It is now common to see traditional, in-person, contact-based courses supported by Virtual Learning Environments (VLEs) such as Blackboard or Moodle, and incorporating the use of participatory web-based technologies such as blogs, wikis, discussion boards, chat servers, podcasts (audio and video), social networking sites, online multiple choice tests, interactive demonstrations and even immersive simulated environments such as Second Life. The drive towards the use of electronic resources to support learning makes two assumptions: first, that the technology actually does support learning, and second, that students will actually use these tools.

From a pedagogical perspective, student use of technology-mediated learning materials is important because of the impact it has on achievement of learning outcomes. Carini, Kuh and Klein (2006) showed that ‘the extent to which students devote time and energy to educationally purposeful activities’ (p. 4) was in general positively associated with educational outcomes (though with differences in the benefit obtained according to factors such as ability, student level and institution). Thus, one might expect that devoting time and energy to electronic learning resources would similarly have desirable outcomes. However, not all students may be comfortable with these pedagogical methods.

There is evidence that some people are more likely to make use of information and communication technologies (ICT) than others. Indeed, a very large literature has built up around the Technology Acceptance Model (TAM; Davis 1989) and a number of other models related to it. These seek to explain user adoption of technology in terms of, among other variables, its perceived ease of use and perceived usability. The key point here is that psychological variables influence the extent to which people use technology – for example, internet-mediated learning support tools.

Kennedy et al. (2008) report that among first-year undergraduate students in Australia, there was variability in familiarity with, and use of, online resources. They argue that ‘While some students have embraced the technologies and tools of the ‘Net Generation,’ this is by no means the universal student experience. When one moves beyond entrenched technologies and tools (e.g. computers, mobile phones, email), the patterns of access to, use of and preference for a range of other technologies show considerable variation’ (p. 117).

This variance in use of online tools is clearly demonstrated in Hoskins and Van Hooff's (2005) examination of student use of a VLE. They found that there was a great deal of variance in how many times individual students accessed the site, ranging from 1 to 310 visits over the course of a 12-week semester.

The important issue here is that many people who have the opportunity to benefit from an Internet-delivered service of some sort choose not do so, make only token attempts to do so, or cease their involvement (drop out) after a period of time. In the context of education, this means that learners who disengage from the technology may miss valuable learning opportunities. If personal characteristics influencing technology adoption are identified, this raises the possibility of either providing learning experiences more suited to the individual's preference (e.g. offline classes), or training the individual so that they are better able to take advantage of the online experiences on offer.

One theoretical construct that has been used to explain differential engagement with online services is that of Internet self-efficacy (ISE) (e.g. Eastin and LaRose 2000). The notion of self-efficacy, put forward by Bandura (1977) revolves around belief in one's ability to perform a given task. If a person has high confidence that they can do it, they are said to have high self-efficacy in that sphere of activity. Self-efficacy may affect both likelihood to attempt the task, and emotional reaction to it. Self-efficacy is best thought of as domain specific (i.e. beliefs about one's capability at certain types of tasks) rather than generalised (where people might believe they were good or bad at all tasks). It has been argued that people have such beliefs about their ability to use the Internet, and a number of questionnaires have been developed to measure ISE. Scores on these measures have been found to affect engagement with online services of various kinds. For example, Hsu and Chiu (2004) found that ISE was positively associated with electronic submission of tax returns. Whitty and McLaughlin (2007) found that undergraduates’ self-reports of using the Internet for entertainment purposes were positively associated with levels of ISE.

ISE is closely related to an important variable in a family of models dealing with technology acceptance. In the classic TAM formulation (Davis 1989), two key variables – perceived ease of use and perceived usefulness – influence attitudes towards technologies, and in turn behavioural intentions to use them and actual use. While more recent variants and alternatives to the TAM have proliferated, they tend to retain the perceived ease of use construct as an important factor.

The relevance of the technology acceptance literature to the current research design is that ISE is likely to be strongly linked to perceived ease of use of internet-mediated tools. Park (2009) found strong evidence that ‘e-learning self-efficacy’ predicted perceived ease of use of e-learning systems. Thus, we would expect that students with higher levels of ISE would perceive VLEs and other online learning tools as easier to use. They should therefore have more positive attitudes towards them, and ultimately higher levels of use. Importantly, there is evidence that training can increase ISE (Torkzadeh and Van Dyke 2002). Given the findings outlined above, we would expect that increased ISE should lead to increased use of online services.

These ideas are very relevant to the Higher Education context. It may be the case that students who are reluctant to take full advantage of e-learning activities, are reluctant because they have low levels of ISE. There are indications that constructs related to ISE do influence attitudes and behavioural intentions towards use of e-learning. For example, Liang, Wu, and Tsai (2011) found that ISE was associated with positive attitudes towards web-based learning programs in nurses. Hartshorne and Ajjan (2009) found that levels of self-efficacy around use of Web 2.0 applications influenced students’ perceived behavioural control, which in turn had a positive influence on behavioural intentions to use such tools to supplement in–class learning. If these attitudes and intentions translate into actual behaviour, then appropriate training programs could boost levels of ISE. This in turn should lead students to – in Carini, Kuh, and Klein's (2006) terminology – devote more ‘time and energy to educationally purposeful activities’ (p. 4), mediated by a VLE, with positive effects on educational outcomes.

The aim of the current project was to examine the relationship between ISE and use of educational technology. It builds on work demonstrating links between ISE and behavioural intentions, by examining actual behaviour. To maximise ecological validity, we measured students’ actual use of technology in an educational setting. We hypothesised that ISE would be positively associated with use of various components of a VLE used on the course in question.

Study 1 was intended to examine links between ISE and student use of educational technology. The study was conducted during a first-year undergraduate lab class dealing with Basic IT Skills for Psychology. As part of the course students were introduced to various forms of educational technology, accessed through the VLE (Blackboard) used by the institution. These included blogs, wikis (both commonly cited as examples of Web 2.0 tools) and a discussion board that were each used in different ways.

Students were required to keep a personal blog detailing their experiences in class. This was used as the basis for one of their coursework assessments, a reflective log which they submitted at the end of the course. Another of the coursework assessments was a group exercise, creation of a PowerPoint presentation. Groups of students were assigned to work collaboratively on this over a period of time, using wikis that had been set up specifically for that purpose.

Study 2 was again conducted during a first-year undergraduate lab class dealing with Basic IT Skills for Psychology. The study took place 1 year after Study 1, so while the same course was used, there was a different cohort of students.

The findings of these two studies do not suggest that ISE is an important influence on use of online technology in the educational field. It does appear to have an impact on (self-reported) online behaviour, at least in terms of the time spent online each week. However, it appears that this time spent online is not reflected in those educational activities whose use it was possible to monitor. It is thus more likely to represent personal rather than academic Internet use, or academic work outwith the bounds of the VLE. Furthermore, ISE does not appear to influence students’ attitudes toward learning technologies.

These findings bear a mixed resemblance to those of previous research. Liang, Wu, and Tsai (2011) finding of a link between ISE and attitudes to web based courses was not replicated. However, as discussed earlier in this paper, important differences between the current work and theirs may account for the discrepancy. Hartshorne and Ajjan (2009) found that self-efficacy beliefs around Web 2.0 technologies were an important component of perceived behavioural control (that is, people who were more confident were more likely to think that they could use the tools effectively). However, this was only weakly linked to self-reported intentions to use the tools. Furthermore, Hartshorne and Ajjan (2009) report that ‘while students acknowledge the pedagogical benefits of Web 2.0 applications in higher education, there is limited use of these tools to supplement instruction in their courses’ (p. 196). All in all, this is consistent with the current finding that ISE did not influence engagement with blogs, wikis or discussion boards in the context of this course.

There was evidence that attitudes towards wikis and discussion boards were associated with level of use. The direction of causality cannot be ascertained from the present study. However, the finding is consistent with the notion, derived from the TAM, that perceived usefulness of a technology will predict its use (at least one of the attitudinal items directly asked about usefulness). Failure to observe the same relationship between attitudes towards, and use of, blogs may arise from the fact that blog use was compulsory and constrained by instructions to the class: they were expected to make a blog entry each week. Thus, the proportion of variance in blog entries accounted for by other factors is necessarily limited.

Study 2 indicated that training in digital skills does increase ISE. However, there is no evidence that this change is reflected in actual behaviour within the context of the course. This may lead educators to question whether it is worth pursuing such interventions.

One possible reason for the failure to find links between ISE and number of interactions with the online materials is that variables other than those measured may influence these outcome measures. For example, people who have problems dealing with technology (i.e. low ISE) might also need to spend more time on their work, correcting mistakes and so on. However, while this argument would apply quite strongly to wiki edits, it is less clear that it would apply to the number of blog entries posted or number of times a discussion board was accessed.

On the basis of the current findings, it would be premature to conclude that ISE does not influence use of Internet technologies for learning at all. The current studies examined use of specific technologies within a formalised learning environment. However, this does not address on the question of what might motivate people to use technology to support learning in informal settings. A prime example here is the phenomenon of MOOCs (Massively Open Online Courses) such as Udacity, Coursera, edX and many others. Learners engaging with these largely choose to do so voluntarily, and it is entirely possible that ISE and other personal traits could have an influence on such choices. The same is true of other technologies – different types of social media, for example, that people could leverage in creating their informal personal learning environments.

Another question might be asked about the present conclusion: is there sufficient statistical power to support the assertion that these variables are not related? Admittedly, sample sizes are small, and with greater power there would be a better chance of finding small effects. However, the answer advanced here is a pragmatic one. The samples used here were typical of the class sizes taught in many Higher Education institutions. If ISE has no discernible effect on behaviour or attitudes among such cohorts of students, then even if a small effect does exist it is unlikely to have any practical significance for educational practice.

In short, there is evidence from a number of fields that ISE can affect adoption of online tools and technologies. However, we found that ISE had no effect on either use of a VLE or attitudes towards its components. While we found that appropriate training led to significant increases in ISE, this appeared to have no effect on the use of technology. Therefore, educators who wish to boost student use of the facilities provided in online or blended learning courses might do better to examine other factors and interventions.