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Adult learners in wales: trajectories andtechnologiesStephen Gorard aa Cardiff University, UKPublished online: 04 Jun 2010.

To cite this article: Stephen Gorard (2003) Adult learners in wales: trajectories and technologies,Innovations in Education and Teaching International, 40:4, 395-403, DOI: 10.1080/1470329032000128422

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395 IETI 40,4

Adult Learners in Wales: Trajectories and TechnologiesStephen Gorard, Cardiff University, UK

SUMMARY

Patterns of lifelong participation in adult education and training in Wales are distinctive within theUK. Participation, qualification and economic activity tend to be lower than for the othergeographic regions of the UK, and remain systematically structured by age, sex and social class. Aproblem facing practitioners and policy-makers attempting to increase participation rates in Walesis the very low population density and accompanying problems of travel to institutions. Wales istherefore a prime site for testing the notion of ‘anytime, anyplace, anywhere’ inherent in virtualparticipation. The results presented in this paper show that, so far, technology has simply replicatedexisting societal divisions. Of a random sample of 1000 households from a 2002 survey, no one inWales reported ever using the UK’s University for Industry/learndirect, for example. Using life-likelogistic regression analysis of patterns of participation in post-compulsory learning, the paper showsthat many of the determinants of adult learning are social and economic. Access to, and use ofinformation and communication technology (ICT), are not good predictors of participation inlearning. Rather, access to ICT is an outcome of the same socio-economic determinants asparticipation itself. Perhaps the money spent on high-profile initiatives such as the University forIndustry would be better spent on more traditional forms of adult learning, whether involvingtechnology or not.

INTRODUCTION

Traditionally, all numeric measures of educationalparticipation and attainment in Wales have beeninferior to those elsewhere in the UK, both at schoollevel and lifelong (Gorard, 2000a, 2000b). A varietyof national policies have been created to deal with theproblem, including provision of learner allowances,targets for participation (Gorard et al., 2002), thevirtual and satellite college system (Selwyn andGorard, 2002), and individual use of information andcommunication technology (ICT). ICT is seen bymany as a technical solution to the barriers of timeand space, and travel to and from an institution. Thereport of the Education and Training Action Groupfor Wales had this to say:

Modern information and communications systems,including digital developments, present both opportun-ities and threats in adult education. ICT can minimise theconstraints of time and space: people can learn or gain

information about what is available, whenever andwherever they wish – providing they have access tomodern technology and the confidence to use it.

(ETAG, 1998, p. 30, author’s emphasis)

In 2002, the UK’s National Institute of Adult andContinuing Education (NIACE) conducted a surveyof adult learners in Wales (Sargant and Aldridge,2002). This paper reports some of the findings of thissurvey with respect to the above claim about ICT andpatterns of lifelong learning. The boosted sample ofrespondents in Wales is around 1000 for the firsttime, and allows a more in-depth analysis thanprevious years. The NIACE survey contains more,and more detailed, questions about individual accessto and use of technology for learning. Therefore,perhaps for the first time on a large scale in Wales itis possible to test this notion of overcoming barriers.There is already considerable doubt about the actual

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role of ‘barriers’ in preventing access to adultlearning opportunities. Evidence is mounting thatnon-participants in formal educational episodes arenot especially deterred by traditional barriers such astime, cost, travel and lack of initial qualification. Thisevidence comes partly from the role of long-term,socio-economic background characteristics – espe-cially the influence of family – in creating a learneridentity which does not view current opportunities asappropriate, interesting or useful (Gorard et al.,1999b). Evidence also comes from a model of twoseparate sets of determinants for extended initial andlater learning, respectively (Gorard et al., 1999c),and from the accounts of widespread informallearning for which barriers are, by their very nature,less relevant (Gorard et al., 1999a). Recent NIACEsurveys in Wales have confirmed the prediction ofTitmus (1994) that there is a substantial sub-set of thepopulation who are ‘beyond all attempts to reachthem’ – see also, Harrison (1993) and McGivney(1993).

As well as leading to economic competitiveness(perhaps) and social mobility (probably), educationis nearly always a genuinely transformative experi-ence for an individual – at least it was in Walesaccording to Lewis (1993) – and one that impactson the local community. Learning should not there-fore be viewed as an escape route from anything,but a normal part of an accomplished life in ademocratic society. Viewed in this way, it is notclear that the experiences offered by the virtualcollege movement, which is too often based on amodel of information transmission, can be genu-inely educational, or that they can lead to betterreasoning skills, creativity and the ability to valuedivergent cultures claimed by Roll (1995). Giventhese severe limitations, it may therefore be seen ascompletely rational for an individual to decline toparticipate. Unfortunately, at least partly becauseprogress is measured in terms of the qualificationtargets used to attract inward investors, such aconclusion is not considered by policy-makers inWales, where much lifelong learning policy is stillprescriptive (Tight, 1998).

The analysis is based on the following questions:

1. Who among the adult population of Wales areparticipants in recent learning experiences, andhow do they differ from those who are notparticipants?

2. What are the long-term and current determinantsof participation in Wales?

3. Who among the adult population of Wales haveaccess to various technologies with a capabilityof delivering learning experiences?

4. Does access to technology, particularly ICT,‘create’ adult learners and learningexperiences?

METHOD

The NIACE 2002 survey of adult learners involved5885 households, based on 390 randomized geo-graphical clusters, including a boosted sample of 995cases from Wales, and re-weighted to reflect appro-priate proportions by sex, social class (assessed interms of AB: professional/managerial, C1: non-manual, C2: manual skilled, and DE: partly or un-skilled) and standard economic region. The responseshave been analysed here in terms of frequencies andcross-tabulations. All figures for Wales are presented,implicitly, in comparison to those elsewhere in theUK.

In addition, logistic regression analysis was used to‘predict’ patterns of individual participation (formore on this see Gorard et al., 1999c). The dependentvariable was participation in lifelong learning (rang-ing from those who report no episodes of educationor training since leaving full-time education to thosewho report episodes of post-compulsory education ortraining in the last three years, including currentepisodes). Some cases remain unclassified for thisanalysis. The independent variables, or potentialdeterminants of participation, are entered in batchesin the order that they occur in the individuals’ life(this is instead of the more usual procedures of eitherentering all variables in one step, or stepwise in theorder of the amount of variance they explain).

The variables entered at birth were age, sex, ethnicgroup and family language. The variables entered inthe second stage were age of leaving full-timeeducation (and the interactions of this with the ‘birth’variables). The variables entered in the third phasewere social class, employment status, area of resi-dence, language in which they wish to learn and ageof children. The variables entered in the fourth phasewere reported access to various technologies, includ-ing the Internet. Each stage also examined the impactof these variables in interaction. In this way, thevariables entered at each step can only be used toexplain the variance left unexplained by previoussteps, and are selected by using the Likelihood Ratiostatistic. Thanks to this novel method of analysis,which models the order of events in individuals’

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Adult Learners in Wales 397

lives, the relevant variables become valuable clues tothe socio-economic determinants of patterns ofparticipation in adult learning.

The decision whether to use qualifications as apredictor when modelling participation is a difficultone, since it is not clear how far qualifications are theoutcome or the determinant of formal episodes.Many observers, having noted the strong relationshipbetween prior qualifications and further participation,have suggested that qualification is a key determinantin an accumulation form of human capital theory(Roberts and Parsell, 1990; Smithers and Robinson,1991). However, changes in participation over timemean that age alone is a good predictor of initialqualifications, and since the growth has been strongerfor women than men, gender in conjunction with ageis an even better one. When further personal andsocio-economic background predictors are added toage and gender, levels of personal qualification arethemselves accurately predictable (Gorard, 1997a).

There is, therefore, no reference to qualifications aspotential predictors in the models that follow.However, alternative models have also been createdwith two levels of information about qualifications –as part of the interactive fitting and criticism process(Dale and Davies, 1994). The gains in the accuracyof allocating individuals to categories of participationwere negligible, although as soon as qualificationsare introduced they become key predictors forparticipation. What happens is that qualificationssimply replace, or act as a proxy for, socio-economicvariables that are just as effective as predictors butwhich predate the qualifications in a causal chain. Putsimply, qualifications are more nearly a characteristicof a pattern of participation than a cause of it.

LEARNERS AND NON-PARTICIPANTS

In the UK, 42% of cases reported a current or recentlearning episode (in the past three years), 22%reported some non-recent post-compulsory educationor training, and 36% reported no learning episodessince leaving full-time education. The first group isdescribed here as ‘recent learners’ and the third groupis described as ‘non-participants’. The size of the non-participant group is similar to that reported in previousstudies of lifelong learning (Gorard and Rees, 2002).Around one-third of adults are not, and have not been,involved in our ‘learning society’ – at least in terms offormal participation in adult learning episodes – butsee Selwyn and Gorard (2002). Many of these are theadults that current policies are attempting to include.

More specifically, this is a key group for whichinformation technology, whether informally, or viaorganizations like the University for Industry (UfI) orlearndirect, is intended to overcome barriers of time,place and cost. The situation in Wales is worse than theUK overall and, as far as we can tell from the earlierfigures (483 cases), getting worse over time. Only39% of adults in Wales are current or recent learners,lower than in 1999 and lower than the UK (see Table1). Thirty-eight per cent of adults report no laterlearning as adults, and this is more than in 1999 andmore than the rest of the UK.

However, as the following results emphasize, recentlearners and non-participants in Wales differ fromeach other in other systematic ways that cast doubton the possibility that increasing opportunities,overcoming barriers and use of technology willprovide a general solution to the problem of non-participation (Gorard, 2000b). For example, reportsof participation are less frequent among: oldercohorts (they decline with age – see Tables 2 and 3);less prestigious occupational groups (they declinewith social class – see Tables 4 and 5); those leavingfull-time continuous education earlier; and those withlower levels of qualification. [Note: In Tables 2 to 5,7 and 8, the ‘total’ value shows the percentage of oursample with a specified pattern of learning (e.g. 39%are recent learners in Table 2). The succeedingcolumns each show the percentage of a sub-group ofthe sample with that specified pattern of learning

Table 1 Patterns of participation over time and place(percentage of cases)

UK,2002

Wales,1999

Wales,2002

Recent learner 42 43 39Non-participant 36 34 38

Table 2 Pattern of recent learners by age cohort(percentage)

Total 17–19 20–24 25–34 35–44 45–54 55–64 65–74 75 +

39 81 66 49 49 36 29 19 6

n = 995

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(e.g. 81% of respondents aged 17 to 19 years arerecent learners in Table 2).] Table 3 is particularlypowerful, since it shows clearly how older groups areless likely to have been involved in any learning,despite the longer time they have had to do so.These tables show that total non-participation willdiminish over time, because such a high proportionof 17–19-year-olds are current learners. However, the18% of the age group not continuing with educationafter school are, according to previous studies, likelyto remain non-participants, and many of the othersare likely to be only ‘transitional’ learners – continu-ing with immediate full-time continuous educationbut then not being involved again. The two groupsdiffered little in terms of ethnic background. Men andwomen are more nearly equally likely to be currentlearners, but men are less likely to be non-partici-pants (36%:41%). Again this bears out earlier workshowing that while immediate post-compulsorylearning is now largely gender neutral, later-lifelearning is not (Gorard, 2002).

The patterns of participation by age and class arereflected in the reported highest qualification of therespondents (see Tables 6–8). Levels of qualifica-tion in Wales are generally lower than in the rest ofthe UK. Highest lifetime qualification below NVQlevel 2 (or equivalent) is more prevalent amongolder age cohorts, again despite the greater timethey have had. Low levels of qualification aresomewhat more common among women (37%),those who left school at the earliest opportunity andthose who report being less likely to participate inthe next three years.

The difference between Wales and the UK appears inalmost every indicator of participation. Only 37% ofadults in Wales report being likely to take part in alearning ‘episode’ in the next three years (comparedto 42% in the UK). Even informal learning activitiesare generally less prevalent in Wales (see Table 9).Much of the rest of this paper considers why thismight be so.

However, it is worth first considering one issueunique to Wales – its bilingual nature. Only 87% ofadults in Wales learnt English as their ‘mothertongue’ compared to 93% in the UK. Of the

Table 3 Pattern of non-participants by age cohort(percentage)

Total 17–19 20–24 25–34 35–44 45–54 55–64 65–74 75 +

38 18 20 26 28 41 42 61 69

n = 995

Table 4 Pattern of recent learners by social class(percentage)

Total AB C1 C2 DE

39 53 57 33 26

n = 995

Table 5 Pattern of non-participants by social class(percentage)

Total AB C1 C2 DE

38 19 22 39 57

n = 995

Table 6 Pattern of highest qualification (percentage)

None NVQ2 NVQ3 NVQ4/5 Other/not known

33 23 12 19 8

Table 7 Pattern of no qualification by age (percentage)

Total 17–19 20–24 25–34 35–44 45–54 55–64 65–74 75 +

33 16 14 11 17 41 41 60 74

Table 8 Pattern of no qualification by social class(percentage)

Total AB C1 C2 DE

33 12 20 32 52

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Adult Learners in Wales 399

remainder, the vast majority (10%) learnt Welsh as achild. These tend to be older, aged 35 + and mostprevalent (19%) among men of the 75 + agecategory. They tend to live in rural areas rather thanthe south-east of Wales, and because of their age theyare more likely to be retired. All describe themselvesas ‘White’, along with 98% of adults in Walescompared to 93% in the UK. Of the recent learners inWales, 5% studied Welsh in last three years.However, the proportion wishing to learn through themedium of Welsh is far lower at 4% than the numberwho learnt it originally. Those wishing to learnthrough Welsh are, in general, not those for whom itis a mother tongue. They are younger 25–44, morefemale, with longer initial education and higherqualifications. In fact, not one person of 995 reportedlack of opportunity to learn in Welsh as a barrier toparticipation. This potential barrier, therefore, cannotbe used to explain the lower figures for participationin Wales. It should be noted, however, that the termsof the Welsh Language Act clearly make it evenharder than in the rest of the UK for the very smallminority who wish to participate in a language otherthan Welsh or English – see, for example, Gorard(1997b).

ACCESSING AND USING ICT

A key argument for the impact of ICT on participa-tion is the ability to overcome physical barriers suchas travel and place through technology – a factor ofparticular relevance to Wales. However, this newdataset agrees with previous work in offering onlylimited support for the argument. Under 2% ofrespondents cited travel/transport as the chief barrierto their future participation. This figure is slightlybetter than for the UK as a whole, but has themethodological problem that the question was onlyasked of participants.

Twenty-six per cent expressed no interest in furtherlearning, 15% claimed to be too old, 10% expressedno need to learn and 4% said they had not got‘around to it’. This is a total of at least 55% of non-participants who, if taken at their word, would beunaffected by access to learning opportunities viaICT. However, of the others, 21% cited lack of time,7% cost, 8% need to care for others and 6% being tooill. All of these problems might be amenable, at leastin part, to a technological solution.

Unfortunately, the patterns of learning and non-participation in terms of age and class are largelyrepeated in terms of access to the technology itself(Tables 10 and 11). For example, while 81% of recentlearners have access to the Internet at work, none ofthe non-participants do. Of course, we cannot tellsimply from these figures if there is a causal link hereor, if there is, in which direction it flows (but seebelow).

Another common argument for the role of technol-ogy is that virtual colleges will encourage wider

Table 9 Patterns of selected ‘informal’ learning(percentage)

UK Wales

Seldom visit library 65 70Seldom visit museum 91 94Seldom visit art gallery 94 97Seldom visit community centre 77 72Regularly visit community centre 13 17Reading as main leisure activity 41 39

Table 10 Pattern of recent learners by regular access totechnology (percentage)

Total recent learners 39Digital TV 44Analogue cable or satellite TV 43Analogue TV 34Internet (work only) 81Internet at home 57No Internet 28No PC 21No telephone 40

Table 11 Pattern of non-participants by regular accessto technology (percentage)

Total non-participants 38Digital TV 32Analogue cable or satellite TV 39Analogue TV 45Internet (work only) –Internet at home 20No Internet 50No PC 57No telephone 49

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participation. In this survey, 15% of people reportedfinding out about their most recent episode fromfriends or family, 6% from a work mate, 8% fromprinted publicity, 6% from newspapers, 14% fromtheir college, 6% from a higher education institutionand 7% from a school. Only 2% (six cases) reportedusing the Internet to help find out about the episode,and none at all used the UfI or learndirect. So thefirst conclusion is that the latter source of informa-tion is not widespread compared to more traditionalsources.

Of the six cases using the Internet as a source ofinformation about learning, four were male, nonewere in social class DE, four already had some post-compulsory learning experience, none were unquali-fied while four had an NVQ3 qualification or higher,five were in work, all were White, five had access tothe Internet at home, and all had a PC and atelephone. Given that no one reported using the UfI/learndirect, the technology route to post-compulsoryeducation appears to be recruiting largely the ‘usualsuspects’. These are younger, employed, profes-sional, male, qualified, already learners, who all haveaccess to the relevant technology at home. Clearly,these are very small numbers (see also below), andthat ‘smallness’ is the key finding here, but there iscertainly no evidence of ICT routes breaking themould since even this small number is relativelyprivileged already.

To a large extent the same pattern appears when weconsider technology as the deliverer of the learningexperience itself, rather than simply of informationabout learning. Of the recent learners, 26% reportedstudying at college, 14% in higher education, 15%at work, 6% at a school, 6% at a private oremployer-funded training centre, and 6% at homevia books or correspondence. In comparison, 4%(16 cases) principally learnt at home using acomputer, and only 1% (four cases) learnt else-where using a computer – including at learndirector UK On-line centres.

Of the 16 cases using a computer to learn at home,eight were male, eight were aged under 45, 14 weresocial class ABC, 11 lived in urban areas, seven hadno previous post-compulsory learning experience,four had no qualifications while six had NVQ4/5, sixwere retired, three were not working, all were Whiteand 10 had access to the Internet at home. Of the fourcases learning at an ICT centre, three were female, allwere under 45, all were social class DE, all lived inurban areas, two had no post-compulsory learning

experience, none were qualified above NVQ2, allwere unemployed, all were White, none had access tothe Internet and none had a PC. Thus, we have someslight evidence, with the caveat of the small numberof cases, that the two groups are distinct. Thoselearning at home using a computer are more similarto those using the Internet to find out about learning– male, qualified and professional – although perhapsa little older. On the other hand, those learning incentres are somewhat more likely to be female andless qualified.

These differences appear also in patterns of access tocomputers, and patterns of learning about computersthemselves. Computer skills, IT and the Internet is bysome way the largest single area of study for recentlearners (27%). These tend to be older or very young(see Table 12), less qualified, more not working(33%) or unemployed (31%), and so more like thoseusing IT in drop-in centres.

Tables 13–15 show that Internet access itself ispatterned by social class and qualification. Internetaccess (36%) is lower than in the UK (42%).

Table 12 Pattern of studying computing skills by age(percentage)

Total 17–19 20–24 25–34 35–44 45–54 55–64 65–74 75 +

27 37 9 22 30 39 23 34 45

Table 13 Pattern of regular access to technology(percentage)

Mobile phone CD DVD PC Internet

70 77 24 48 36

Table 14 Pattern of regular access to Internet by socialclass (percentage)

Total AB C1 C2 DE

36 61 55 31 17

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Adult Learners in Wales 401

Of those with access to the Internet the single mostcommon use was e-mail (just over 23%), followed bybrowsing (just under 23%) and looking for informa-tion on goods and services (6%). Seven per centmostly used the Internet for finding informationrelevant to learning or training, and 4% mostly usedit for learning on- or off-line.

THE LONG-TERM SOCIO-ECONOMICDETERMINANTS OF PARTICIPATION

There is so much potential interaction between thevariables in the analysis so far that I attempt to clarifyat least part of the story via multivariate analyses. Forexample, it is clear so far that older groups and thosewho leave full-time education early are both lesslikely to have access to technology such as theInternet. But older groups are also more likely tohave left full-time education early, and it is unclearwhat the interrelationship is here. The analysisconcerns those who are recent learners and those whoare non-participants. The variables are entered intothe analysis in the order that they would occur overthe life-course, and are used to try to predict in whichof the two groups each individual will be.

Using only those variables known about eachindividual at birth the model is 67% successful inallocating them to being recent learner or not (seeTable 16). The only variable retained is the age

cohort (sex and language of the home, for example,are not relevant once age is taken into account).Older individuals are, as we have seen above, far lesslikely to be learners than non-participants. Forexample, the youngest cohort is 44 times more likelyto be learners than the older cohort.

By the time of leaving full-time education, the modelfor recent learners is 75% accurate (see Table 17),and now includes age (as above) and age of leavingfull-time education (an individual is 1.2 times aslikely to be a learner with every year of immediatepost-compulsory education).

When adult and work-related variables are added, themodel for recent learners becomes 78% accurate (seeTable 18). The determinants are age, age of leavingfull-time continuous education, and now social class.The more prestigious the social class the more likelythe individual is to be a learner. For example, those insocial class AB are 3.2 times as likely (once otherfactors are accounted for) to participate as class DE.

Finally, when current access to technology variablesare added, the model for recent learners becomes81% accurate (see Table 19). The determinants are asabove, with the addition of access to technology.While regular access to mobile phone (0.98), CD(0.67) and DVD players (1.1) make little difference,having access to the Internet increases the odds ofparticipation by 9.0 compared to having no access totechnology at all.

Table 15 Pattern of regular access to Internet by highestlifetime qualification (percentage)

Total None NVQ2 NVQ3 NVQ4/5

36 14 43 60 57

Table 16 Success in predicting learners at birth

Predictednot learning

Predictedrecent learner

Observed not learning 240 156Observed recent learner 96 281

Note: tables like this show the number of cases that can beclassified correctly in terms of their current/recent learninghistory using only information that predates it

Table 17 Success in predicting learners at school

Predictednot learning

Predictedrecent learner

Observed not learning 276 120Observed recent learner 73 304

Table 18 Success in predicting learners as adults

Predictednot learning

Predictedrecent learner

Observed not learning 307 89Observed recent learner 82 295

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402 IETI 40,4

Several conclusions can be drawn from this simpli-fied trajectory analysis. First, despite missing infor-mation about family background, it is clear that long-term social, economic and educational factors areclosely related to patterns of current/recent learning.While access to ICT is largely patterned in the sameway, access to ICT does not, in itself, add much moreto our predictive model. It is, like qualifications,largely a proxy for the other, more complex,variables that pre-date it.

CONCLUSION

Clearly non-participation in education remains asignificant and deep-rooted trend in Wales, with orwithout ICT-based initiatives. That 39% of individ-uals reported taking part in some form of learningover the past three years is tempered by the conversefinding that an equivalent proportion of the popula-tion reported no learning episodes after full-timecontinuous education. Crucially, the data reiterate theconclusion that whether or not an individual partici-pates in learning is a lifelong pattern, alreadypresaged at school leaving age, and intrinsicallyrelated to long-term social, economic and educationalfactors.

It also follows from the regression analysis of theparticipation data that access to ICT does not, initself, make people any more likely to participate ineducation and (re)engage with learning. Access toICT continues to be largely patterned according tolong-term pre-existing social, economic and educa-tional factors. Thus, like educational qualifications,access to ICT is a proxy for the other, more complex,social and economic factors that pre-date it ratherthan as a direct contributory factor in itself. Thispoint is an important one and worth reiterating as it isat odds with much contemporary educational think-ing and rhetoric. This is highlighted in the followingnewspaper report on the preliminary analysis of thesame survey data:

‘Divide is Blamed for a Slump in Adult Learning’ . . .the digital divide is having a big impact in preventingmore adults from accessing new skills and learningopportunities . . . Inability to access the Internet is afactor in encouraging more people to start studying again. . . More than two thirds of people without Internetaccess at home said they are unlikely to learn in thefuture, compared to less than half of those connected tothe web.

(South Wales Echo, 2002)

The data from the NIACE survey may not accuratelyreflect all informal learning that people are likely tobe using ICT for. There was considerable evidencethat the learning episodes reported by participantswere likely to take the form of basic or advanced ICTskills courses. There was also emerging evidence thatsome non-participants are being ‘won over’ by ICT-based learning. The emergence of a small number of(relatively) older women from lower socio-economicgroups beginning to learn to use IT in community-based sites does point to a change in the pattern oflearning activity directly attributable to IT. What thelonger-term effects are of such groups ‘dipping theirtoe in the water’ of adult learning in this way remainsto be seen. It is a sign of hope. Indeed, ICT skillscourses could be seen as the typing and secretarialcourses of the 2000s but the fact remains that suchprovision has replaced and not augmented othertypes of provision. Indeed, set against the highexpectations set up by the government I can onlyconclude that ICT is not having the widespreadbeneficial impact that many politicians and educatorswould have us believe.

From a political point of view, politicians and policy-makers need to resist the tendency to overplay what ison offer to learners via ICT. Much of this so-called‘new’ ICT-based educational provision is eitherrepackaged ‘old’ educational provision and courses ora narrow provision of new courses. As Cullen et al.(2002) quite sensibly concluded, ‘the evidence doesnot suggest the “new learning technologies” imply orprecipitate “new forms of learning” ’. The majority oflearndirect’s 800 specialist courses have been in basicand advanced IT skills and tied into existing learningproviders such as further education colleges andcorporations – hardly the cornucopia of diverselearning opportunities (anything) being made avail-able ‘anytime’ and ‘anywhere’. Over the last threeyears (practically the whole life of the UfI) 14 peopleout of a national sample of nearly 6000 have found outabout, or arranged, a learning experience through it.That represents around 0.002% of the population, whoare, in any case, merely the ‘usual suspects’.

Table 19 Success in predicting learners via ICT

Predictednot learning

Predictedrecent learner

Observed not learning 326 70Observed recent learner 74 303

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Gorard, S (1997a) Initial Educational Trajectories, Work-ing Paper 8, Patterns of participation in adult educationand training, School of Education, Cardiff.

Gorard, S (1997b) Paying for a little England: schoolchoice and the Welsh language, Welsh Journal ofEducation, 6, 1, 19–32.

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BIOGRAPHICAL NOTES

Stephen Gorard is a Professor at the Cardiff Uni-versity School of Social Sciences, having previouslybeen a secondary school teacher/manager and adulteducation lecturer. His current research interestsinclude widening adult participation in learning, therole of technology in lifelong learning, informallearning (http://www.cf.ac.uk/socsi/ICT), the role oftargets, the impact of market forces on schools(www.cf.ac.uk/socsi/markets), underachievement,teacher supply and retention, and developing inter-national indicators of inequality (http://www.cf.a-c.uk/socsi/equity). His main task at present is todirect the research capacity-building support networkfor the ESRC Teaching and Learning ResearchProgramme (http://www.cf.ac.uk/socsi/capacity). Heis the author of over 200 publications; these includebooks, book chapters and papers.

Address for correspondence: Stephen Gorard, Car-diff University School of Social Sciences, Glam-organ Building, King Edward VII Avenue, CardiffCF10 3WT, UK. e-mail: Gorard@cardiff.ac.uk

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