesecond language community (see Chiswick & Miller, 2001; Chiswick, Lee, & Miller, 2002;Stolzenberg & Tienda, 1997). Dustmann, Fabbri, Preston, Wadsworth (2003) have calculatedfor the UK that lack of proficiency in English keeps wages 18–20% lower, and employmentchances 15–17% lower.1 For asylum-seekers and refugees, the consequences are even moresevere: social marginalisation and isolation from labour markets follow from low languageproficiency. (Bloch, 2000; Cole & Robinson, 2003).

∗ Corresponding author. Tel.: +44 1382 308965; fax: +44 1382 308400.E-mail address: [email protected] (C. Rice).

0362-3319/$ – see front matter © 2008 Western Social Science Association. Published by Elsevier Inc. All rights reserved.doi:10.1016/j.soscij.2008.07.009

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458 C. Rice et al. / The Social Science Journal 45 (2008) 457–475

Recent studies of refugees in Britain and Europe (Bloch, 2000; European Training andResearch Centre for Human Rights and Democracy, 2004) have uncovered the frustrationof many non-native speakers (NNSs) at the shortage of opportunities for formal instruc-tion in their second language. Publicly funded language provision for both refugees andmigrants varies widely across Europe, depending both on political and on economic con-ditions.

In the UK, a huge increase in asylum-seekers prompted a recent overhaul of English forSpeakers of Other Languages (ESOL) provision in England and Wales and led to signifi-cant increases in national public funding of provision. In Scotland, the Scottish Executiveis committed to the development of a new national ESOL strategy which aims to2 provide“high quality, accessible and affordable English for Speakers of Other Languages” (ScottishExecutive, 2005). Since 2004 the migrant worker population has shot up throughout the UKfollowing the accession of Eastern European countries to the EU. The topography of ESOLprovision is changing rapidly and the salience of the language needs of new groups of migrants,refugees and their families is increasing. However, publicly funded ESOL classes are in themain provided in Scotland by Further Education Colleges and by Community Learning andDevelopment Departments of local authorities. Classes are offered on a full-time or part-timebasis either free or at fairly low fees. Evaluations of second language programmes aimed atmigrants and refugees are not easily found, particularly evaluations of length and intensity(i.e. hours per week), both of which vary enormously from country to country. Yet assessingthe appropriateness of these aspects of language programmes for learners would appear to befundamental to policy decisions about future development.

This study compares learners enrolled on ESOL classes of different intensities (full-time,part-time and very part-time, i.e. 5 h per week or less). Its purpose is to investigate motivationaland socio-demographic differences. Do these differences match the pattern of provision? Dodifferent types of classes serve the learners’ needs? The study uses data from a national surveyof ESOL learners in Scotland. The paper is organised as follows. Section 1 overviews therelevant recent literature on instruction and motivation. Section 2 describes the method anddata used in the empirical study. Section 3 explains the results of the analysis while Section 4presents a discussion of these findings. The concluding discussion in Section 5 points to widerand to practical implications of the study.

1.1. Migration, proficiency and instruction

The studies by Dustmann, Chiswick and their colleagues have not included language instruc-tion (whether pre- or post-migration) as a factor in the development of language proficiency.The effect of instruction is well-known but very few studies of adult learners have attemptedto match length of instruction or intensity to attainment. Publicly funded second languageprogrammes in different countries offer a strikingly wide range of course lengths. The Aus-tralian Adult Migrant English Program (AMEP) provides immigrants with up to 510 h of freeEnglish classes (and 100 more hours allowed to refugees) (Australian Government Departmentof Immigration and Multicultural Affairs, 2006). In many European countries on the other handprovision is scarce, hours of instruction limited and waiting lists common, as they are in theUK.

C. Rice et al. / The Social Science Journal 45 (2008) 457–475 459

The relationship between language programme length and exit level proficiency has clearimplications for educational planners (Hakuta, Butler, & Witt, 2000; Oakeley, Urrabazo, &Yang, 1998). Defining what this level should be can be problematic. Hakuta et al. define“functional proficiency” as the level at which a learner can safely exit English-only education.For a child, this means able to perform school tasks in the second language at the same level asnative speaker pupils (“academic English proficiency”—Hakuta et al., 2000); for adults, onethat would allow someone to work or to embark on a college or university course (e.g. IELTSBand 6.0). Oakeley et al. and Hakuta et al. analysed longitudinal test data and concluded thatNNS children needed 5–7 years to reach the proficiency level of their native-speaker peers (i.e.a level at which they are deemed not to need further language support).

Intensity of instruction has been shown in some recent studies to play a greater role inlanguage development than total hours. Research into immersion study by school students inCanada has demonstrated a positive relationship between immersion and proficiency (Baker& MacIntyre, 2000; Genesee, 1984) as well as motivation, perceived competence and confi-dence (Wesche, Morrison, Ready, & Pawley, 1990). Freed and her colleagues have compareduniversity students’ progress in programmes of different intensity. Measuring intensity ofinstruction as well as variables of quantity and quality of language use outside the classroom,they have found that spending a study period (typically a university semester) abroad can resultin smaller proficiency gains than intensive study at home even when the students attended lan-guage classes during the study abroad period (Freed, Segalowitz, & Dewey, 2004). Pre- andpost-tests in speaking fluency (as measured by nine variables) revealed that the Immersiongroup, studying French in the United States for 17 h per week over 7 weeks with an aver-age 79 h of exposure per week made 100% improvement in “oral fluidity” (fluency) overall,while the Study Abroad group, on a similar number of classroom hours but with only 26 hof out-of-class practice, improved by only 40%. The At Home group, with 3–4 h of classesin French and about 11 h of extra-mural practice in French per week made no “oral fluidity”gains over the semester, and in some subskills performed worse in the post-test. Most inter-estingly, when students studying abroad were asked to keep a log of their interactions in theL2, it was clear that they used the L2 less often and for shorter periods than their first lan-guage (L1), or than students in an immersion setting. Active engagement in interactions inthe target language had a greater impact on learning than simply living in a foreign languageenvironment.

As Freed et al. (2004) note, research into the impact of “massed” (i.e. intensive) versus“distributed” hours of instruction on proficiency is sparse, particularly for adult learners. Ina large-scale study of Francophone schoolchildren in Quebec, Collins, Halter, Lightbown,and Spada (1999) found that 12-13-year-olds studying in “massed” conditions made moreprogress in ESL than those following the same programme in “distributed” classes. On arange of measures, from tests of vocabulary recognition to narrative writing, students whohad studied English intensively (18–20 h per week over 5 months) performed significantlybetter than those who had been taught for approximately 8 h per week for 10 months. Thisstudy, as the authors make clear, conflicts with studies in the field of cognitive psychologywhich have claimed an advantage for “distributed” as against “massed” instruction in othersubjects. It may be that intensive modes are more effective for language learning than for othersubjects.


1.2. Impact of motivation on learning behaviour

Motivation has been shown to have a highly significant bearing on attainment, and onrate of progress in language learning. Gardner’s influential socio-educational model (Gardner,1983, 1985, 2001; Gardner & Lambert, 1959, 1972; Masgoret & Gardner, 2003) places moti-vation alongside aptitude as the main predictors of proficiency. Motivation itself is drivenby “integrative” or “instrumental” orientations and it is the former that has most oftenbeen implicated in stronger motivation and higher proficiency (Gardner, 1985). Au (1988)criticizes the model and its hypotheses as lacking in operational rigour and, in particular,failing to establish empirically a causal link between the integrative motive and L2 profi-ciency. Dornyei (2003) points out moreover that, integrativeness and integrative orientation,although key to the model, are not fully defined. These concepts do not by the way neces-sarily invoke the desire to integrate with the L2 community although they do imply somekind of identification with it. The three necessary components of motivation, according toGardner, are attitudes towards learning the second language; the desire to learn; and effort tolearn.

Gardner’s work was based on research into French immersion classrooms in Canada(although see Masgoret & Gardner, 2003) and has been criticised for “not demonstratingthe relationship between social milieu and attitudes” (Spolsky, 1989, p. 159). Clement andKruidenier (1983) found, for example, that it was social milieu that determined whether or notan integrative orientation was a motivating factor. Peirce’s (1995) study of immigrant womenlearners in Canada sets the problem of motivation and exposure in a social identity framework.The women in her study though highly motivated and pro-active in seeking out classes feltunable to speak English in many social situations. Peirce argues that the dualistic concepts ofintegrative and instrumental motivation fail to describe attitudes to language learning whichare also affected by power relationships and identity, shifting across time and space. Insteadshe posits the metaphor of investment in the target language to include the notion of an inter-active relationship between learner, target language and “target society” (pp. 17–18). Peirceconcludes that it is the classroom teacher who has the main responsibility for helping “languagelearners to claim the right to speak outside the classroom”.

Wang (1999) goes further in distinguishing the situation of migrant learners of Englishfrom that of learners typically studied in motivation research. Her study of adult femaleMandarin speakers in Canada revealed that “behind learners’ high motivation. . .were theirfrustrations at being unable to have access to language acquisition contacts that they des-perately needed” (p. 11). She concludes, with Peirce, that Gardner’s socio-educationalmodel does not hold for such learners because opportunities for language acquisitionare so limited for them. Such opportunities are available through English-speaking socialnetworks, mainly provided by jobs—which they cannot access because of their poorEnglish.

1.3. The study

The language classroom plays a key role in second language acquisition and not only as asource of input. In this space, learners both acquire knowledge of the language and practise


communicative use, in a safe environment. Migrant workers and their families who live andwork in the target community do not however have less need of the classroom than learnersin L1 settings. Purvis (1996) points out that for beginners, including migrants, most exposureto English is provided by the classroom. Migrants’ target language communicative needsoutside the classroom are great and complex, so the classroom environment can be crucial, justfor survival. Countries such as Australia with continuing large-scale demand for immigrantworkers have recognised the need for intensive language learning opportunities for newlyarrived migrants: migrants who do not enrol on language classes within 3 months of arrivalare penalised by losing their entitlement to free classes.

The purpose of the present study is to compare the motivation and profile of learners enrolledon ESOL courses of different intensities. In particular it seeks to test to what extent learners’choice of study intensity is influenced by motivation—as measured by a range of variables.While previous studies in this area have shown that a key component of motivation is the effort,including time, learners are prepared to spend on language learning, it is proposed here that therelationship between commitment and motivation may be more complex, particularly for thoseconstrained by family and work responsibilities. Our study examines both initial motivation(goals) and attitude to language learning of learners in three conditions—full-time, part-timeand very part-time.

2. Method and data

2.1. The ESOL Survey

Our empirical tests will make use of data from an extensive survey (henceforth the ESOLSurvey) of the ESOL learner population in Scotland, conducted by Rice, McGregor, Thomson,Udagawa (2005) on behalf of the Scottish Executive. For the Survey, a self-completion question-naire was sent to 2500 learners in four areas of Scotland3 in publicly funded and voluntary-runclasses taking place in further education colleges, council-run community centres, schools,church halls and in homes. The learners targeted in the ESOL Survey included migrant work-ers and their families, students in Scotland to study English and refugees and asylum-seekers.A total of 1475 learners completed and returned the questionnaire. The returns yielded dataon the demographic structure and profiles of the ESOL population and on learners’ reasonsfor and attitudes to learning English in Scotland, as well as on barriers to learning such asresponsibility for childcare. The present study makes use of data from the ESOL Survey toderive profiles of learners who were attending classes of various intensities, i.e. for differentnumbers of hours per week.

2.2. Model and research hypotheses

Our empirical tests investigate whether there are any effects of motivations for learn-ing English on choice of study intensity. The hypothesis is that the probability of studyintensity choice will be affected by motivation in addition to other variables such asstudents’ socio-demographic and English proficiency. Thus the hypothesis should be the


following function:

Pr(study intensity) = F(motivation, socio-demographic, proficiency).

To explain the relationship between learners’ study intensity and motivation, we use multivari-ate statistical techniques with ordinal regressions where dependent variables are three ordinalcategories of study intensity – very part-time, part-time, and full-time study – and indepen-dent variables are items associated with motivation, directly and indirectly derived from thequestionnaire. Other factors such as learners’ socio-demographic characteristics and Englishproficiency are included in our regression model as dependent variables. To explore the effectsof explanatory variables on all levels of the ordered categorical outcome, an ordinal regres-sion method is deemed the most appropriate. The model for our regression is presented as thefollowing equation:

F {γi(X)} = log{

γi(X)

1 − γi(X)

}= log

{P(Y ≤ yi|X)

P(Y > yi|X)

}= αi + βX + ε,

where γ represents cumulative probability of being study intensity category i, which takes 1for ‘very part-time’; 2 for ‘part-time’, and 3 for ‘full-time’; α represents an intercept for eachprobability; X is a matrix of explanatory variables consisting of motivation-related variablesand others; β is a coefficient matrix corresponding to X; and ε is an error term.

In the above equation, the magnitude of the motivation’s effect on study intensity is explainedas how significantly one unit change in an explanatory variable representing motivation affectsthe change of the odds of the event occurrence—that is, a ratio of the probability of eventoccurrence to the probability of event non-occurrence. In this case, a ratio of the probability of‘very part-time’ to the probability of ‘part-time’ or ‘full-time’ will be tested. After examining therelationship between learners’ initial motivation and study intensity, we investigate whetherlearners’ motivation after enrolling on courses varies with study intensity through a meanscomparisons approach.

2.3. Dependent variable

The dependent variable in our model is study intensity (INTENSITY), represented by thenumber of hours of English classes a learner attends per week. The descriptive analysis ofINTENSITY is presented in Table 1. Study intensity in the sample ranges from 1 to 30 h perweek—mean: 11.41 h. Although INTENSITY can be expressed quantitatively, it is implausibleto assume that the variable is continuous and has normal distribution. Table 2 shows thatnearly three-fourths of the learners in the sample fell into one or other of the eight categories

Table 1Study intensity (hours of English classes per week; N = 1475)

Mean 11.41 Minimum 1.00Median 13.00 Maximum 30.00Mode 15.00 S.D. 5.40


Table 2Frequencies of study intensity

Study intensity (hours per week) N Percent Cumulative percent

15.00 436 29.6 29.68.00 112 7.6 37.24.00 97 6.6 43.7

16.00 94 6.4 50.110.00 92 6.2 56.312.00 92 6.2 62.618.00 89 6.0 68.6

2.00 86 5.8 74.4

of study intensity. For the purpose of this study, three categories are derived from the data:very part-time (5 h per week or less), part-time (6–14 h per week) or full-time (15 h or moreper week). Most 2- and 4-h courses are free and are delivered via Community Learning andDevelopment Departments of local councils. Part-time courses with more hours, togetherwith full-time courses are provided by Further Education Colleges. INTENSITY can thus bedescribed on an ordered or interval scale as a trichotomous measure (very part-time, part-time,and full-time) and an ordinal regression, rather than linear regression, is the most practicaltechnique. The ordinal regression model is preferred too because the model does not assumethe normality and constant variance for the dependent variable.4 In the ordinal regression,these three categories of study intensity – very part-time, part-time and full-time – are valuedat 1, 2, and 3, respectively. 19.7% of learners in the sample were ‘very part-time’ while 33.5%were ‘part-time’ and 47.3% were ‘full-time’ (Table 3).

2.4. Explanatory variable—motivation

The ESOL Survey questionnaire includes several items associated with learners’ initialmotivation for studying English. Learners were asked to select one main reason for havingcome to the UK from six: ‘to learn English’, ‘for work’, ‘to be with your family’, ‘to study’,‘as a refugee or asylum-seeker’, and ‘other’. It may be assumed that if language learning ischosen as the main reason for migration, motivation to learn is higher than if other reasons areselected. For this item, we established a dummy variable (MAIN), whose value is 1 if the mainreason is ‘to learn English’ and 0 otherwise.

It cannot be denied however that other reasons than ‘to learn English’ are associated withmotivation, as regardless of the primary purpose of migration a command of English is nec-

Table 3Description of the dependent variable

Category N %

Very part-time (5 h or less per week) 284 19.3Part-time (more than 6 less than 15 h per week) 494 33.5Full-time (15 h or more per week) 697 47.3

Total 1475 100.0


essary for work or for settling into a new community. To address this issue we include sevendummy variables, derived from the questionnaire, describing reasons for studying English.These reasons may be roughly classified as “instrumental”: ‘to apply for a job or for a betterjob’, ‘to help me with job that I have now’, ‘to take an English examination’, ‘to enter a Britishuniversity’, and ‘to start a college course (not English)’ or “integrative”(‘to make friends withEnglish-speaking people’ ‘to be more independent in daily life’). Respondents were asked totick as many as they wanted. For each choice the corresponding dummy variable is valued at1, otherwise 0. The seven dummy variables are labelled APPLY, JOB, EXAM, UNI, COLL,FRIEND and DAILY in the following tables. Given that motivation research has not been ableto correlate type of motivation (instrumental or integrative) with achievement, we cannot rankthese variables according to strength.

Motivation expressed as effort may be measured by how soon learners start English classes:the shorter the gap between arrival and joining an English class the greater the effort. Thequestionnaire asked about length of time in the UK and length of attendance at English classesin the UK: deducting the latter from the former produces a motivation-related explanatoryvariable (GAP), whose unit of measurement is a month. If this motivation variable does explainhours of classes, then the coefficient of GAP will be statistically significant.

2.5. Other explanatory variables

In addition to motivation, we include other variables which might affect study intensity.Firstly, as learners’ everyday responsibilities might restrict time available for attending Englishclasses, two variables associated with them are included in the model—employment status andfamily or child responsibilities. Both are dummy variables valued at 1 if a learner is workingfull-time or part-time or has a pre-school child or children without a carer at home. They arelabelled WORK and CHILD in the tables below.

We further include learners’ level of English as an explanatory variable. Learners were askedin the questionnaire to assess their own proficiency as needing “help to speak and understandEnglish most of time”, needing “help some of the time”, or not needing help “most of the time”.As a trichotomous measure this variable is an ordered dummy variable, 2 for the lowest levelof proficiency, that is, needing help most of time (LOW), and 1 for needing help some of time(MID). The reference category of this dummy variable is the highest level of proficiency—notneeding help most of the time.

Finally, three socio-demographic variables are added to the regression. These are age (aquantitative variable measured by a year unit—AGE), gender (a dummy variable – 1 for maleand 0 for female – GENDER), country of origin (a dummy variable – 1 for an advancedeconomy represented by membership of the OECD – OECD), and education level (a dummyvariable – 1 if the highest education level attained was college or above, and 0 if below collegelevel – EDU).

2.6. Data profiles of explanatory variables

Before running the regression, these explanatory variables and the three categories of studyintensity are presented in the form of cross-tabulations—in two types, depending on whether


Table 4Description of explanatory variables representing motivation (quantitative variable)

Very part-time Part-time Full-time

Gap between arrival and starting English classes (GAP)Mean 53.53 24.52 14.59S.D. 96.33 48.48 33.64

Mean difference from very part-timePart-time 29.01* (4.198)Full-time 38.93* (3.974) 9.92* (3.287)N 271 485 683

Note: unit is a month. Standard errors in parentheses. *Significant at 5% level based on Scheffe method. Due torounding, mean differences may not be equal to those calculated by means in the table.

the explanatory variables are continuous or categorical. It should be noted that the sample sizeof variables varies due to missing data. Table 4 describes one of the explanatory variablesrepresenting motivation—the gap in months between arrival and starting English classes in theUK. The means are 53.53, 24.52, and 14.59 months for very part-time, part-time, and full-timelearners, respectively. Thus on average very part-time learners joined an English class nearly4.5 years after they came to the UK, while part-time and full-time learners started their Englishclasses 2 years after and a year and a few months after, respectively. The mean differencesamong the three categories are statistically significant in each case.

Table 5 gives the eight categorical dependent variables associated with motivation by thethree study intensity groups—whether the main reason for coming to the UK was to studyEnglish or not and reasons for studying English. To check whether the three study intensitygroups are different from each other chi-square tests were used.

The proportion of learners who give their main reason for coming to the UK as to studyEnglish grows with the rise in study intensity, that is, the largest proportion appears amongstthe full-time learners, 20.1%, in comparison with 14.1% for part-time learners and 7.4% forvery part-time learners. The difference in the proportions is statistically significant: learnerscoming to the UK mainly to study English are more likely to attend a full-time English course.

With respect to reasons for studying English, more very part-time learners answered thatthey were studying English ‘to help them with the current job’, ‘to make friends’, or ‘to be moreindependent in their daily life’ than part-time or full-time learners. The proportions of verypart-time learners giving these three reasons were 67.7, 22.4, and 72.4%, respectively, and thedistributions of the proportions among the three categories of study intensity are statisticallysignificant.

On the other hand, ‘to take an English examination’ and ‘to enter a British university’ weremore commonly given as a reason for studying English by full-time or part-time learners thanby very part-time learners. Whereas 42.8% of the full-time learners and 41.3% of the part-timelearners said that they were studying English to take an English examination, only 29.6% ofthe very part-time learners chose this reason. 36.0% of the full-time learners and 29.8% of thepart-time learners were attending English classes to enter a British university, in comparisonwith 12.2% of the very part-time learners. The distribution patterns of these two answers acrossthe three categories of the learner’s study intensity are significantly different.


Table 5Description of explanatory variables representing motivations (categorical variables)


Main reason for coming to the UK (MAIN)To study English

Yes 7.4 14.1 20.1No 92.6 85.9 79.9 χ2 = 25.984*N 283 491 696 ρ = 0.000

Reasons for studying EnglishTo apply for a job/better job (APPLY)

Yes 51.7 54.3 56.4No 44.9 45.7 43.6 χ2 = 0.895N 284 494 697 ρ = 0.639

To help me with my current job (JOB)Yes 22.4 17.2 12.8No 74.1 82.8 87.2 χ2 = 16.714*N 284 494 697 ρ = 0.000

To make friends (FRIEND)Yes 67.7 55.7 49.1No 28.9 44.3 50.9 χ2 = 36.048*N 284 494 697 ρ = 0.000

To be more independent (DAILY)Yes 72.4 60.1 50.2No 24.1 39.9 49.8 χ2 = 51.995*N 284 494 697 ρ = 0.000

To take an English examination (EXAM)Yes 29.6 41.3 42.8No 67.0 58.7 58.5 χ2 = 1.131*N 284 494 697 ρ = 0.004

To enter a British university (UNI)Yes 12.2 29.8 36.0No 84.4 70.2 64.0 χ2 = 52.952*N 284 494 697 ρ = 0.000

To start a British college course (COLL)Yes 30.6 32.8 38.0No 66.0 67.2 62.0 χ2 = 5.214N 284 494 697 ρ = 0.074

Note: *significant at 5% level.

The remaining two reasons for studying English, ‘to apply for a job or a better job’ and ‘tostart a college course’ were chosen more often by the full-time learners, but the differencesamongst the groups are not significant.

Table 6 presents the non-motivational categorical explanatory variables and Table 7 givesthe quantitative variable, AGE. All seven categorical independent variables showed significantdifferences in the distribution pattern across the three categories of study intensity. The signif-icant differences in the categorical independent variables show us that very part-time learnersare more likely to be in full-time employment, female, from a non-OECD country and edu-cated to below college level. The proportions of these five characteristics decrease according


Table 6Description of other explanatory variables (categorical variables)


Full-time work (WORK)Yes 18.7 7.7 3.3No 81.3 92.3 96.7 χ2 = 66.775*

N 284 494 697 ρ = 0.000

Looking after a child/children (CHILD)Yes 14.8 19.2 10.8No 85.2 80.8 89.2 χ2 = 16.903*

N 284 494 697 ρ = 0.000

English proficiency (whether or not needing help to understand English)Most of the time (LOW) 30.5 15.1 14.2Some of the time (MID) 45.2 52.0 52.0Do not need help 24.3 33.0 33.8 χ2 = 38.839*

N 272 485 681 ρ = 0.000

Socio-demographicGender

Male (GENDER) 16.4 29.5 44.5Female 83.6 70.5 55.5 χ2 = 76.051*

N 275 484 685 ρ = 0.000

Home countryOECD (OECD) 14.8 20.4 25.0Non-OECD 85.2 79.6 75.0 χ2 = 12.864*

N 284 494 697 ρ = 0.002

EducationCollege/university (EDU) 46.7 61.5 64.2Below college 53.3 38.5 35.8 χ2 = 24.560*

N 261 475 671 ρ = 0.000∗ Significant at 5% level.

to the order of study intensity, that is, full-time learners are the least likely to have these fea-tures. Part-time learners show the highest proportions for CHILD, whose distribution patternis also significantly different. The mean comparison of AGE is significantly different across allgroups.

Table 7Description of other explanatory variables (quantitative variable)


Age (AGE)Mean 35.38 32.75 30.81S.D. 10.45 9.85 9.25

Mean difference from very part-timePart-time 2.63* (0.738)Full-time 4.56* (0.699) 1.94* (0.577)N 268 482 677

Note: unit is a year. Standard errors in parentheses. *Significant at 5% level based on Scheffe method. Due torounding, mean differences may not be equal to those calculated by means in the table.


Table 8Results of ordinal regression (dependent variables: very part-time = 1, part-time = 2, and full-time = 3)

Coefficient Wald ρ

Threshold for INTENSITYVery part-time (=1) −1.893** 46.121 0.000Part-time (=2) 0.017 0.004 0.949

MotivationsGAP −0.005** 11.508 0.001MAIN 0.441** 12.242 0.000APPLY 0.110 0.825 0.364JOB −0.196 1.437 0.231EXAM 0.253** 3.964 0.046UNI 0.451** 11.043 0.001COLL 0.330** 6.724 0.010FRIEND −0.511** 16.141 0.000DAILY −0.397** 9.294 0.002

OthersWORK −0.707** 22.563 0.000CHILD −0.268* 2.856 0.091LOW −0.305* 3.166 0.075MID 0.168 1.688 0.194AGE −0.006 0.714 0.398GENDER 0.819** 43.741 0.000OECD 0.129 0.656 0.418EDU 0.116 0.913 0.339

N = 1215. Pseudo R2: Cox and Snell = 0.168, Nagelkerke = 0.193, McFadden = 0.090a, the model assumption ofparallel lines was not violated, * and ** indicate significant at 10% and 5% level, respectively.

a The regression with complementary log–log link produced the Pseudo R2 as Cox and Snell = 0.160, Nagelk-erke = 0.184, and McFadden = 0.086.

3. Results

Using the model with the logit link, Table 8 shows that one threshold of the model equation,very part-time, is significantly different from zero, marking a clear distinction between verypart-time learners and the other two groups. The table also shows that both motivational andother factors have a significant influence on mode of study.

Among the motivation-related explanatory variables, six show statistical significance forlearners’ choice of study intensity. The regression results show that the sooner learners enrolledon their English courses after their arrival in the UK (that is, the shorter GAP is), the less likelythey are to be very part-time. As a shorter GAP is considered to represent stronger motivation,very part-time learners appear the least motivated. A similar inference can be drawn fromthe dummy variable standing for studying English as the main reason for coming to the UK(MAIN). The coefficient of MAIN is significantly positive, which means learners for whomstudying English is their main reason for migration are less likely to choose very part-timecourses.

Learners declaring their reason for studying English as to enter a university or start a col-lege course are less likely to be very part-time students. The two dummy variables, UNI


and COLL have a positive coefficient with statistical significance. The distribution compari-son in Table 3 does not show significantly different figures for very part-time and part-timeor full-time on COLL. However, the dependent variable in the ordinal regression analysisis associated with very part-time learners. Along with these two variables, if the reason forstudying is to take an English examination, a learner will join a more intense course. Thecoefficient of EXAM is also significantly positive. On the other hand, learners giving socialreasons for studying English tend to choose very part-time courses: the variables “to makefriends” and “to be more independent” have significantly negative coefficients, showing thatlearners with these reasons choose less intense study modes. The remaining study reasonvariables, related to work, were negative but failed to show statistical significance. Thesewere to help with current job (JOB) and to apply for a job or a better job (APPLY). Thus itappears that learners with strong instrumental motivation tended not to enrol on very part-timecourses.

Non-motivational independent variables – responsibilities at work and at home – appeared toreduce learners’ study intensity. The variables WORK and CHILD have statistically significantnegative coefficients, which means learners who work or take care of a child or children tendto choose very part-time courses. The dummy variable of GENDER is significant indicatingthat women are more likely to be very part-time learners. The remaining explanatory variablewith statistical significance is LOW, which identifies the least proficient group as more likelyto be very part-time.

3.1. Learners’ opinions of their classes

Motivation can reshape during an English course. Changes in motivation will impact onthe ordinal regression. We therefore compared the three study intensity groups with regard toopinions on their current classes. The ESOL Survey questionnaire asked learners to evaluatefour aspects of these—length of a class unit, amount of homework, number of examinations, andpreferred number of hours of classes per week. All the questionnaire items give trichotomouschoices. For example, for the item asking about length of a class unit answers were “too long”,“just right”, or “too short”. We have coded these choices as 1, 0, −1, respectively, and comparedthe means for the three study intensity groups. The codes for the other items, together with themean comparison results are presented in Table 9.

Our mean comparisons results depict very part-time learners’ motivations after enrollingon English classes—particularly in terms of class hours (both length of class and aggregatedhours per week) and examinations. Firstly for the item on class length (presented in the firstpart of Table 9) the mean for very part-time learners was negative, −0.155, which shows thatthere are more very part-time learners who consider their classes are too short than thosewho think them too long. This is significantly different from the full-time group, as more ofthese respondents felt classes were too long rather than too short—the mean was marginallypositive. Although the means for the amount of examinations are negative for all three groups,the very part-time mean was the largest, −0.388, significantly different from the part-timeand full-time means. Thus regardless of study intensity, learners wanting more examinationsoutnumbered those who wanted fewer examinations but it is the very part-time learners whoexpressed the strongest desire for more examinations. Opinion on preferred length of class


Table 9Mean comparisons of opinions about current classes

Mean S.D. N Mean differenceS.E. ρ

Length of class (too long = 1, just right = 0, and too short = −1)Very part-time−0.155 0.432 271Part-time −0.128 0.494 454 Very p.t.–p.t. −0.272 0.036 0.753Full-time 0.018 0.449 656 Very p.t.–f.t. −0.173** 0.034 0.000

Amount of homework (too much = 1, just right = 0, and too little = −1)Very part-time−0.058 0.432 259Part-time −0.114 0.527 466 Very p.t.–p.t. 0.056 0.039 0.367Full-time −0.078 0.523 667 Very p.t.–f.t. 0.020 0.037 0.865

Number of tests and examinations (too many = 1, just right = 0, and too little = −1)Very part-time−0.388 0.532 224Part-time −0.108 0.507 437 Very p.t.–p.t. −0.281** 0.043 0.000Full-time −0.133 0.519 648 Very p.t.–f.t. −0.256** 0.401 0.000

Preference for number of hours (increase = 1, no change = 0, and decrease = −1)Very part-time 0.729 0.478 269Part-time 0.551 0.595 454 Very p.t.–p.t. 0.178** 0.045 0.001Full-time 0.296 0.638 648 Very p.t.–f.t. 0.432** 0.043 0.000

Number of increased hours preferred (only respondents who chose ‘increase’ for the previous item)Very part-time 5.479 4.342 200Part-time 6.600 5.230 274 Very p.t.–p.t. −1.122** 0.428 0.033Full-time 6.398 4.056 256 Very p.t.–f.t. −1.422** 0.435 0.005

* and ** indicate significant at 10 and 5%, level, respectively.

hours per week similarly demonstrates the very part-time learner’s positive motivation: morelearners in this group wanted to increase class hours than to decrease them—the mean for thisitem was 0.729, which is significantly larger than those for part-time or full-time learners. Infact, a large majority of the very part-time learners (200 out of 269, or nearly 75%) favouredan increase in class hours—the equivalent proportions for part-time and full-time were 60 and40%, respectively. Interestingly, however, the number of extra class hours preferred by the verypart-time learners was smaller than those for the part-timer or full-time learners, with statisticalsignificance. The mean for the very part-time learners who wanted extra hours was 5.5 h, whilethose for part-time or full-time learners were 6.6 and 6.4 h, respectively. This suggests thateven though most very part-time learners wanted more English instruction, they perceivedlimitations on the time available for classes.

4. Discussion

Subjecting the ESOL Survey data to multiple regression analysis reveals firstly that language-learning motivation is multifaceted. Adult migrants may delay joining classes, enrol on verypart-time courses and express no interest in goals for which a high level of competence in thesecond language is a requirement—all indicators of low effort, identified by Gardner (1985)as a key component of motivation. However, these same migrants can exhibit a commitmentto learning a second language in the classroom comparable to those who migrated for study


purposes or who attend classes for more hours per week and their enthusiasm for languageacquisition may be as strong or even stronger.

Reason for migration to the UK and the gap between arrival and enrolment on an Englishcourse were singled out as initial motivation variables. For these learners, who are largelywomen, the early years of settlement in the UK may be characterised by family concerns, suchas pregnancy and the need to care for young children not yet in school as well as by supportfrom other family members (see Bloch, 2000). Typically, ESOL teachers find that women insettled ethnic minority communities seek out classes when their children start school, especiallyprimary school, and the slightly older age of very part-time learners seems to confirm this.

The regression analysis showed a clear distinction between the reasons for learning given byfull and more part-time learners and those selected by very part-time learners: the latter chosereasons that may be best described as integrative and tended not to choose reasons connectedto further study or employment requiring higher level proficiency. It may seem paradoxicalthat these learners did not show more interest in job-related reasons for learning English whenso many of them were in full or part-time employment. Focus group discussions undertakenfor the ESOL Survey revealed that many women in very part-time classes work in low-skilledjobs where fluent English is not required and interaction is routinized (e.g. shop work) or workin isolated situations requiring very little language of any kind (e.g. cleaning jobs).5

Ho (1992) found that part-time ESOL learners in Canada tended to be older, female andless educated, as we did. They had also lived in Canada for longer—in some cases for morethan 10 years. Women learners frequently arrive in the second language community with lowerlevels of English and have fewer opportunities for learning than men. Bloch’s (2000) study ofrefugees in East London (Somali, Turkish, Iraqi, Sri Lankan and Kosovar) found that althoughmost of the women described their level of English, both spoken and written, as lower than themen, “everyone who was studying for less than 5 h a week was female” except one. This patternis also described by Chiswick et al. (2002) amongst migrants in Australia, where women weremore likely to arrive with lower English proficiency and to make slower progress.

The data reveal a significant contrast between very part-time and full-time (i.e. 15–20 h perweek) learners’ attitude to length of classes, and a difference, though not significant, betweenthe former and part-time learners (i.e. 6–14 h per week). The attitude to homework, exams andtests supports the hypothesis that neither demographic differences between the groups nor studyintensity are necessarily reflected in attitude, in this case desire to learn and attitude to learning.Very part-time learners did not have a different attitude to the amount of homework they wereset—although we have no way of knowing if different types of learner were set comparableamounts of homework. On the other hand, very part-time learners differed significantly fromboth the other groups in wanting more tests and exams in English. This finding would appearto contradict the picture of very part-time learners as less ambitious and less oriented to higherlevel employment or study, as examinations are the gateway to both of these. It is all themore significant given the lower average proficiency of very part-time learners, as lower levelstudents may be less likely to perceive the value of tests and examinations than more advancedstudents who may be preparing for “exit” examinations such as IELTS which can give accessto further or higher education, vocational training or professional jobs.

Finally, a comparison of attitude to number of hours of English classes per week shows thatthose attending for the fewest hours express the strongest desire for more hours. The fact that


the number of extra hours they propose is lower may be accounted for by the heavier burden ofresponsibilities shouldered by this group. This strongly suggests that very part-timers’ motiva-tion is at least similar to that of other learners. Bloch’s (2000) major study of refugees in EastLondon found that 33% of those she interviewed wanted more hours, and that this proportionrose amongst the women, who were overwhelmingly enrolled on very part-time classes.

5. Concluding discussion

We set out to compare the background and attitudes of ESOL learners attending Englishclasses for different numbers of hours per week and to determine to what extent motivation canaccount for choice of mode of learning. Significant differences in reasons for learning Englishand commitment to learning amongst people on full-time, part-time and very part-time courseswould suggest that when providers offer a range of modes of attendance they are meeting arange of student needs. Very part-time learners are distinguishable from part-time and full-timelearners by their initial reason for coming to the UK and by the long gap between arrival andjoining an English class. However, our analysis shows that motivational factors alone do notpredict choice of study mode and that what we have termed demographic factors also play apart. Learners enrolled on the most part-time courses were more likely to have jobs, whetherfull-time or part-time, to be female and to be responsible for children. Moreover, some keycomponents of motivation such as effort and desire to learn appear as strong and perhapsstronger amongst very part-time learners.

The learners in our sample who were enrolled on very part-time courses were less proficientin English than those attending for more hours per week. This may be a function of the provision:free, part-time community-based courses are open only up to intermediate level. Research hasshown that for language learning massed instruction is preferred and that students in part-time or very part-time courses make slower progress—yet these are the learners who have thegreatest need for accelerated learning.

While the consensus amongst educational psychologists is that distributed hours of instruc-tion are more beneficial than massed, Collins et al. (1999) have shown that for language learningmassed instruction is preferred and that students in part-time or very part-time courses makeslower progress. Nevertheless the learners in our sample who were enrolled on very part-timecourses were less proficient in English than those attending for more hours per week.

These findings thus present a considerable challenge to language providers. The evidence ofthe low level of English of very part-time learners together with their commitment to languagelearning, in particular their strong preference for more hours of instruction, should promptproviders to consider ways of improving access to classes, by offering more childcare (normallyavailable only at Colleges) and more flexible hours for classes, including evening classes andmore morning classes.

At a time of international migration unprecedented in modern times, the language needs ofmigrant workers are an economic, social and political priority. Our analysis has shown thata significant minority of motivated learners are not well-served by existing provision. Thequestion arises: how many migrants are there who need English but who are deterred fromeven enrolling on ESOL classes by the current patterns of provision?


Notes

1. In fact, because of the uncertainty of self-reported proficiency estimates, the effect maybe considerably greater—perhaps up to 26% difference in the likelihood of finding workfor males (Dustmann et al., 2003; Dustmann & Van Soest, 2001).

2. The Scottish Executive became the Scottish Government in May 2007.3. Glasgow and surroundings, Edinburgh and surroundings, Aberdeen and Dumfries.

Between them, these were estimated to contain around 60% of all ESOL learners andpotential learners in Scotland.

4. The assumption of parallel lines across all levels of the categorical outcome will bechecked during our test.

5. Very part-time women learners may perceive the need to study English when a “languageshift” takes place at home—i.e. when their children and husbands are starting to useEnglish at school or work. Although the questionnaire which we used for our analysesdid not directly ask this issue, we have collected a few pieces of information inferring“language shift” from the data. The estimated average age of stating English class forvery part-time female learners was 32.4 years old, compared with 25.4 years old forthe full-time male, at the furthest end of the spectrum. As the main reason for comingto the UK, 59.1% of very part-time women gave “to be with their family” while only3.6% of full-time men did so. As one of reasons for studying English, 75.7% of thevery part-time women described “to be more independent in their daily life”, whereas43.9% of the reference group did so. The results suggest that women who followedtheir husbands to the UK and have a school-age child or children started an Englishclass for their daily life. We thank an anonymous referee for raising this interestingpoint.

Acknowledgments

The authors are indebted to the Scottish Executive (Department of Transport, Enterpriseand Lifelong Learning) which commissioned the research on which this study is based. Theyalso gratefully acknowledge the help and advice given by Dr Peter Romilly of the Universityof Abertay Dundee. The authors are responsible for any factual errors and for all opinions andinterpretations given here.

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Documents

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