Patterns of participation in lifelong learning: Results from the Adults Learning@Home 2002 household survey
Stephen Gorard and Neil Selwyn
Cardiff University School of Social Sciences
King Edward VII Avenue
Cardiff
CF10 3WT
Paper presented at the British Educational Research Association Annual Conference, Heriot-Watt University, Edinburgh, 11-13 September 2003
Abstract
This paper is based on 1,001 home-based interviews with UK adults. It describes their varying patterns of participation in lifelong learning and their use of technology for learning and leisure. It finds that 37% of all adults report no further education of any kind after reaching compulsory school leaving age. This proportion declines with each age cohort, but is largely replaced by a pattern of lengthening initial education and still reporting no later education. These patterns of participation are predictable to a large extent from regression analysis using a life-order model of determining variables – all of which are set very early in life. This suggests that universal theories to describe participation, such as human capital theory, are incorrect in several respects. Where individuals create, for themselves and through their early experiences, a ‘learner identity’ inimicable to further study, then the prospect of learning can become a burden rather than an investment for them. This has implications for the now widespread and lavishly-funded notion of overcoming barriers to access via technology.
Background
Greater attention than at present needs to be given to patterns of adult participation in learning opportunities for several reasons, most importantly because the determinants of participation are so widely misunderstood. Most research in this area considers only the views of participants and recent participants, and so biases the scientific conclusions. It tends to obscure the scale of resolute lifelong non-participation in formal learning episodes, ignore the often very valid reasons for non-participation, and downplay the valuable self-directed learning evident among apparent ‘non-learners’ (Gorard and Rees 2002). In addition, there is often a confusion between changes over time for successive age cohorts and for individuals. Most existing policies are directed at improving measurements of learning such as participation among working-age adults that have no impact at all on the life of the adults themselves. For example, most of the growth towards UK targets for lifelong learning is explained by the annual addition of qualified 16-year-olds to the workling-age population and the subtraction of less qualified retirees. Growth towards the target is, thus, achieved without any increase in education or training for adults (Gorard et al. 2002). In fact, formal participation in later-life is reducing over time and becoming more inequitable in terms of sex, social class, and employment (Gorard et al. 1999). The results of the new study outlined in this paper begin to explain why the multiplication of opportunities and the removal of barriers to participation may not be effective, in isolation, in attracting those on a non-participatory ‘trajectory’.
The potential of information and communications technology (ICT) to ‘free’ adult education from the barriers that previously prevented people from participating has been prioritised at the core of the current ‘lifelong learning’ ICT agenda in the UK and elsewhere. Existing barriers to learning, whether they are categorised as cultural, structural and personal (Maxted 1999) or situational, institutional, and dispositional (Harrison 1993), are now seen as resolvable through the use of technology:
Rather than sitting in the stands or cheering from the touchline, ICT will enable learners to acquire transferable skills and to play a full part in the game, according to their own rules … ICT can provide for learning that is differentiated by learner choice, rather than by the imposition of the governing body or the expert referee (Hawkey 2002, p.5).
It is little surprise, therefore, that the UK government has been keen to adhere to the notion that technology is a means through which to free learning from those characteristics that have made it traditionally unattractive or inaccessible to large sections of the population. To this end the present New Labour government in the UK has introduced a host of technology-based lifelong learning initiatives under the aegis of ‘learndirect’ and 'UK Online'. Learndirect most prominently takes the form of a telephone-based helpline for directing individuals to approved and kite-marked learning opportunities as well as providing its own technology-mediated learning opportunities via a network of over 7000 ‘UK Online’ centres in community sites throughout the UK. The initiatives not only aim to widen participation, but to reduce the current inequalities in participation amongst those groups traditionally under-represented in adult education, i.e. women, the elderly, some ethnic minorities, those on low incomes, ex-offenders and people with learning difficulties (see Selwyn 2002).
Through initiatives such as ‘learndirect’ and ‘UK Online’, the UK government has firmly stated its faith in ICT to establish an inclusive learning society (although other commentators view the University for Industry, in particular, as an initiative whose effectiveness presupposes the acquisition of relevant skills, Winch and Clarke 2003). Moreover, these initiatives coupled with the ever growing rates of domestic and work-based access to ICT are prompting politicians and educationalists of all persuasions to make wide-ranging claims about the promising combination of adult education and ICT as at last overcoming existing social inequalities and leading to a ‘renaissance’ of lifelong learning in the UK (see Selwyn et al. 2001). For some, therefore, the ability to learn with and through ICT has solved the lifelong learning conundrum in one fell swoop:
E-learning is a relatively new tool with the potential to radically improve participation and achievement rates in education. Benefits include; the ability to customise learning to the needs of an individual and the flexibility to allow the individual to learn at their own pace, in their own time and from a physical location that suits them best. This could be in their local library, at their work or at home. Through e-learning we have the opportunity to provide universal access to high quality, relevant training and education (DfES 2002a, p.4 - emphasis added).
Questioning the ICT-based Adult Learning Revolution
Although offering great potential to adult education, there are some serious caveats that the present e-learning agenda faces. The substantial claims made on behalf of ICT-based adult learning are often speculative with very little, if any, sustained evidence of likely success in actually widening participation to those social groups previously excluded from learning. Indeed, there has been some suggestion that ICT may not be as transformatory as the politicians and technologists suppose, and that an emphasis on technology-based learning may actually reinforce existing barriers to education and even create new barriers (Gorard & Selwyn 1999). Crucial to this thesis is the supposition that the consumption and take-up of technologies is not uniform across the population and that there are individual and local contingencies and specificities which work alongside global influences in people's consumption of ICTs (Williams 1999). ‘There is an increasing assumption that access to the internet can be taken for granted and an increasing number of information and service providers only work via the internet... This is quite dangerous to democracy as it increases social exclusion both explicitly and implicitly’ (Sargant and Aldridge, p.102). According to this NIACE review, in early 2002 only around 41 percent of the population had regular access to the internet, and this figure is quite consistent across a number of studies – both official and academic.
Indeed, if we disengage ourselves from hyperbole for a moment, it becomes apparent that there is little substantial evidence to either support or refute claims that ICT-based adult education will facilitate an inclusive ‘learning society’. Studies of participation (and non-participation), such as the new one described below, are therefore described by Sargant (2000) as being crucial to promote genuine inclusion. She points out that a number of large-scale studies such as those from Glass (1954) to Gorard et al. (1999a) have shown that the determinants of participation, far from being easily fixable, are long-term, and rooted in family, locality and history. But, despite the millions of pounds being invested we know little of the extent to which access to home and community-based ICT is contributing to formal and informal learning amongst adults in the UK. As Summers (2002, p.12) recently lamented, “measures are in place that will act as an incentive to learners but the appropriate means of measuring their progress is still absent”. In particular, very few large scale analyses have been carried out examining the success (or otherwise) of the recent ICT-based educational initiatives as well as the impact on adult learning of the domestic proliferation of ICTs into people’s homes. Summers (2002) also points out that existing measures of participation tend to concentrate only on those ‘high achieving’ adult learners rather than those apparently mundane forms of non-credentialised activities which are nevertheless important learning episodes. Aside from these educational issues, our empirical knowledge in terms of technology is equally as indistinct. For example, as Rideout (2000) observes, a major problem with much research on adults’ use of technology is that it provides us only with simple measures of household access to technology - ignoring issues of public community access to ICT and lacking a community level of analysis which the government’s UK Online model is fundamentally based upon. It is clear, therefore, that we need to develop a detailed evidence base of how ICT is impacting on patterns of participation in adult learning in its broadest sense - asking who is using ICT to engage in formal and informal learning as well as who is not using ICT to engage in learning.
Given these present empirical gaps, this paper will now consider to what extent can ICT be said to be contributing to the development of the UK as an inclusive ‘learning society’ by addressing the following research questions:
1. Who among the UK adult population are participants in adult learning experiences, and how do they differ from those who are not participants?
2. What are the determinants of participation in adult learning?
3. Can access to ICT be said to ‘create’ adult learners and learning?
Research design and methods of data analysis
These questions are examined, initially, by drawing upon household survey data that was collected in a multi-phase study of the patterns of ICT use by adults in England and Wales. A structured-interview instrument was administered by a university-based commercial research organisation during the summer and autumn of 2002 in four local authorities in the west of England and South Wales. These were selected as representative for population density, economic activity and levels of educational attainment of England and Wales local authorities, and are briefly characterised below:
· Blaenau Gwent - Mining communities in the South Wales valleys with relatively impoverished levels of economic employment and education
· Cardiff - an urban area, typical in many ways of an administrative capital city with considerable polarisation in terms of education and income and some ethnic diversity;.
· Bath and North East Somerset – a mixed urban/rural area, including the city of Bath and the surrounding rural north-east area of Somerset. Polarised in education and income, and with the added advantage of having been well resourced in terms of public ICT access;
· Forest of Dean - a predominantly rural area, with high levels of poverty in some parts. The area has been used in previous studies as an English comparator for similar localities in South Wales.
The final sample comprised 1,001 adults, and the age distribution was 352 respondents aged 60 and more years, 319 aged 41-60 years, and 330 aged 21-40 years. The primary response rate was 75 percent. Within the sample, 41 percent (n=405) were male and 59 percent (n=596 female), 92 percent (n=917) were classified as ‘white’ and 8 percent (n=84) classified as ‘non-white’. The age range of adults spanned 21 to 96 years with a mean age of 52 years (standard deviation 18). According to the 1991 local census returns (2001 figures not available at the time) for these areas, the sample over-represents female respondents, but is otherwise a fair representation of the population of study (see Madden, Selwyn and Gorard 2002 for further details of the sampling and survey administration procedure). The structured-interview instrument was 36 pages long and consisted of items covering detailed demographic details relating to the respondent and family, compulsory and post-compulsory educational histories, employment life histories and details of current and past ICT use at home, work and in community sites (see www.cf.ac.uk/socsi/ict).
The responses to these questions are described in terms of frequencies, cross-tabulations and, where appropriate, means and standard deviations. In addition, logistic regression analysis with forward stepwise entry of predictor variables was used to ‘predict’ or ‘explain’ the various patterns of individual participation. The dependent variable, to be explained or predicted, is the lifelong form of participation. Non-participants are those who reported no episodes of education or training since leaving school at the earliest opportunity. Transitional learners reported at least on episode of immediate post-compulsory education or training and nothing subsequently. Delayed learners reported no episodes of immediate post-compulsory education or training but at least one subsequent episode as an adult. Lifelong learners reported at least immediate one episode of post-compulsory education or training and at least one other episode. These patterns are also summarised in two binary variables – immediate and later participation. The independent variables, or potential determinants of participation, are entered in batches in the order that they occur in the individuals life (this is instead of the more usual procedures of either entering all variables in one step, or stepwise in the order of the amount of variance they explain).
The variables entered at birth were age, sex, place, and family background. The variables entered in the second stage were the nature of schooling, age of leaving full-time education, and first occupation. The variables entered in the third phase were modal occupational class, employment status, areas of residence, and own family. The variables entered in the fourth phase were the reported access to various technologies, including the Internet. At each stage we also examined the impact of these variables in interaction. In this way, the variables entered at each step can only be used to explain the variance left unexplained by previous steps, and are selected by using the Likelihood Ratio statistic. Thanks to this method of analysis, which models the order of events in individual's lives, the relevant variables become valuable clues to the socio-economic determinants of patterns of participation in adult learning.