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Conceptualizing differences in learners’ experiences of e-learning

Conceptualizing differences in learners' experiences of e-learning: a review of contextual models

Report of the Higher Education Academy Learner Difference(HEALD) synthesis project

Dr. Rhona Sharpe

Oxford Brookes [email protected]

With thanks to Helen Beetham, Greg Benfield, Fred Garnett, Vivien Hodgson, Julie Hughes,

Mary Lea and Martin Oliver

July 2010

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CONTENTS

Background.............................................................................................................3Aims and scope.................................................................................................................................................... 3

Concepts and models..............................................................................................7Digital society....................................................................................................................................................... 8

Young people's use of technology .................................................................................................... 9

The Net Generation ............................................................................................................................... 9

The digital divide.................................................................................................................................. 10

Pedagogy and the local environment......................................................................................................10

Community of inquiry......................................................................................................................... 11

Critical pedagogy: participation in hetereotopian spaces..................................................12

Spaces and tools................................................................................................................................................12

Negotiated learning spaces.............................................................................................................. 13

A framework for mobile learning..................................................................................................14

Learner Generated Contexts............................................................................................................ 14

Maturity models................................................................................................................................................15

Information literacy: a model of responses and influences ...............................................16

Developing effective e-learners...................................................................................................... 17

Conclusions...........................................................................................................18In what ways do learners differ?...............................................................................................................18

The role of models in explaining learner differences.......................................................................18

References............................................................................................................20

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BackgroundLearner experience research has developed rapidly in the last few years. The JISC has supported a programme of carefully designed studies in post-compulsory education that have collected in depth, rich descriptions of learners’ beliefs and expectations of technology1. Many of the Higher Education Academy Pathfinder studies promoted institutional research including large-scale surveys of learners’ experiences, with the aim of influencing institutional practices (Sharpe 2010). New ways of eliciting, capturing and analysing learners experiences are being developed and there is a growing confidence in applying these methods. There is a large and growing community of researchers at ELESIG2 who are sharing how they are investigating and evaluating learners’ experiences.

This fast growing field set out to show how introducing technology changes the experience for the learner (see Sharpe, Beetham & de Freitas 2010 for review). For example, Holley’s study of widening participation students in an inner city university which shows that ‘providing online resources changes the places where [learners] study, and also how the individual is able to re-conceptualize their individual learning space’ (Holley 2009 p.13). There has been a shift in how such individual experiences are captured and reported, with the development of methods that engage learners in sustained data collection and elicit their holistic experience of learning in a digital age. These methods allow us to see the experience of learning with technology from the learner’s perspective and give rise to vivid, compelling stories.

However, as more stories have been collected from individual learners, it has become clear while they are a few general findings that apply to ‘the learner’, there is a more complex reality where technology changes the experiences of learning in different ways for each individual student. We know that there is enormous variety in the data and we have moved from talking about the learner experience to learners’ experiences (Sharpe 2008). Now we have a collection of a large number of learners’ voices, we cannot help but notice how different they all are. While some students are using technology in ways that help them study and learn, other students find technology to be an obstacle to their learning (Sharpe et al. 2009).

The field is at the early stages of developing conceptual accounts to help us make sense of these complex and sometimes contradictory findings. There is a need to be able to explain what accounts for differences in experiences between individual learners. This review aims to identify conceptual accounts that explain differences between learners in their experiences of e-learning. It is this modeling and problematising of the findings, which takes us beyond relating the stories of the individual learners, to be able to make recommendations with confidence.

Aims and scopeThis was a highly focused review, accessing only literature that relevant to the theme explaining learner differences. The review did not synthesize current empirical research. Instead it reviewed conceptual accounts which have explanatory power, i.e. those account which could be used explain individual differences in learners’ experiences of e-learning. The review does not intend to be comprehensive and has 1 See JISC Learner Experiences of E-learning Programme outputs at https://mw.brookes.ac.uk/display/jiscle22 See http://www.elesig.net

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identified only a small selection of possible conceptual accounts. However, it is hoped that it will be a starting point for theoretical developments in this field.

Alongside a traditional desk based review of the peer reviewed academic literature, the review began by drawing together a group of experts in the field to scope the review and identify relevant sources. This group attended a start up meeting in July 2009 to clarify the aims of the review.

The starting point was to consider why it is necessary to understand learner differences. Clearly the large investments that the sector has made in technology is an important consideration. Holley (2009) is one of many who have argued that given the investment in ICT (especially virtual learning environments) in higher education, it is surprising that the individual learner experience is so underreported. This is changing and now that improving ‘the student experience’ is an explicit aim for many in higher education, what is surprising is how poorly understood it is. Furthering our understanding of the impact of the investment that has been made in technology enhanced learning on individual learners, would enable us to:

level individual differences develop learners have an influence (having determined what differences we can influence) and, provide differentiated teaching for technology mediated and/or online

contexts

Clearly then this review is not only about promoting theoretical developments. There is a noticeable emphasis on practical outcomes. A valuable function of this review would be highlighting what differences are important for teachers to understand. It is likely that these would be the differences that have most effect. This is a theme that is returned to throughout the review.

Second, the expert group discussed at length the extent to which there is a need for this review within the context of e-learning. There were considered to be a number of reasons why it was important to locate the review specifically within the field of technology enhanced learning. Although there is much useful literature from mainstream education, technology enhanced learning was considered significantly different to warrant a separate approach. This led us to asking, in what ways is technology enhanced learning different? Suggestions included that technology enhanced learning:

takes places in panoptical environment; conceptualizes learning as social constructed knowledge building uses participative pedagogies e.g. collaborative learning, networked learning,

reflective learning and assessment e.g. peer assessment; blurs the boundaries between formal, non-formal and informal learning; identity construction is formed through dialogue and performance in particular

settings in response to personal agenda and perceived audiences.

Technology is different enough to be disruptive, to disrupt how students behave and learn. So, although we are interested in the methods that educational research has used to conceptualize learner differences in general, the review should start with a blank page of findings. If we start from today’s digital world, we might find that what influences learners now is very different indeed. We extend our aim then to review existing explanations for understanding learner difference within the digital domain.

Finally, the expert group’s discussion focused on the need for an understanding of learner difference to have practical implications. We asked, in what ways could an

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evidence informed understanding of learner differences to technology enhanced learning help us practically? If there is an understanding of who succeeds and why they succeed, it should be possible to adapt institutional provision towards increasing the chances of all succeeding. More is known about such success factors for some groups of students than others. The JISC LEX final recommendations reminded us that just knowing that learners’ experiences are diverse is not enough, we need to identify the barriers and meet the needs of diverse student groups, such as international, work based, disabled and part-time students (Sharpe et al. 2009). The learner difference review should be able tell us if we should be asking for example about access, how learners find resources, how they access university services or make use of peer networks? We could help teachers know what questions to ask and understand what the likely enablers and barriers for technology enhanced learning are going to be for their group of students. Ultimately, if learner difference is better understood, this should lead to advice on differentiated teaching, personalization, engaging all learners in collaborative pedagogies, even transition, progression, pathways, and skills development. While it is recognized that this review will not take us this far, it is important to note that the purpose of understanding learner difference is so that learner development can be better supported.

The reality is that any individual’s experience is formed by a complex interplay of individual differences, maturity, and contextual factors. Our previous attempts to explain learner difference concluded that while there is some evidence of learners developing in the sophistication of their technology use over time, it is the contextual factors that are most important (Sharpe et al. 2009). We saw that learners’ technology use with primarily a response to meeting their own individual needs and the demands of their context. Seeking conceptual accounts of ‘context’ is a particular focus for this review.

Within the scope of this small scale review, and knowing what we already know, it is not the purpose of this review to undertake a comprehensive meta-analysis of primary research in an attempt to determine which factors account for the differences in the ways learners respond to the use of technology in higher education. Rather, what we hope to be able to say is that, of all the different ways in which learner difference is understood, we think that these are the important ones to look at because (a) there seems to be most evidence to support them and (b) it is easy to see what the practical implications are.

So, the final drafting of the aims of this review is: to review existing explanations for understanding learner difference within the

digital domain to present the findings in ways which are of use to practitioners.

For this reason the review outputs are (a) a concept map (Figure 1)3 representing how existing concepts, models and theories explain learner difference, and (b) short summaries of each of the models reviewed, with suggested follow up readings. The hope is that these will be a starting point for others to add to.

3 The map was created in CMapTools, available to download from http://cmap.ihmc.us/conceptmap.html

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Figure 1: Conceptual accounts to explain differences in how learners experience technology enhanced learning.

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Concepts and models

The centre of map distinguishes between concepts and models, as shown in Figure 2 below. A concept is a broadly stated belief about why learners experience technology differently. Here four broad conceptions of the influence of context on learner difference form the centre of the map. (Beyond our interest in context, other conceptual accounts are possible such as: ‘stable individual differences between learners cause them to respond differently’ or ‘people learn or conceive of learning differently’).

Figure 2: Concepts at the centre of map of conceptual accounts that explain differences in how learners experience technology enhanced learning.

A model is a derivative of a concept, which has been developed and investigated by researchers. Two or three examples of models have been identified for each concept and placed on the map. So for example, Figure 3 shows that two seemingly different types of models: pedagogic practice and critical theory are derived from the same underpinning concept that learner difference can be explained by individual responses to the local pedagogic environment. This is an important point that influenced the choice of models for inclusion. Our interest is in conceptual accounts of an overarching problem (learner difference), not in models produced as a way of succinctly arranging large amounts of data.

Figure 3: An illustration of the relationship between concepts and models

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For each model, a short summary is presented. An attempt has been made to reach a judgment about how suitable each model is for explaining differences in learners’ experiences to technology in education, drawing on an understanding of how the model was developed and its relevance to this review. A more convincing judgment would be made by reviewing the evidence for each model. There has not been conducted within this a review of this scale, but could perhaps for a focus for future work.

Finally, the review has deliberately highlighted models that are less well known in this field and those which are just emerging. The expert group and the wider ELESIG community have been invaluable in making suggestions for models to consider.

Digital society The digital society models are derived from the conception that differences in experiences can be explained by the changing role of technology in society (see Figure 4). Although popular at one time, empirical evidence to support the ‘net generation’ models has not been forthcoming. The anecdotes and observations that children and young people have some natural affinity with technology have not translated into the arrival at university of young people competent in using technology to support their learning. These models are included in this review as a valuable reminder of the need to search for evidence. They have been replaced by a reasoned study of young people’s use of technology. Studies of young people’s use of technology, similar to recent studies of students in higher education, confirm that there are both majorities and minorities hidden within the ‘typical’ descriptions of young people.

Figure 4: Digital society models and their underpinning conception

This section also includes an emerging modeling of recent data concerning students’ access to technology. Wei et al’s (forthcoming) model may or may stand the test of time, but is included as a reminder of the need to update our understandings of ‘access’ in light of (a) the widespread availability of networked personal computers in the developed world and (b) the differences in access to technology and connectivity between the developed and the developing world.

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Young people’s use of technologyThere is no doubt that children in the developed world spend a great deal of time in front of a screen. Studies show that mostly this is spent in low key activities: gaming, retrieving information and maintaining local social networks. Crook (2008) refers to such activities as ‘low bandwidth exchange’. There are also examples of a minority of young people who are using technology in creative and innovative ways (e.g. Green & Hannon 2007) and at the other extreme young people who do not participate either because they are disenfranchised or make a decision not to engage (Boyd 2008). However, for the majority, their use of the technology is not prompting them to new ways of shared production or creation of knowledge. The evidence is that young people are not intuitively making use of the educational affordances of the technology.

The consistent finding is that most young people’s use of technology takes place outside of school. Indeed their exposure to the use of technology through education is frequently narrow and limited. Any discussion of learner difference then needs to look beyond the educational setting to learners’ use of technology for social purposes and entertainment. Indeed, this is where it gets interesting. Buckingham (2007) notes that ‘the use of technology in schools may serve to accentuate inequalities between students rather than ameliorate them’ (p. 96). The point being that students with good internet access at home, skills and values, will be even better using technology at school than those with such advantages. Facer and Selwyn (2010) suggest that social networking sites are important places for identity production in students arriving at university and it might therefore be appropriate for higher education institutions to ‘remaining purposively unaware’ of how students are using these technologies.

Rather than a characterization of young people who are intuitively engaged with new technology, research from this field should be a warning not to make assumptions about learners’ digital competencies and literacies when they enter higher education.

The Net GenerationThe Net Generation models go further than studies of young people’s uses of technology to claim that groups of people who have grown up at different times have different preferences for learning, and that these are due to their early experiences.

In a pair of short opinion pieces published in 2001, Marc Prensky distinguished between ‘digital natives’ who have grown up with technology and ‘digital immigrants’ who discover technology later in life (Prensky 2001a, 2001b). These two groups are described as having different styles of learning. Digital natives prefer interactivity, graphics and rapid changes in attention.

Don Tapscott (1998) has been similarly specific about the generational differences on how people use technology, with people born after 1977 belonging to a digital generation where the Internet is dominant. In order to illustrate the characteristics of this generation, they are compared with a previous ‘television generation’. These are seen as completely different e.g. where TV is passive, the Internet is active. The Net Generation vary in many ways from the older generation, with claims made for a wide range of social and psychological differences.

Oblinger and Oblinger (2005) wrote about the implications of these young people arriving in higher education and questioned the extent to which higher education would need to adapt to meet their expectations and needs.

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In a review of the academic literature, Bennett (2008) challenged the claim that young people think and learn differently because of their exposure to technology. The claim that a generation of young people prefer to multitask, to shift attention rapidly and receive information visually, is not supported by empirical evidence. Similarly, the suggestion that young people have a different preference or style of learning does not fit well with the learning styles literature which has now recognizes that styles are difficult to measure and liable to change over time (Coffield et al. 2004).

The ‘net generation’ framework is not supported by research evidence and as such, is not suitable for helping us understand differences in how learners experience technology supported learning.

The digital divideDespite the generational models having little value for this review, the place of technology in society may have other effects. In particular, learners’ experiences of technology may be influenced by the uptake of technology in society. Differences may be explained by some people having access to society’s technology while others do not. The digital divide used to be conceptualized as such an access issue – with groups divided by the access to ICT.

It is now understood that the divide has moved. Having access is not enough. For example, Lebens and Mayer (2009) in study of school age children in Germany found that while most children had access, those from lower socio-economic status were still disadvantaged. This was thought to be due to ‘the extent of support that low SES children receive from their social network and prevailing stereotypes with respect to ICT proficiency’ (p.255). This important role of socio-economic status (SES) is confirmed by Hargittai (2010) who finds that even after controlling for Internet access, socioeconomic status is an important predictor of how the Internet is actually used in daily life.

Wei et al (forthcoming) present a social cognitive model of the digital divide with three levels: access, capability and outcome. The first divide, access, as we might expect, refers to access to technology in homes and schools. The capability divide refers to an individual’s capability to exploit the technology to which they have access. The final divide is a difference in outcomes (e.g. learning and productivity) of exploiting the technology to which there is access. The model is based on data from over 4000 students in Singapore, which confirms the hierarchical nature of the levels of access. For example, even when students are provided with technology in schools, it does not dissipate the differences from having or not having access at home.

Pedagogy and the local environmentThere are many models of online teaching that aim to improve the student learning experience, such as networked learning (Goodyear et al. 2004, Goodyear 2005) and the conversational framework (Laurillard 2002). They are based on a fundamental belief that the teaching environment will influence the student experience. However, despite an initial rush of enthusiasm in the 1990s, the reality is that not all students respond positively to the changes in pedagogy found in online learning. The question is whether pedagogy practice models can help us better understand how and why individual students react to e-learning (see Figure 5).

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Figure 5: Models of pedagogy and their underpinning conception

Garrison and Anderson’s community of inquiry framework is chosen as an example. This shows that although the original model was developed from a teacher centric view, typical of the time, the subsequent research it has prompted does go some way to explaining learner differences.

Perhaps more interesting, and certainly less well known, are the critical pedagogy conceptual accounts. These give voice to experiences that tend to be under-reported and could offer a way of conceptualizing the reality of supporting online learning groups in which learners respond differently to changes in power and control.

Community of inquiryGarrison and Anderson (2003) set out to devise a conceptual framework for the implementation of e-learning. It is based on the premise that higher education should offer students the opportunity to engage with a community of learners, and that technology can be applied to help achieve that goal. Engagement with such a community promotes critical reflection and inquiry. They go on to identify three key elements that should be considered when planning an e-learning intervention: cognitive presence, teaching presence and social presence.

The model provides guidance for course designers and teachers and, in general, refers to students as a homogenous group. This model arose in a time when teacher centred guides were dominant. Where there is mention of student diversity in the conceptual framework, it is related back to the role of the teacher, such as the recognition that not all students will be comfortable working online and the resultant need to clarify expectations of participation (Garrison & Anderson 2003, p.81). However, subsequent studies have accessed the learner perspective. Arkyol and Garrison (2008) confirmed expected relationships between presence and perceived learning and satisfaction. Cleveland-Innes, Garrison & Kinsel (2007) collected comments from learners on their early experiences of, and adjustment to, online learning.

In terms of the suitability of this model for explaining differences in learners’ experiences of e-learning, it is based on a clearly expressed theoretical perspective and informed by empirical research (largely based on the coding of transcripts from computer conferences). It is presented from the teacher’s perspective with a primary function to offer guidelines to improve practice.

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Critical pedagogy: participation in hetereotopian spacesIn contrast to the pedagogic practice models which promote the benefits of online learning for all, challenges have come from critical theory to the presumed utopia of critical reflection, collaboration and participation. These suggest that when applied without reflexivity, collaborative pedagogies may be perceived by learners as oppressive and dominating (Reynolds 1999), and learners may express their feelings of alienation within a learning community (Brookfield 1994). These approaches recognize that changes in pedagogy are not value free and explicitly consider the impact for learners of changes in power, control and participation.

Ferreday and Hodgson (2008) use Foucault’s notion of heterotopian spaces to examine online spaces, recognizing that the pedagogy will disrupt, rather than empower, the learners’ experiences. Working from this viewpoint allows them to explore experiences that tend to be under-reported such as the pressure for learners to behave in similar ways and the frustration when participation is not reciprocated. They go on to consider why some learners’ behaviour does not meet the expectations of the learning community, discussing the influence of taking on new multiple identities, including cultural identity. They posit that some learners ‘don’t necessarily have the capacity, understanding or … the inclination to conform to a normative and inflexible view of participation’ (p.644).

Such frameworks could offer a way of conceptualizing the reality of supporting online learning groups in which learners respond differently to changes in power and control.

Spaces and tools

Figure 6: Models of spaces and tools and their underpinning conception

Where the pedagogic practice models saw a fairly direct relationship between environment and response, the set of models collected under the ‘spaces and tools’ heading attempt to conceptualize a much more complex picture of interactions

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between people, place and technology (see Figure 6). They make use of Activity Theory to get to grips with the many influencing factors that could be considered under the heading of ‘context’ (see Engeström 1999). In brief, these models stem from the concept that learners’ experiences are a result of a complex interplay between the spaces students inhabit, the tools they use and the people they interact with.

Negotiated learning spaces Holley offers an empirically based framework for analysis with three themes: learning, technology and space, of which space is perhaps the most interesting. Using biographical narratives which foreground the student view, she explores the personal spaces which learners create for learning, recognizing that learners do much to create their own spaces. The framework recognizes the complex reality of the learning experience e.g.

The two studies revealed that there were local and specific practices that defined students’ engagement with the course. As expected, this was not a simple matter of access to technology; instead, these accounts revealed that engagement with education (including engagement with technology used for educational purposes) was shaped by the students’ existing dispositions and social connections. (Holley 2009, p.196-7)

The proposed framework for analysis has three axes: the amount of control learners have over the technology they use for learning, their expectations of the forthcoming educational experience and their preferences for inhabiting private or social spaces for learning. The notion of match or mismatch between the learning needs of the student and those of the module is thought to be important. It is illustrated through the case studies and plotted on the axes, e.g.

“those poorly equipped in terms of previous life skills and educational experiences are most likely to experience a mismatch with the course. For example here we have Kwame who really wants to perform well, but finds barriers to his learning extremely difficult to overcome. (p. 215)

In terms of practical application, it is suggested that plotting students on the graph could be a way of identifying ‘at risk’ students, where low risk students have a good match between their expectation of education and the courses they select combined with high control over their home environment and use of technology (see Figure 7). Conversely high risk students ‘would struggle to use technology effectively and be more likely to claim that their expectations of the course and the course offering were not met’ (p. 218).

Match Low Medium Low

Expectation of education

Medium Medium Medium

Mismatch High Highest High

Private Social

Space

Figure 7: Mapping expectations and space and expectations and control, adapted from Holley (2009)

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In terms of the suitability of this model for explaining differences in learners’ experiences of e-learning, it does have advantages in that it recognizes the complexity of learners’ lives, lived within and outside the educational context. The model arises from conversations with learners, collected and analyzed from the perspective of a well informed phenomenological tradition. It offers most to these studying how students colonize their home working spaces.

A framework for mobile learning In Sharples, Taylor and Vavoula’s (2007) framework, interactions between learning and technology are understood to be complex and varied, with learners making use of whatever technology is available as they move locations. Crucially, it is the learner who is seen to be mobile, rather than the technology. The learner moves from place to place, using different technology as they go. The notion that learning occurs as part of such everyday activities is important. Learning then must be transferred between formal and informal contexts such as between home and work and school. As such, this framework is intended as a challenge to educators to bridge the gaps between formal and experiential learning.

Sharples locates this framework within a literature of informal learning and explicitly builds on Activity Theory in developing a model specifically for understanding mobile learning.

“We separate two perspectives, or layers, of tool-mediated activity. The semiotic layer describes learning as a semiotic system in which the learner’s object oriented actions are mediated by cultural tools and signs. The technological layer represents learning as an engagement with technology, in which tools such as computers and mobile phones function as interactive agents in the process of coming to know.” (Sharples et al. 2005, p.1)

In this system the cultural factors are defined as control, context and communication. In each you can see the interaction between the learners’ actions and the technological development. The example given is of children using informal communication technology (txt, IM, email, phone) while working on homework. Their use of the technology leads to new ways of working (and learning?) and the technology is continually being evolved in response the ways they are appropriating it. Sharples visualises the semiotic and technological layers moving together and apart, pushing both learning and technical developments forward.

In terms of the suitability of this model for explaining differences in learners’ experiences of e-learning, it is written from the learner’s perspective and it does stem from a well informed Activity Theory tradition. It does not yet have an empirical base, although it is starting to generate research.

Learner Generated ContextsThe Learner Generated Contexts group was formed in 2007 in response to the rise in user-generated technology in society (e.g. YouTube, Flickr). They have proposed a framework which links formal and informal learning. A Learner Generated Context can be defined as ‘a context created by people interacting together with a common, self-defined learning goal. The key aspect of Learner Generated Contexts is that they are generated through the enterprise of those who would previously have been consumers in a context created for them’ (Luckin et al. 2007, p. 91).

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The development of the framework stems from a recognition that learners are using technology outside of the educational system, and that this outside use tends to be more creative than inside. This is a tension where students are seen as consumers of content. The idea of learner generated contexts is to shift our perception of learners from consumers and creators, so that we can work with them to create contexts for learning.

The framework builds on Luckin’s (2008) Ecology of Resources which looks at how the resources learners have available to them (people, technology and artifacts) can be organised and utilized in ways which support the learner within their context. As with Sharples’ and Holley’s frameworks, ‘context’ is seen to be broader than the physical space. The context refers to the interactions between the learners and their resources and as such, is different for each individual. Drawing on Activity Theory, context is created through relationships and interactions that take place within a dynamic environment.

This framework is learner centric and ambitious. It is intended to be used to ‘facilitate the development of context-based models as the organising principle for designing learning and [examine] what institutional practices might support or retard their development’ (Luckin et al. 2008, p.1).

Maturity modelsThe models grouped together under the heading of maturity recognize that learners’ experience of technology changes over time. They examine how learners’ relationship with technology changes and how difference could be conceived of as a development in capabilities over time.

Figure 8: Maturity models their underpinning conception

There is already evidence that while learners are in further and higher education they use technology more for learning and become more confident in their use. They develop better information seeking and handling skills and can describe improvements to their organizational management and learning skills (Jefferies,

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Bullen & Hyde 2009). Two approaches to understanding such developments are given here. First, are the models which attempt to identify the significant differences between experienced and inexperienced learners, of which Sharpe & Beetham’s model of creative appropriation is an example. Second, are those models which explain maturity in terms of what it is that needs to be developed, of which the digital literacy models are currently popular.

Developing effective e-learnersSharpe and Beetham have been working on a model which arises from the JISC funded Learner Experiences of E-learning programme. This programme funded ten projects over five years to work with learners from further, higher and work based learning contexts. In total, these projects involved 186 learners in some form of sustained engagement over an extended period, such as interviews, audio or video diaries, or the authoring of case studies. In addition, almost 3000 learners took part in survey based research. The programme aimed to gather thick descriptions of learners’ uses of technology and to understand their technology use in a holistic way. The techniques developed by the projects to engage learners in productive dialogue and record keeping elicited rich descriptions of use and of attitudes and beliefs operating behind their actions. One of the findings from this programme of research was that although there were some students who made good use of technology to support their learning experiences, this is not the case for all. There was a wide variation in use between individuals and some evidence from the longitudinal studies of changes in technology use over time (see Sharpe et al. 2009).

Figure 9: A developmental model for developing effective e-learners (from Sharpe & Beetham 2010)

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The learners’ experiences reported in these studies have been arranged into a developmental sequence which describes how learners progress towards effective use of technology for learning (see Sharpe & Beetham 2010). The developmental sequence emphasizes that attributes of effective learners are built upon a set of technology based practices, which in turn require appropriate skills and functional access to the relevant technologies. At the top of this sequence is the notion of ‘creative appropriation’. Here learners are using available technologies in creative ways (beyond that recommended by tutors) to meet their own goals, managing and personalizing the resources they need. Where strategies have been adopted and practised to the extent that the choices involved become unconscious, they are said to be ‘appropriated’.

Overall, the model emphasis practices, which learners with skills and access can develop, and choices where effective learners can choose which practices to adopt in which contexts, with which technologies. The point is not to place a learner at a particular place in the pyramid. In reality learners do not develop all capabilities at the same rate. Rather, it is intended that the model is used as a planning and self-assessment tool for students, teachers and institutions. This model describes differences well but does not explain them.

Information literacy: a model of responses and influencesThis model stems from a recognition that learners experiences of using online information sources are extremely diverse and likely to become more as students become more culturally and socially diverse. The model is based on qualitative research with 25 international learners using critical incident techniques and draws widely on existing information literacy theory.

Hughes is concerned with information ‘use’ which she defines broadly as “the experience of engaging with online information for learning. This is understood as a multifaceted experience involving users’ context, needs, actions (behaviours, information seeking), responses and influences (cognitive, affective, cultural, linguistic) and outcomes (insight, knowledge construction).” (Hughes 2006, p. 280) This is an important distinction from models which focus only on skills. The broader definition is an attempt to explain the findings that skills are not enough – that learners for example do not always employ the skills they have when needed or apply them uncritically. Personal and cultural factors are included as important elements of this model. Use must be understood not as a stand alone action, but as something with purpose and outcomes. The model has many similarities with action research, being cyclical in nature, with ‘use’ being represented as a dynamic cycle of planning, acting, recording and reflecting. At each of these stages, the learners is subject to ‘responses’ and ‘influences’, where responses are the behavioural, cognitive and affective factors and the influences are the cultural and linguistic aspects. In this way, the model attempts to represent the multifaceted experience of using online information resources. As with the other maturity models, there is an intention that the model is used as a framework for developing information literacy in learners.

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Conclusions

What makes a difference to students’ experiences of technology?This review began with a reminder that young people are not digitally astute by virtue of belonging to a certain generation. In addition, even when young people are provided with technology in schools, this does not dissipate the differences between them which stem from having or not having access to technology within their home. In attempting to understand the differences between learners in higher education, we need to recognize the continuity of the learners’ experience from home to school to university, and to develop frameworks which can explain these influences adequately. Wei et al’s model is worthy of further investigation, with its three levels of: access, capability and outcome. Rather like Sharpe & Beetham’s reporting of the ‘functional access’ arising from the JISC studies they reviewed, these frameworks explain differences as a result of a learner’s ability to exploit the technology to which they have access.

What might help learners exploit technology? Holley found that students’ engagement was shaped by their disposition and social connections. Previous life and educational experiences are important here in equipping students to learn well. Holley’s framework used the notion of mismatches between learners’ expectations of education and their courses, with those poorly equipped in terms of previous life skills and educational experiences are most likely to experience a mismatch with the course.

Moving on, we found models that assume that differences between how learners exploit technology can (and we might argue – should) be influenced by good teaching. Within the context of technology enhanced contexts, the community of inquiry models suggest that good teaching includes demonstrating presence, clarifying expectations, and being aware of new users’ early needs. However, we need a more nuanced understanding of good teaching that takes account of the reality of power and participation (critical pedagogy) and perhaps identifies students at risk (Holley).

The interaction between these learning and teaching environments and learners with all their diverse access and experiences is incredibly complex and its modeling benefits from taking a systems approach. Attempts to model this aspect of learner difference explain how context is formed differently for each individual, as an interaction between the learner and their resources.

It is likely that higher education institutions will need to take on responsibility for supporting learners to develop their abilities to exploit technology in ways which enhance their learning. In order to support learners’ development, it is important that we are clear about our aims. The maturity models showed that differences between learners cannot be explained by looking only at skills and that practices might be a more useful way of conceptualizing this.

The role of models in explaining learner differencesThe focus on contextual factors in this review is necessarily limiting. They are other concepts and associated models which take a different view and which, although beyond the scope of this review, could be explored. Psychological trait theory offers models of individual difference that might be added to the original concept map, such

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as risk taking, sociability and self efficacy (Figure 10). There have also been attempts to typologize learners into groups of those with similar characteristics (e.g. Green and Hannon 2007, Salmon 2000, Currant et al. 2010). We also now that learners’ behaviour and strategies are influenced by their conceptions of learning, including their beliefs about their own competence, their motivation and their understanding of the teaching and learning process (Ellis & Goodyear 2010).

Figure 10. Concepts and models of individual difference

The review has shown that models are useful where they are underpinned by evidence collated through approaches and methods which recognize the actuality of the student experience. They are useful where they arise from conversations with learners, collected and analysed from the perspective of a well informed tradition (such as phenomenological, semiotic or systems approaches). Models need to explore learning from the student perspective, which means taking a whole life perspective. For Ainley (2008), this is achieved by using students to research students building towards an ‘anthropology of the student experience’ (p.621) and recommends observation, interviewing and longitudinal studies.

Wilson (1999, p. 250, cited in Hughes 2006) states: ‘A model may be described as a framework for thinking about a problem and may evolve into a statement of relationships among theoretical propositions’. The role of models is to bring explanatory power, to precipitate further research and ultimately, to lead to practical recommendations. The models reviewed here recognize the problematic complexity of learners’ lives, lived within and outside the educational context. They encourage us not seek simplistic typologies for describing today’s learners but to generate frameworks which can not just describe but explain how people come to be successful learners within the complex context of higher education.

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