Students and e-learning – a start to the People section

The following is the first step in the People component of the Ps Framework from chapter 2 of my thesis. The first bit (“People”) is the introduction to the thesis section and the following (“Students”) is the first major section of that component. Hopefully, over the next week and in fairly quick progression the remaining sections of the People component will get posted.

As always, this stuff is version 1 draft quality and there is always going to be more that can and probably should be included, however, I’m currently going for “good enough” rather than “too good”.

The suggested four sections that conclude the “People” section are a work in progress and may change. I’m wondering whether the “chasm” section should be included within the “People involved in e-learning section”. Time will tell.

People

Any excellence demonstrated by a University is not a product of technology, it is a product of the faculty, students and staff who play differing roles in the pursuit of scholarship and learning (Dodds 2007). It comes from the people. Teaching and learning are two of the most highly personalised processes (Morgan 2003). It is clear that consideration of the human dimension is critical to education (Watson 2006). Personal characteristics have been found to influence e-learning implementation (Siritongthaworn, Krairit et al. 2006) and most universities are still struggling to engage a significant percentage of students and staff in e-learning (Salmon 2005).

While the success of an information systems innovation can be determined in a number of ways, there has been a range of work that marks a shift from organisational measures, such as delivery on-time and on budget, to more user focused measures including system usage (Behrens, Jamieson et al. 2005). It is the uptake and use of features, rather than the provision of those features, that really determines education value (Coates, James et al. 2005). The perceptions of the people who may potential use an information and communication technology play a significant role in their adoption and use of that technology (Jones, Cranston et al. 2005). The beliefs held by those involved in the educational process, regardless of how ill-informed, can have a tremendous impact on the performance of both students and teachers and how effectively technology may be utilised (Stewart 2008).

In considering adoption, it is important to recognise agency, the ability of the individuals or groups within universities to consciously or unconsciously respond to and change practices (Trowler and Knight 1999). Especially since taking full advantage of e-learning will require university administrators, lectures and students to think differently about teaching and learning (Volery 2001). Individuals and groups within the same institution will often have very different, even conflicting, views of best practice in learning and teaching that will influence priorities, including the implementation of e-learning (Luck, Jones et al. 2004). The members of an organisation will vary greatly in their individual characteristics, including their willingness to adopt an innovation like e-learning (Jones, Jamieson et al. 2003).

This section examines, arguably, the most important component of the Ps Framework, People. It draws on the literature to discuss people associated issues that impact upon the implementation and practice of e-learning within universities. It does this through the following major sections:

  • People involved in e-learning;
    An examination of what is known about the characteristics and purpose of the different types of people involved with e-learning within universities.
  • The e-learning chasm;
    Describes an important finding regarding different categories of people involved with e-learning that offers an explanation of less than effective implementations.
  • People and cognition; and
    A brief examination of what is known more generally about people, cognitation and how that may effect the implementation and practice of e-learning.
  • Lessons from People for E-learning.
    Offers one distillation of what has been previously described into particular lessons that may help inform the implementation of e-learning.

People involved in e-learning

The first step taken here to examine issues around people that impact upon learning and teaching is to review what is known about the various roles associated with e-learning. The roles examined here include: students, teaching staff, leaders and managers, technical staff and instructional designers. Each of these roles are examined in turn in the following sections.

Students

An essential component of facilitating learning is understanding learners, and particularly their learning styles, attitudes and approaches (Alexander 2001; Oblinger 2003). Not surprisingly, university students play the key role in their own learning, however it is striking how recently the notion has been contested, or even ignored (Goodyear and Ellis 2008). Students are not educated solely through the efforts of teaching staff, but also through the contributions of fellow students (Jongbloed, Enders et al. 2008). A student’s experience of university is embedded in a complex environment made up of diverse, interdependent elements with students’ characteristics as one set of elements (White 2006). A familiarity with the evolving characteristics of adult learners and a sensitivity to their diverse needs improve facilitation of their academic journey (Semmar 2006). This section draws on the literature to develop a semblance of familiarity.

Non-traditional working adults over the age of 26 now comprise over 50% of the post-secondary student population within the United States and are the fastest growing market segment and the largest audience for e-learning (Ausburn 2004). Table 2.1 compares characteristics of university students in the United States between 1970 and 1999. The 70% of students in 1999 labelled non-traditional are students who have delayed enrolment, attend part-time, work full-time, have dependents, are single parents or did not graduate from high school (Oblinger 2003). Speaking in the UK context Jones and O’Shea (2004) report on rapidly changing educational patterns with many more part-time students, mature students and students from more diverse backgrounds, often with lower levels of qualification.

Table 2.1 – Data on student characteristics in the United States (1970 and 1999) (adapted from Oblinger 2003)
Characteristic 1970 1999
Enrolment 7.4m 12.7m
2-year enrolment 31% 44%
Part-time 28% 39%
Women 42% 56%
Older than 25 28% 39%
Non-traditional N/A 73%
Have dependents N/A 27%
Employed N/A 80%

Consequently, students are no longer insulated from external pressures and they have to deal with real world concerns including student loans, poor accommodation and part-time-working and yet many students still aspire to the assumed richness of campus-based education (Haywood 2002). However, there is a significant trend towards students spending less time on-campus and in class and more time in paid employment (Russell 2008). Lock-step approaches to learning, that consist of regular study schedules and weekly modules, are increasingly in conflict with the need for flexibility of these students (Herrington, Reeves et al. 2005). Differences between individuals increase with age, consequently adult education must make provision for differences in style, time, place and pace of learning (Knowles, Holton et al. 2005). Adults value options, variety, self-directedness and effective two-way communication with their classmates and instructor (Ausburn 2004). A large group of students, with significantly different characteristics, find asynchronous e-learning highly suited to their lifestyles and requirements (Hitt and Hartman 2002).

Surveys of student experience and attitudes towards technology, do show an evolution from students having less technology experience than expected through to more recent surveys showing significant personal and social experience with technologies (Hardy, Haywood et al. 2008). A number of researchers have found evidence of young people using technology frequently and creatively in ways that has transformed their experience of childhood and adolesence in comparison to former generations (Somekh 2004). Students (and staff) accustomed to the convenience of modern technology use in banking, mobile communications and web-based retailing do have changing expectations of the use of technology to support their university experience (Duderstadt, Atkins et al. 2002). There does, however, exist an extreme difference between the experience of technology at home and the experience at school that can only be accounted for by the institutional functioning of education systems as a whole (Somekh 2004). Students are increasingly seeing the use of technology in education as inadequate (Oblinger 2003). The growing expectations of technology use within education present an exciting, though potentially disruptive and complex problem (Hardy, Haywood et al. 2008).

There is a line of literature that suggests that an affinity for e-learning is particularly strong amongst students who have grown up with computers and the Internet. Students who have been labelled as the net generation (Tapscott 1998), digital natives (Prensky 2001) or millenials (Oblinger 2003). Students who grew up with computers and often with a broadband connection to the Internet and who, at least in the US, use the Internet (87%), use it daily (51%), play games online (81%), get news online (76%), and use the Internet to communicate with one another (Salaway, Katz et al. 2006). It has been suggested that growing up using this technology has fundamentally changed the way these students think and process information and consequently they are no longer the people educational systems were designed to teach (Prensky 2001).

However, it has been suggested that arguments for the changes in the brains of digital natures is a helpful illusion based on unfounded estimates and a faulty chain of logic (Sheely 2008). New students, with a self-reported high level of competence and confidence with information technology, are relatively conservative in their approach to study prefer to work with traditional face-to-face locations and methods with online sources used as on demand supplements (Hardy, Haywood et al. 2008). Claims about the media habits of digital natives do not appear to carry over to what students expect, or do, in universities (Goodyear and Ellis 2008). These students are confident about their use of ICT and digital media, but they do not want them to erode or substitute for face-to-face teaching and social interaction (Joint Information Systems Committee (JISC) 2007). Two large scale surveys of undergraduate students (Kvavik, Caruso et al. 2004; Salaway, Katz et al. 2006) in the United States – 28,724 respondents for the 2006 survey – reached similar findings including that students prefer a “moderate” amount of technology in their courses and that while many fit the net generation characterisation, many do not.

There is a rich body of knowledge arising from research into higher education that has established a relationship between students’ conceptions of learning, their approaches to study and eventual learning outcomes (Gonzalez 2009). Student resistance can be a behavioural impediment to the implementation of e-learning (Siritongthaworn, Krairit et al. 2006). The expectations and values are a constraint on innovation (Dutton, Cheong et al. 2004). Hirschheim (1992) found that a majority of students taking the Internet version of class, which was virtually identical to a face-to-face version of the class, believed that they were receiving a lower level of education. Perceptions of a lower level of education appear to arise because of the changed learning experience where the e-learning students missed out on traditional face-to-face experience such as lectures and face-to-face discussions (Hirschheim 1992). Participation in traditional classroom formats are still considered an important experience by all students, which suggests that the cultural context of higher education and the resulting student expectations place an additional constraint on e-learning innovations (Dutton, Cheong et al. 2004).

It is dangerous to make assumptions about students’ adoption or rejection of educational technology as their choice and practices are shaped in quite subtle ways (Goodyear and Ellis 2008). Selwyn (2007) sees students as making active choices informed by the signals they pick up from teachers, the curriculum, assessment and workplace demands. Consequently, the diversity in backgrounds and expectations of students forms one of the greatest challenges facing higher education today (Oblinger 2003). Individual differences including gender, system experience, prior knowledge, spatial ability, culture, occupational experience and cognitive styles have a significant effect on the behaviour of learners (Sabry and Baldwin 2003). The combination of diversity from a range of factors that make up the e-learning system means that there is no one student experience of e-learning (Alexander 2001). The growing percentage of adult learners and their preference for variety and flexibility (Herrington, Reeves et al. 2005; Knowles, Holton et al. 2005) only increases this diversity.

However, there are some common factors that are significant determinants of student satisfaction with e-learning including prompt and informative feedback on work, clarity of faculty expectations, high levels of participation by other students time available to devote to the course adequate technical support and training (Alexander 2001). White (2006) suggests that students most value lecturers that are passionate about teaching and readily recognise its absence and how organisational priorities impact on how lecturers approach their teaching responsibilities. Sheely’s (2008) description of the digital native argument as a helpful illusion arises because in the end the digital native argument ends with a description of how students learn and an exhortation for educators and educational institutions to prepare to deal with students who learn this way. However, rather than preferring some new and unusual way of learning these students learn by constructing knowledge through authentic experiences in social situations, in other words, how humans have always learnt (Sheely 2008).

References

Alexander, S. (2001). "E-learning developments and experiences." Education and Training 43(4/5): 240-248.

Ausburn, L. (2004). "Course design elements most valued by adult learners in blended online education environments: an American perspective." Educational Media International 41(4): 327-337.

Behrens, S., K. Jamieson, et al. (2005). Predicting system success using the Technology Acceptance Model: A case study. Australasian Conference on Information Systems’2005, Sydney.

Coates, H., R. James, et al. (2005). "A Critical Examination of the Effects of Learning Management Systems on University Teaching and Learning." Tertiary Education and Management 11(1): 19-36.

Dodds, T. (2007). "Information Technology: A Contributor to Innovation in Higher Education." New Directions for Higher Education 2007(137): 85-95.

Duderstadt, J., D. Atkins, et al. (2002). Higher education in the digital age: Technology issues and strategies for American colleges and universities. Westport, Conn, Praeger Publishers.

Dutton, W., P. Cheong, et al. (2004). "The social shaping of a virtual learning environment: The case of a University-wide course management system." Electronic Journal of e-Learning 2(1): 69-80.

Gonzalez, C. (2009). "Conceptions of, and approaches to, teaching online: a study of lecturers teaching postgraduate distance courses." Higher Education 57(3): 299-314.

Goodyear, P. and R. A. Ellis (2008). "University students’ approaches to learning: rethinking the place of technology." Distance Education 29(2): 141-152.

Hardy, J., D. Haywood, et al. (2008). Expectations and reality: Exploring the use of learning technologies across the disciplines. 6th Networked Learning Conference. Halkidiki, Greece, Lancaster University.

Haywood, T. (2002). Defining moments: Tension between richness and reach. Digital Academe: The New Media and Institutions of Higher Education and Learning. W. Dutton and B. Loader. London, Routledge: 39-49.

Herrington, J., T. Reeves, et al. (2005). "Online Learning as Information Delivery: Digital Myopia." Journal of Interactive Learning Research 16(4): 353-367.

Hirschheim, R. (1992). "The Internet-Based Education Bandwagon: Look before you leap." Communications of the ACM 48(7): 97-101.

Hitt, J. and J. Hartman (2002). Distributed learning: New challenges and opportunities for institutional leadership. Washington, American Council on Education: 28.

Joint Information Systems Committee (JISC) (2007). Student expectations study. London, Author.

Jones, D., M. Cranston, et al. (2005). What makes ICT implementation successful: A case study of online assignment submission. ODLAA’2005, Adelaide.

Jones, D., K. Jamieson, et al. (2003). A model for evaluating potential Web-based education innovations. 36th Annual Hawaii International Conference on System Sciences, Hawaii, IEEE.

Jones, N. and J. O’Shea (2004). "Challenging hierarchies: The impact of e-learning." Higher Education 48: 379-395.

Jongbloed, B., J. Enders, et al. (2008). "HIgher education and its communities: Interconnections, interdependencies and a research agenda." Higher Education 56(3): 303-324.

Knowles, M. S., E. F. Holton, et al. (2005). The Adult Learner. Oxford, Butterworth-Heinemann.

Kvavik, R., J. Caruso, et al. (2004). ECAR study of students and information technology, 2004: Convenience, connection and control. Boulder, CO, EDUCAUSE Center for Applied Research.

Luck, J., D. Jones, et al. (2004). "Challenging Enterprises and Subcultures: Interrogating ‘Best Practice’ in Central Queensland University’s Course Management Systems." Best practice in university learning and teaching: Learning from our Challenges.  Theme issue of Studies in Learning, Evaluation, Innovation and Development 1(2): 19-31.

Morgan, G. (2003). Faculty use of course management systems, Educause Centre for Applied Research: 97.

Oblinger, D. (2003). "Boomers, gen-Xers and millennials: Understanding the new students." EDUCAUSE Review: 37 – 47.

Prensky, M. (2001). "Digital natives, digital immigrants." On the Horizon 9(5): 1-6.

Russell, C. (2008). E-learning adoption in a campus university as a complex adaptive system: mapping lecturer strategies, University of Leicester. PhD: 250.

Sabry, K. and L. Baldwin (2003). "Web-based learning interaction and learning styles." British Journal of Educational Technology 34(4): 443-454.

Salaway, G., R. Katz, et al. (2006). The ECAR Study of Undergraduate Students and Information Technology, 2006. Boulder, USA, EDUCAUSE Center for Applied Research.

Salmon, G. (2005). "Flying not flapping: a strategic framework for e-learning and pedagogical innovation in higher education institutions." ALT-J, Research in Learning Technology 13(3): 201-218.

Selwyn, N. (2007). "The use of computer technology in university teaching and learning: a critical perspective." Journal of Computer Assisted Learning 23(2): 83-94.

Semmar, Y. (2006). "Distance learners and academic achievement: The roles of self-efficacy, self-regulation and motivation." Journal of Adult and Continuing Education 12(2): 244-256.

Sheely, S. (2008). Latour meets the digital natives: What do we really know. Hello! Where are you in the landscape of educational technology? Proceedings of ASCILITE Melbourne 2008, Melbourne.

Siritongthaworn, S., D. Krairit, et al. (2006). "The study of e-learning technology implementation: A preliminary investigation of universities in Thailand." Education and Information Technologies 11(2): 137-160.

Somekh, B. (2004). "Taking the Sociological Imagination to School: An analysis of the (lack of) impact of information and communication technologies on education systems." Technology, Pedagogy and Education 13(2): 163-180.

Stewart, D. P. (2008). "Technology as a management tool in the Community College classroom: Challenges and Benefits." Journal of Online Learning and Teaching 4(4).

Tapscott, D. (1998). Growing up digital: The rise of the Net Generation. New York, McGraw-Hill.

Trowler, P. and P. Knight (1999). "Organizational socialization and induction in universities: Reconceptualizing theory and practice." Higher Education 37(2): 177-195.

Volery, T. (2001). "Online education: An exploratory study into success factors." Journal of Educational Computing Research 24(1): 77-92.

Watson, D. (2006). "Understanding the relationship between ICT and education means exploring innovation and change." Education and Information Technologies 11(3-4): 199-216.

White, N. (2006). "Tertiary education in the Noughties: the student perspective." Higher Education Research & Development 25(3): 231-246.

4 thoughts on “Students and e-learning – a start to the People section

  1. Pingback: PhD Update #16 – return from a break « The Weblog of (a) David Jones

  2. Pingback: Academics – the next part of the People section « The Weblog of (a) David Jones

  3. Pingback: Leaders and managers – the next bit of People « The Weblog of (a) David Jones

  4. Pingback: Lessons for e-learning from people « The Weblog of (a) David Jones

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