A review of the literature relevant to student satisfaction and achievement in online education has identified five areas which are important to consider.

  • Instructional pedagogy
  • Quality of instruction
  • Interaction and communication
  • The online learning environment
  • Individual learner qualities.

The dominant educational philosophy associated with online education is social constructivism (Kanuka, Anderson, 1998), however it is not clear if this is the optimal pedagogical model for online or blended delivery.  In a study of MBA programmes, Benbunan-Fich and Arbaugh found that if the educational process involved either group collaboration or knowledge construction, learning outcomes were improved (2005).  When constructivism and knowledge transmission (objectivism) were considered independently of other factors students who were involved in constructivist learning perceived that their learning was less than those who are taught with an objectivist method when in fact their actual learning was greater (Benbunan-Fich & Arbaugh, 2005).  When collaborative approaches were combined with individual learning approaches, the students learning was greatest when collaborative approaches were used, which was consistent with their perceptions (Benbunan-Fich & Arbaugh, 2005). However the same authors found that the gains made with constructivist learning and collaborative learning were not additive.  There was no significant difference in achievement between courses which used either constructivist approaches, collaborative approaches or a combination of the two.  Given that student’s perceived learning was maximised when knowledge transmission & group-oriented approaches were combined (Arbaugh & Benbunan-Fich, 2006), and that this combination of pedagogical themes was one of the combinations that optimised student achievement, these research findings suggest that the combination of knowledge transmission and collaborative learning is the logical pedagogical model to use in the design of online courses.

Factors which have been found to be significant relating to the quality of instruction are clear expectations of coursework requirements (Comm and Schmidt, 1988 as cited in Bryant, 2003) and how to proceed through the course (Shea et al., 2001), as well as receiving prompt, high quality feedback from the instructor (Shea et al., 2001).

Many authors have described the importance of  social contact and social processes in online learning (Laurillard, 1997 as cited in Caplan & Graham, 2008; Shea et al., 2001; Pillay et al., 2007).   According to Pillay, Irving and Tomes “social interaction within the [online learning environment] supports and motivates students to complete their work and seek out new learning experiences” (2007, p. 218).  Other authors have identified the level of interaction with classmates (Shea et al, 2001; Benbunan-Fich & Arbaugh, 2005) & and the instructor (Bryant, 2003) as key factors contributing to student satisfaction.  A high level of interaction has also been found to contribute to student achievement (Pillay, Irving, Tomes, 2007; Benbunan-Fich, Arbaugh, 2007).  Conversely dissatisfaction with the level of interaction with the learning community and/or the instructors has been found to contribute to poor outcomes for students (Pillay et al., 2007). Providing detailed feedback as close as possible to the performance of the assessed behaviour contributes to good outcomes for students (Shepard, 2000 as cited in Caplan & Graham, 2008).  This evidence provides strong support for the use of formative online tests that provide feedback on performance immediately following the test (Prensky, 2001 as cited in Caplan & Graham, 2008).  However this will generally not be all that is needed, as this type of testing is generally only able to provide feedback on the memorisation of individual units of knowledge rather than the complex integration of concepts which is important for higher level learning.  Regular, timely feedback from the learning facilitator will also therefore be necessary.

The nature of the online learning environment appears to be a significant factor in student satisfaction, learning and achievement.  Researchers within the SUNY learning network found that having a simplified online interface contributed to student satisfaction (Shea et al., 2001).  According to the principles of cognitive load theory this should reduce cognitive load, and result in improved learning (Paas, Renkl, Sweller, 2003). Cognitive load theory provides a useful, well researched model for the design of online learning experiences (Paas, Renkl,  Sweller, 2004; Clark & Mayer, 2004). An online learning environment can be designed in accordance with cognitive learning processes (Clark & Mayer, 2004), and the degree to which design is tied into cognitive learning processes is predictive of student achievement (Pillay et al., 2007; Clark & Mayer, 2004).  Dysfunctional learning activities have been found to contribute significantly to dissatisfaction (Pillay, Irving, Tomes, 2007) and poor educational outcomes for students (Pillay et al., 2007; Clark & Mayer, 2004).  Online learning activities may be dysfunctional due to poor design, a lack of testing, or technology failures.  Instructional design and testing is more important in an asynchronous learning environment when compared to a classroom because of the lack of feedback.  In a classroom environment you are often able to dynamically mould the classroom experience based on your perceptions of how the learning activities are working (or not working) with your group of students.  In an asynchronous learning environment the activities that you have designed are more static.  This is yet more support for the inclusion of technologies which facilitate communication and feedback.  Technology failures are also a significant contributor to dysfunctional learning experiences.  Strategies for managing this type of technology risk include having a plan A and a plan B, having a back-up communication channel (including VOIP, audioconferencing, email-group, a point of contact such as a facilitator’s cell-phone, facilitator having everyone’s phone numbers), ensuring that the learning facilitator is able to contact the server administrators in the case of server failure (McQuillan, 2007).  If a real-time educational experience such as web-conferencing is planned, having two or more facilitators may be advisable so that one person is free to concentrate on resolution of any technology problems while another facilitator can concentrate on facilitating the educational experience (McQuillan, 2007).  Students can have their own technical computer difficulties which can act as a barrier to their learning (Shea et al., 2001).  Pillay et al. found that students who had a course with a flexible rate of learning achieved more highly than those in courses which were relatively more static (2007).

Qualities of the individual learner have also been found to be related to student achievement.  According to Clark and Mayer (2004) online students need to have metacognitive skills.  These are the ability to set learning goals, to determine how to reach their goals, and to make adjustments where necessary. Students with poor metacognitive skills need more direction whereas students with good metacognitive skills tend to be more self-sufficient learners. This skill-set has been described elsewhere (Connor, 2004) as the qualities of a “self-directed learner”. While computer literacy prior to taking part in an online course has been found by some authors to be uncorrelated with satisfaction and learning (Shea et al., 2001), this presumably depends on a combination of the level of technical ability required to negotiate the online learning environment, the computer support which is available to students, and individual students self-efficacy with respect to computers.  Pillay, Irving and Tomes found that students with a low level of computer self-efficacy were more inclined to feel anxiety when required to use computer applications.  This anxiety leads users to interpret events more negatively than non-anxious users and therefore contributes to dissatisfaction (2007).  The same authors found that computer self-efficacy is enhanced by the development of computer skills suggesting that educators involved in online study should consider the incorporation of computer literacy training within or associated with their programmes.  Other researchers have found the level of satisfaction with the level of computer support to be predictive of satisfaction with online learning as a whole (Shea et al, 2001).  Pillay et al, found that  computer literacy and computer self-efficacy were positively correlated with educational outcomes for students (2007).  While computer skill is not necessary for participants in online courses, computer self-efficacy  and computer supports are.

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