This essay identifies and discusses factors influencing motivation in the context of electronic learning (e-learning). First, it defines electronic learning and motivation. Then it discusses theories of motivation relevant to e-learning. Third, it identifies and critically evaluates factors influencing motivation. Finally, it offers some conclusions.
The essay will consider e-learning in both the work and education contexts, where methods of facilitating learning have developed from traditional 'chalk and talk' - which is still employed by some university lectures, despite being considered outdated - to newer technology-based methods, using virtual learning environments (VLEs) such as Blackboard and other forms of electronic learning.
Electronic learning, often abbreviated to e-learning, can be defined as any learning activity supported by information and communication technologies - or ICTs. There are debates concerning the labels, for example whether ICT-based learning is the same as computer-based learning, or is the same as e-learning. The differences are related to the different channels through which the materials are delivered (see Figure 1). Online materials are internet-based and use the world wide web channel. Intranet materials, which can look like those available on the world wide web, are delivered through an internal network of personal computers. Floppy disks and CD-ROMs are used on stand alone personal computers - that is not connected to a wider network. E-learning is taken to mean any form of electronic technology - as opposed to chalk and blackboard technology - to support learning.
Blackboard Stand-alone P.C. Networked P.C. On-line W.A.P.
Figure 1: Forms of electronic learning
Theories of motivation
Motivation can be defined as a decision making process in which the individual sets some desired goals and then adopts particular behaviour to achieve these goals. This notion of motivation as being goal-based and intended to satisfy needs can be categorised as a content approach to theories of motivation. From this (simplistic) perspective, motivation can be provided by identifying what goals individuals seek and then providing means of achieving these goals, in this case through computer based learning. More recently, the focus has shifted to the more individual, complex, dynamic and contextual aspects of motivation, categorised as a process approach. This approach requires identifying how and why individuals are motivated. In addition, motivation can be intrinsic (to meet personal, internal goals) and/or extrinsic (to achieve tangible, external rewards).
These general theories of motivation can be related to the learning arena, and in particular computer based learning, in that learners might have different motivations to learn, and may learn in different ways.
Theories of learning can be categorised into three broad perspectives. Behavioural models of learning (see, for example, Skinner 1953) are based on a stimulus-response approach, where learning is achieved through trial and error, and practice. Motivation is stimulated by the possibility of gaining some (extrinsic) reward. Such models are associated with learning how to do things, where the results can be measured through observing physical changes in performance. One such approach is social learning (Bandura 1977). Cognitive models (Piaget 1971) liken learning to information processing or a complex computer programme. Here, learning is stimulated by a need to make sense of new information, and achieved through manipulating this information to generate personal understanding. This is an internal process that can be measured (externally) by changes in knowledge and comprehension. Humanistic models (for example, Rogers 1983) focus on learning about oneself and one's situation. This approach includes social constructivist models of learning.
Deci and Ryan (1985) propose three motivational orientations. The autonomy orientation involves 'a high degree of experienced choice with respect to the initiation and regulation of one's own behaviour,' (Deci & Ryan, 1985: 111). Individuals are more likely to be intrinsically motivated and self-determined. In the context of computer based learning, this suggests individuals are able and willing to identify - and set goals to meet - their learning needs. Identifying learning needs could be achieved through individual's own experience of problems, through appraisal and performance management processes, or through computer-based packages (such as psychometric tests). Motivation to meet these needs could be enhanced through the individual's ability to decide the content, medium, timing/pace and place of learning. The control orientation involves 'people's behaviour being organised with respect to controls either in the environment or inside themselves,' (ibid:112). Here, learners tend to do things because they think they 'should' and tend to rely on controlling events, such as prescribed assessments or extrinsic rewards to motivate them. This could include being sent on a course by a manager, or perceiving the need to undertake training to enhance promotion prospects. The impersonal orientation involves 'people's experiencing their behaviour as beyond their intentional control,' (ibid). Applied to the context of computer based learning, individuals are likely to see themselves as incompetent and experience strong anxiety when entering new (learning) situations. This is of particular relevance where learners are unfamiliar with computers, perhaps older employees, or have had prior 'bad' experiences of (on line) learning. This orientation is amotivational.
As many users of e-learning will be adults, it is useful to consider the concept of adult learning or andragogy. The principles of adult learning, including motivation to learn, are explained by Knowles (1978). These principles (cited in Mumford 1986:260) are summarised below in Figure 2, along with the implications for software design.
Desired design features of e-learning to provide motivation
see themselves as independent
Are learning materials self-contained? Can learners adopt their preferred style(s)
desire a sense of self accomplishment and determinism
Is there learner control - eg over choice of content, pace, learning strategy and style?
are motivated through diagnosing their own needs
Are learners able to diagnose their individual learning needs?
like to actively participate in the learning experience;
Is there active participation in learning eg interaction with software?
like to be involved in self-evaluation and compare performance to norms;
Are there opportunities for self-evaluation and feedback?
consider their previous experience an essential basis for future learning;
Can learners navigate through the content, eg enabling individuals to start from appropriate knowledge base?
evaluate learning in relation to its application to day-to-day living
relevance of the content to current/future job
Figure 2: The principles of adult learning
Factors influencing motivation
Keller (1987) covers the subject of motivation and computer assisted learning (CAL) under three broad headings of interest, attention and feedback. Keller (1987) suggests that computer based packages can provide motivation if the content is of interest and relevance to the learner, if the presentation engages and maintains their attention, and if there are appropriate feedback mechanisms. Content, presentation and feedback are considered in other sections of this review under access, presentational, evaluation and technical issues.
To provide motivation, e-learning needs to take into account a range of factors, such as learner independence, meeting individual learner's goals, learner control, allowing opportunities to practise, giving feedback, enabling active participation, group work, mood, relevance and anxiety. Each of these factors is discussed below.
Learner independence allows the learner to adopt their preferred learning style(s) (Kolb 1976, Honey & Mumford 1986) and approaches to learning (Biggs 1987). Keefe (1979) defines learning style as the characteristic behaviours of learners that serve as relatively stable indicators of how they perceive, interact with, and respond to the learning environment. However, effective learning requires that learners are not only aware of their preferred learning styles but also develop the ability to operate outside of these, to enable them to work around the experiential learning cycle (Kolb 1983). A key issue in how successful individuals learn is their flexibility and ability to use appropriate learning approaches (Honey and Mumford 1986). Helping individuals become aware of their own learning processes and approaches (meta-learning or learning about learning) can speed up this process. For example, if students are activists and fail to be reflective learners, this may impede effective learning. Research by Clariana (1997) suggests that learning styles can change with exposure to CAL, and that subsequent learning behaviours can be viewed as both positive and negative, depending upon the instructional methodology. Cann (1999) argues that the learning style imposed by any CAL package is inherent, but also partly imposed by the context in which the package is presented to or used by students. Cann (1999) found that online learning materials, introduced to promote reflection, were used by students for relatively short periods of time, questioning whether the World Wide Web is a suitable medium to encourage reflection. A question to be asked of e-learning materials is - does the software allow time for reflection?
Research by Ross and Schultz (1999), investigating the impact of learning styles on human-computer interaction, found that learning styles significantly affected learning outcomes. They refer to work by Friend and Cole (1990) who discovered that sensing-thinking individuals responded more favourably to computer-aided instruction (CAI) than did intuitive-feeling types, who need more human interaction to achieve desired learning outcomes. They also refer to the work of Enochs et al (1985) who found that concrete learners (using Kolb's Learning Style Inventory) learned better from CAI than did abstract learners. They cite research by Dunn and Dunn (1979) who asserted that students who are motivated, require specific instruction, are sequential in their approach to learning, and enjoy frequent feedback generally do well with programmed learning such as CAI. However, students who are kinaesthetic, peer-oriented learners may not be engaged adequately by the same method of instruction. Thus, e-learning should be able to accommodate these different learning styles to enable learner independence.
Scott, Buchanan and Haigh (1997:19) report that, 'In many institutions increasing priority is being given to the educational goal of intellectual independence with course objectives placing more emphasis on the processes of learning and less on course content.' The authors then discuss their research to develop an independent learning approach in the context of large university classes. They 'introduced a student-centred learning (SCL) approach to encourage students to take more responsibility for their own learning,' (ibid:21). They drew upon Kolb's (1984) work on learning styles and that by Boud (1988) on student independence, including characteristics such as choosing where and when learning can take place, engaging in self-assessment, and acquiring new learning strategies and tools. Success was measured by student enthusiasm and evidence of deeper learning. They report that, 'While results to date show a pleasing increase in the use of effective learning strategies for independent learning by students, a significant minority of students have not responded positively to the independence goal,' (ibid). A question to be addressed in e-learning is, therefore, how can learners be supported and allowed to become independent.
Meeting individual learner's goals and learner control
Traditional definitions of pedagogy suggest that the learner is dependent upon the teacher, who decides content, delivery, timing and assessment. A learner-centred approach allows the learner greater control over their experience, (Rogers 1965, 1969), such as setting individual goals, which increases motivation. Self-diagnosis of learning needs affords ownership of and, thus, greater commitment to the learning. Ross and Schulz (1999:5) refer to the work of Rasmussen and Davidson (1996), who argue that one of the most powerful features of computer-aided instruction (CAI) is its capacity to individualise instruction to meet the specific needs of the learner. Ross and Schulz (1999:6) note that, ' while CAI has tremendous potential to individualise instruction, a number of learner characteristics such as motivation, learning styles, and background knowledge may affect the quality and effectiveness of a CAI instructional session.'
Motivation to learn, in any circumstances and particularly with Computer Assisted Learning (CAL), requires an explicit link between required learner actions and educational objectives and goals (http://www.auckland.ac.nz/cpd/caleval.html). Borges and Baranauskas (1998:27) suggest that 'in the human-centred approach to the design of computer-based learning environments, we must consider that the goals stem from human needs, and not from the computational system.' That is, the goals should be pedagogical and learner-centred, rather than technological. McLoughlin and Oliver (1998:126) suggest that a constructivist view (Knight and Knight 1995) of computer based learning treats the computer as a tool through programming the learner is able to control the technology and generate responses.' CAL can potentially change the education system and help learners achieve higher level educational goals through more flexible learning processes. It can enable a move away from the conventional model dominated by didactic linear technologies of knowledge transfer to non-linear technologies. Such informational educational technologies aim at non-linear structuring of information and knowledge in the form of hypertext, hypermedia and databases placed on CD-ROM or on the Internet and World Wide Web (Pak 1998). Such technologies allow learners to engage with the learning material in different and multiple ways, increasing interest, attention and control (Keller 1987). Small and Grabowski (1992) found that high motivation levels led to subjects spending more time with the computer program, and subsequently contributed to higher learning outcomes. Low motivation levels had an inverse effect.
Keller (1987) suggests that computer based packages can provide motivation if there are appropriate feedback mechanisms. Self-evaluation enables feedback to be sought when the learner considers it necessary and/or appropriate. Ninness, Ninness, Sherman and Schotta (1998) conducted an experiment into maths performance. Using computer-interactive tutorials and self-assessment procedures, with and without accuracy feedback, Ninness et al (1998) found that, after students were trained, computer-interactive self-assessment with feedback may facilitate high rates and duration of performance. In the same research, Ninness, Ellis and Ninness (1999) found that, with practice, self-assessment may become a source of secondary reinforcement and may sustain high rates of academic behaviour in the absence of external reward systems.
Simons (1999:15) states that there are three ways to learn: guided learning, experiential learning and action learning, and argues for a shift from passive to active learning. Active learning can be defined both as learning in which the learner uses opportunities to decide about aspects of the learning process, and the extent to which the learner is challenged to use his or her mental abilities while learning. Active participation enhances experiential learning through the construction of personal understanding and the transformation of experience (Kolb 1983). Simons argues that 'Active learning is more attractive to learners because they are more motivated and interested when they have a say in their own learning and when their mental activity is challenged,' (1999:17). He also states that ideal learning processes are the active, cumulative, constructive, goal-directed, diagnostic, reflective, discovery oriented, contextual, problem oriented, case based, social and intrinsically motivated kinds of learning (ibid). Thus, quality computer based learning should incorporate active learning characteristics, such as flexibility (choice over learning goals and how these are achieved) and appropriate intellectual challenge, to provide (intrinsic) motivation.
Aldrich, Rogers and Scaife (1998:327-328) conducted a study systematically assessing the pros and cons of two CD-ROM software packages against a set of pedagogical criteria. In terms of motivation and engagement, the 'better' package featured a format that stimulated curiosity, included creative ways of finding out information, were challenging and diverse. The 'poor' package featured a rigid structure, which could not necessarily hold the learner's interest for long.
Opportunities for practice
Thorpe (1999:41) reports that new technologies have been found to offer one or a combination of benefits including opportunities for drill and practice, through software allowing unlimited trial and error and through virtual learning environments, as in virtual field trips, virtual microscopes and simulations. McLoughlin and Oliver (1998:126) report that 'Until the 1980s the success of computer assisted learning (CAL) was attributed to its capacity to individualise instruction (Saljo 1994). Computer software of the drill and practice variety is designed according to the behaviourist principle that learning is best achieved by an individual practising tasks in a repetitive manner until mastery is achieved. The computer is regarded as a teacher, giving immediate feedback on responses and enabling further practice.' However, McLoughlin and Oliver (1998:126) note that, 'Computer tasks of this nature also limit educational goals to the attainment of lower order skills such as remembering, reciting or producing isolated segments of information.'
One issue in the discussion of motivation to learn is the extent to which computer-based learning facilitates group learning and social interaction. McLoughlin and Oliver (1998:127) refer to work by Nastasi and Clements (1992) suggesting that students working together enjoy peer support and increased verbal exchange leading to high levels of tasks involvement and problem solving behaviours. Not only are these behaviours positively related to improved learning outcomes, but they also lead to increased motivation.' Thorpe (1999) also highlights that computer-mediated communication (CMC) offers the potential for people to work together.
One potential demotivator in terms of computer based learning is anxiety. Lee (1997) argues that the design of effective computer training needs to take into account the level of anxiety toward learning computer technology in addition to other adult learning characteristics. It is argued that 'human beings are relentless theorisers, actively learning and seeking to make sense of their experiences. This is particularly so in computer related tasks, where operations are normally quite alien to the new user, creating stress and an added incentive to gain a measure of control as soon as possible (Carroll and Mack 1995),' (Hale 1998:185). Brosnan (1998) explored the relationship between computer anxiety and computer performance using a self-efficacy framework and argues that computer anxiety directly influenced the number of correct responses obtained. Brosnan (1998) found that less anxious participants obtained more correct responses.
Presno (1998) studied instructional techniques and behaviours that either reduced or exacerbated anxiety in an Internet class for novice adult students, using observations, interviews and document analysis, and found four main areas of anxiety. These were: Internet terminology anxiety, Net search anxiety, Internet time delay anxiety, and general fear of Internet failure. Blair et al (1999) investigated the relationship between expertise, state self-efficacy and state worry during the administration of a computerised job-related certification test. A questionnaire survey instrument was administered to individuals who had just finished the test and were willing to self-report. Participants who scored highly (experts) demonstrated significantly higher levels of state self-efficacy and less state worry than those classed as non-experts.
In terms of computer-related success and failure, Rozell and Gardner (1999) found that computer training was effective in raising user efficacy levels and improving computer performance, as well as reducing anxiety. In addition to training, Levine and DonitsaSchmidt (1998) found that computer use itself has a positive effect on perceived computer self-confidence, as well as on computer-related attitudes. In another study, Berk and Nanda (1998) found that the use of humour reduced anxiety, improved attitudes to course content and increased achievements.
Adult learners returning to learning after long periods of absence may experience fear of failure and lack of confidence, both contributing to anxiety. Ference and Vockell (1994) identify features of adult learning similar to Knowles (1978), but also note that adult learners are often motivated by internal factors including self-esteem, recognition, confidence, career satisfaction and overall life quality; they also tend to be motivated by external factors like better jobs, increased promotional opportunities, and higher salaries (cited in Lee 1997:139-140). From the research cited, it is suggested that confidence can be increased through computer training, computer use, appropriate feedback and support, for example. However, it is debatable to what extent any other of these particular internal and external motivating factors can be provided through software design.
It is important to consider whether motivation to engage in computer based learning is intrinsic or extrinsic, for example, whether learners have choice over what, why and how learning is to be achieved. However, Ventkatesh and Speier (1999:3) comment that little is known about the underlying factors influencing extrinsic and intrinsic motivation, the key drivers of technology usage. They suggest that if a technology is perceived to be useful in facilitating the individual's productivity, s/he is likely to have extrinsic motivation to use a given technology. However, if a technology is not perceived as useful, it will offer no advantages to job performance or financial reward.
Another factor in providing motivation is a person's mood. In terms of computer technology training, an individual's perception of enjoyment of technology use is likely to be influenced by mood, and a positive mood is likely to lead to greater creativity, task enjoyment and satisfaction. Ventkatesh and Speier (1999:23) found that, 'positive moods at the time of training result in short term increases in intrinsic motivation and intention to use the technology,' However, they also found that, 'a long-term lowering of intrinsic motivation and intention was observed among those in the negative mood condition,' (ibid:1). This would suggest that if training was conducted when a person is in a negative mood, there would be diminished training benefits and the decreased motivation experienced could last for a long time, influencing further episodes of (computer based) learning. Ventkatesh and Speier (1999:21) argue that, 'developing intrinsic motivation during learning is critical.'
In terms of mood, enjoyment and usefulness, Ventkatesh and Speier (1999:10) report that prior research assessing the influence of motivation on computer technology acceptance has measured extrinsic motivation as perceived usefulness and intrinsic motivation as perceived enjoyment, citing the work of Davis et al (1992). Usefulness refers to resulting enhanced job performance/productivity. This might be more easily measured within work activities. Enjoyment and usefulness relate to making computer based learning 'pleasurable and successful'. Teo, Lim and Lai (1999) studied motivation for the use of the Internet, defining intrinsic motivation as perceived enjoyment and extrinsic motivation as perceived usefulness. They found that local Internet users used the Internet mainly because they perceived the Internet to be more useful to their job tasks and secondarily, because it was enjoyable and easy to use.
To conclude, this essay has identified factors influencing motivation to learn and critically evaluated these in the context of e-learning. Key features of e-learning that provide motivation are: learner independence, meeting learner's goals, active participation, opportunities for practice and feedback, group work, mood and relevance. However, factors such as fear and anxiety should also be considered as these can de-motivate (particularly older) learners. Whatever the method, e-learning is going to continue to provide innovative ways of facilitating and supporting the development of knowledge and skills, whether at work or in the classroom, or wherever.