This paper conducts a timely exploration of students’ attitudes towards digital technology and how students use technology, using a new instrument designed for purpose. It asks whether mature students have more negative attitudes towards technology than younger students, and how the usage differs between these two groups. It will be of use to educators who are designing resources using technology for use in higher education (HE) classrooms, which are more and more likely to contain mature students as part of a changing cohort. Additionally, it makes a wider contribution to the study of technology attitudes and use, an ongoing field that is continually changing with the evolving technology landscape and a changing student cohort.
This section will introduce mature students as a group, and what is meant by technology enhanced learning. It will then discuss previous studies on students’ attitudes to technology, and finally set out the purpose of the study, including the research questions.
Mature students
Most UK universities define mature students as those who are aged 21 and over at entry, however Lewis (
2018) found that students aged between 21 and 25 felt there was little difference between them and 18-year-old entrants. Some studies suggest that ‘mature’ should signify students who are older than this, for example above 30 (e.g. Mackey et al.,
2018). Baxter and Britton (
2001) define mature students are those who enter HE at an age of 26 or above. This is the definition that has been adopted for the purposes of this study, not least because it acknowledges students’ perspectives reported by Lewis (
2018).
Mature students have been a growing group of applicants to higher education for several decades (Evans & Nation,
1996; Pearce,
2017; Schuetze,
2014), although application numbers have fallen in recent years (UCAS,
2017). The Universities and Colleges Admissions Service (UCAS) (
2017) found that in 2017, 10.4% of successful HE applicants from the UK were mature, and in 2018, the number of acceptances for students aged 26 and over increased by 6.7% (UCAS,
2018). Initial UCAS data for 2019 show that older age groups, particularly for students aged over 30, have increased significantly (UCAS,
2019). This follows a general pattern over the last few years of increasing acceptance rates for students in older age groups. It is therefore worth our efforts to ensure our pedagogies are appropriate for mature students.
It should be noted that mature students are a diverse, heterogeneous group, consisting of adults from different genders, cultures, socioeconomic groups, and educational backgrounds, all with different reasons for studying (Schuetze,
2014; Waller,
2006). It is important, therefore, to maintain awareness of the diversity of mature students; being ‘mature’ is just one facet of their complex status.
Technology enhanced learning
There is no single agreed definition of technology enhanced learning (TEL), due to its extremely diverse and evolving nature. This study draws on the definition suggested by Law, Niederhauser, Christensen, and Shear (
2016), and defines TEL as
learning in an environment that is enriched by the integration of digital technology. The types of digital technology used for TEL are equally diverse, and may include hardware such as laptops, mobile telephones, televisions and e-readers, or software such as social networking, office suites, online forums, and videos (Antoniadis et al.,
2009; Loughlin,
2017).
TEL has been shown to benefit students and improve their HE experience, both pedagogically and otherwise (Akçayır & Akçayır,
2018; Awidi & Paynter,
2019), and in turn, lecturers choose TEL such as VLEs, social media, and videos to improve student experience (Loughlin,
2017). Screen readers, recording tools and planning tools increase the accessibility of courses for disabled students; however, this raises concerns surrounding the ‘digital capital’ these students have - the social and cultural support and resources a person can access. This is something that can particularly affect mature students as well (Seale, Georgeson, Mamas, & Swain,
2015).
The use of technology in the learning environment can develop students’ higher-level thinking by moving beyond simple memorisation and recall (Lee & Choi,
2017). Students who exhibit higher-order thinking are more likely to be academically successful (Zohar & Dori,
2003). Mature students have been found to be more likely to adopt higher-order approaches over memorisation approaches (Richardson,
1994), and Lee and Choi (
2017) found that the use of technology can help them to do so. Students’ attitudes towards technology indirectly affect higher-order thinking, which in turn increases academic success. Therefore attitudes are an important factor in order for design approaches to work as intended, and it is vital to explore these when designing TEL resources (Lee & Choi,
2017).
TEL also increases collaboration. This could be through providing resource-sharing platforms (Al-Emran, Elsherif, & Shaalan,
2016), enabling resource creation (e.g. student podcasts in Lee, McLoughlin, & Chan,
2008), or simply allowing easy communication between students, from which peer feedback and reflection will naturally arise (Lee & Choi,
2017). Furthermore, technology enables the development of interactive teaching approaches such as blended learning (Dalsgaard & Godsk,
2007) or flipped learning (Akçayır & Akçayır,
2018). These have been found to increase attainment (Al-Qahtani & Higgins,
2013; Charles-Ogan & Williams,
2015), as well as decrease subject-specific anxieties (Marshall, Staddon, Wilson, & Mann,
2017).
It is therefore unsurprising that TEL has been, and continues to be, a growing focus in higher education. HE institutions are integrating technology throughout, and encouraging (and sometimes pressuring) lecturers and tutors to use it in innovative ways. In turn, all students are expected to engage with technology in some form, irrespective of level or background. This presents challenges in designing learning activities that are accessible to all. In particular, it is vital to consider the learning needs of all students.
Attitudes to technology
It is often anecdotally thought that mature students are more anxious about technology than younger students, and that they are generally poorer and slower at gaining digital literacy skills (Broady, Chan, & Caputi,
2010). Some studies have found that older people are less likely to engage with technology than younger people (Czaja et al.,
2006); however, when they perceive that the technology is useful, their motivation to use and learn it increases (Czaja & Sharit,
1998; Mitzner et al.,
2010). The extent to which there are observable differences between mature and younger students in their approaches and attitudes to technology has not yet been clarified for the modern cohort.
The extent to which students choose to accept or reject technologies can have positive or negative effects on their education, since universities are embracing TEL more and more (Henderson, Selwyn, & Aston,
2015). Attitude is one important factor in technology acceptance, affecting whether students adopt technologies, but attitudes are also subject to change over time, often dependent on whether one is having positive or negative experiences (Broady et al.,
2010; Straub,
2009).
Attitude can be difficult to define, as it has several dimensions and is used in various ways according to the needs of each author or instrument (Di Martino & Zan,
2010). Broadly, it is an individual’s disposition towards a subject, and whether it is positive or negative. Hart (
1989) breaks it down into three components: emotional response, beliefs, and behaviour. This is a particularly useful definition, as it explicitly includes the behavioural aspect, allowing links to pedagogical methods and outcomes. This is the definition used for the purposes of this paper.
An overall attitude can be multi-dimensional, with several factors (Czaja & Sharit,
1998). Factors relating to TEL have been explored in previous literature, usually for younger students. They include confidence level (Garland & Noyes,
2005), previous experience (Garland & Noyes,
2004), and perception of the required knowledge level to engage with a resource (Levine & Donitsa-Schmidt,
1998). These can be classified as relating to the emotional response, behaviour, and belief attitude components respectively. Purpose, usefulness, and support also contribute to the overall attitude (Czaja & Sharit,
1998).
Gardner, Dukes, and Discenza (
1993) found that students who use computers more are more confident with computers, and therefore have a more positive attitude towards them. They propose two factors affecting computer attitude: frequency of use; and how long the user has been using the technology. It is worth noting that Gardner et al. (
1993) was conducted over 25 years ago, when computers were less common and less user-friendly, and therefore frequency of use may have had more of an effect on confidence than in modern days. In contrast, Garland and Noyes (
2005) found that computer confidence isn’t the main factor affecting attitude, but confidence in
learning from computers is. The distinction between the two may arise from the passage of time, in which those who have used computers the least, and are thus still learning, are less confident as they perceive they have less computer knowledge. This may mean they are more likely to be more apprehensive about technology they are not yet comfortable with, which may manifest itself as a ‘negative’ attitude. ‘Technology learning’ confidence may, then, be analogous to length of time of use, as explored in Gardner et al. (
1993). Other factors that potentially affect attitude include: whether the technology is used for home use or in educational institutions (Gardner et al.,
1993); self-perceived knowledge level (Mitzner et al.,
2010); and perceived usefulness of the technology (Czaja & Sharit,
1998).
In 2005, Garland and Noyes found that mature students had lower computer confidence than younger students, both for general use and learning from computers. Interestingly, distance-learning students who were mature actually had more confidence in general computer use than younger students, but still had lower confidence for learning from computers. This again fits with the idea of more experience giving higher confidence, since distance-learning students use computers almost exclusively for learning. In contrast, Broady et al. (
2010) found that older students have an initial lack of confidence that improves with use, which could be interpreted as lower confidence when learning about computers.
The factors that affect how users adopt technology are numerous and complex, and technology acceptance models have been used and studied for decades (Scherer, Siddiq, & Tondeur,
2019; Wingo, Ivankova, & Moss,
2017). One of the most famous and utilised is the Technology Acceptance Model, or TAM (Davis,
1989). The original TAM study suggested that the main two contributing factors to attitude are perceived usefulness and perceived ease of use. The TAM2 (Venkatesh & Davis,
2000), an update to the TAM, looked at voluntary versus compulsory use of technology, as well as other factors such as social influence processes and cognitive instrumental factors. Both the TAM and TAM2 are old instruments designed in a world where technology was not as prevalent. They also focus on job performance and productivity, not education, and although it has been used for student attitudes as well over the years (e.g. Levine & Donitsa-Schmidt,
1998; Ngai, Poon, & Chan,
2007), they may perform better in business environments (Legris, Ingham, & Collerette,
2003).
Studies that examine the attitudes of mature students to technology are often out of date, sometimes dating from more than a decade ago (Czaja & Sharit,
1998; Gardner et al.,
1993; Garland & Noyes,
2005), or focus on distance learning students (Jelfs & Richardson,
2013). Technology has evolved rapidly, and technological advances have changed how students learn (Kim, Song, & Yoon,
2011), reducing the validity of the older scales (Garland & Noyes,
2008). With changing technology, attitudes and use will also have evolved (Broady et al.,
2010). More recent studies on attitudes to technology have their own limitations, such as only exploring one aspect such as frequency of use (Kennedy, Judd, Dalgarno, & Waycott,
2010), focussing on mode of study (Arrosagaray, González-Peiteado, Pino-Juste, & Rodríguez-López,
2019), gender (Cai, Fan, & Du,
2017), or being too course-specific rather than allowing students to reflect on the use of technology in their everyday lives (Awidi & Paynter,
2019; Edmunds, Thorpe, & Conole,
2012). Other studies are or specific to certain types of technology such as mobile devices (Al-Emran et al.,
2016) or simply computers (Garland & Noyes,
2004).
Purpose of the study
Higher education has a changing cohort, with increasing acceptance rates for mature students. Universities are adopting more and more technology, and expecting lecturers and tutors to integrate it as widely as possible (Shelton,
2014). As researchers, we therefore need to ask whether it is pedagogically efficient to treat our modern cohort the same as a traditional cohort? This calls for deeper understanding of the technological learning needs of mature students, and how they differ from those of younger, more ‘traditional’ students. This understanding is also crucial for the design of TEL resources (Lee & Choi,
2017). A diagnostic exploration will allow us to either reassure ourselves of the probable efficaciousness of current practice, or compel us to amend our learning environments.
This paper presents the findings from a quantitative study exploring students’ use of technology and their attitudes towards it. This study is timely because previous work examining the attitudes of mature students to technology may no longer reflect contemporary student age profiles. Technology has evolved much over the years, and therefore attitudes and use will also have evolved, particularly surrounding specific technologies that may have dropped out of use or evolved beyond recognition. No existing instruments were found to be suitable for the task, and so a new instrument was created for this study, based on Hart’s (
1989) three attitude components, and also exploring students’ technology use. A factor analysis was carried out in order to determine the dimensions of students’ attitudes towards technology.
The following research questions were posited: (1) Are mature students more negative about technology enhanced learning than younger students? and (2) Is there an attitudinal difference between different ages of mature student? The answers to these questions will inform a discussion of the pedagogical implications for designing age inclusive classrooms, allowing us to begin to address the gap between intended and actual learning.