The nature of information technology and its implications for

practices and policy in higher education

Carmel McNaught

Academic Development Unit

La Trobe University

Melbourne

Australia

C.McNaught@latrobe.edu.au

http://www.adu.latrobe.edu.au/

Abstract

Are computers just another tool for teaching? Nice images, lots of memory, but nothing fundamentally new? Or have we entered a ënew ageí in our ability to explore and represent knowledge, to present knowledge to our students, and to design systems of education for delivery of this knowledge to large numbers of students? These are not simple questions to answer. This paper will explore some of the epistemological and pedagogical characteristics of information technology.

The consequences of these characteristics for the design of on-campus and open learning systems of education will be described in terms of the much greater number of options which are now available for course design and delivery. It will be suggested that the university of the future will use this flexibility to create a variety of pathways for students to follow. This may relate to the ways in which students mix on-campus and distance courses, to flexibility in the design of individual courses, to changes in assessment options and to altered relationships between the work place and university education.

1 Characteristics of information technology

By appreciating the metaphorical power of the computer as well as its technical capabilities, ... the computer is more than merely a sum of its component parts. It has become an ëobject-to-think-withíóstimulating thought and providing a new medium of expression.

(McQuillan [1], p. 650, using Turkle [2])

There is no doubt that computers have transformed the working and living conditions of many people. Four of these transforming characteristics are:

However, much of the current debate centres around the logistics and economics of technological advancesóthe what?, when? and how? type questions. There are also fundamental epistemological questions which need to be exploredówhy? and towards what future?

2 Epistemological implications

As we represent data in new ways, do we ask new types of questions? There are some indications that this is occurring. As we share our ideas in new ways and in new time frames, do our thinking patterns change?

We need to consider what epistemological shifts may be facilitated by current technologies. Some of these are:

Batson [3] lists nine dichotomies in an attempt to map the epistemological shifts that are facilitated by current technologies. As with all attempts to dichotomise issues, Batsonís framework (Table 1) should not be taken as implying that all traditional approaches on the left no longer have value and should be replaced by the new approaches shown on the right. It does, however, provide a framework where new approaches can be seen as presenting fresh opportunities in higher education.

Table 1 Epistemological shifts that are facilitated by current technologies (after Batson [3])


Traditional View

New Tendency

Comments
1. Knowledge as an object

(printed, storable)

(closure is possible)

Knowledge as conversation

(consensus, process)

(closure is difficult)

This is not to say that knowledge was only viewed in an objective framework before the advent of computers, but rather that the fluidity of knowledge exchange using computer technology is beginning to challenge an objective paradigm in new ways. This is the first paper I have written citing email discussion groups and Web sites. The shift in my own thinking and academic practices has been marked. The ëauthorityí of the words on the screen has added another resource to my literature searching. Note that this is an enrichment of my knowledge base, not a replacement of one resource by another, and brings new criteria into my assessment of the value of ideas and strategies I read about onscreen. I have a sense of being more critical, less reliant on the authority of other reviewers. This places new responsibilities on academicsóboth in their own scholarly work and in relation to the academic training they provide for their students.
2. Knowledge identified with individual ëexpertsí (e.g. theories are labelled as Freudian, Darwinian, etc.) Knowledge identified with community

(e.g. public email discussions)

This table was originally posted on the AAHESGIT (American Association for Higher Education Special Group on Information Technology) listserv and opened to public debate. Many of the comments I am including have been stimulated by that open, quite rapid, international debate.
3. Teaching as conveying knowledgeTeaching as getting students engaged in the knowledge-development process
Interactive multimedia has enabled the construction of resources for students to use in constructing their own understandings. Computers offer more dynamic opportunities for using visual media to represent both concrete and highly abstract entities and relate these representations. A good example is use of multiple representations by Thomason, Cumming and Zangari [4] in their statistics multimedia package where students can manipulate and construct their own distributions and visually solve statistics problems in several distribution playgrounds. The emphasis is on personally held constructs and not on formula-driven solutions.
4. Professional collaboration still difficult in some fields Collaboration seen as how knowledge is developed and therefore legitimate Collaboration is now much easier at both national and international levels. Sharing of large data files on a global basis is now relatively easy, making genuinely collaborative work across continents and cultures a reality. Collaborative grants between universities which are thousands of kilometres apart are actively encouraged by several funding agencies and the use of email and computer conferencing are normal professional practices.
5. Publications are refereed before distribution Publications may be refereed ex-post facto Some Web sites now clearly distinguish more finished papers from works in progress. An example is the Committee for the Advancement of University Teaching (CAUT) Humanities and Social Sciences site called ultiBASE [5] where active debate is encouraged on posted papers. A traditional international journal HERD (Higher Education and Research Development) has just announced that it will use this site in a developmental/ public refereeing mode.

Traditional View

New Tendency

Comments
6. Linear favoured over associational thinking Associational thinking gains credence
The advent of hypertext media, both in instructional interactive multimedia and in hypertext essays on the Web illustrates this well. Beaumont and Brusilovsky [6] and Ecklund [7] discuss the relationship between cognitive structures, instructional design and learning outcomes; they argue that people learn by constructing several links between concepts, personal records of events and formal theories. Hypertext media are therefore likely to facilitate learning. Kozma [8], p. 197 describes the critical capability of the computer to proceduralise and relate the relationships between symbols in mental models. One powerful tool that has evolved is concept mapping software that is used in variety of ways (e.g. Gaines and Shaw [9]).

Many Web sites are still very linear in design and therefore underutilise the potential of the medium; a good example of a hypertext essay on the Web is on the Open Learning Technology Corporation (OLTC) [10] site.

7. Classroom and the real world seem separate Classroom connected to off-campus world
Open learning now applies to both on- and off-campus courses. Email and electronic conferencing (both desk-top and room-based) technologies have reduced ëthe tyranny of distanceí considerably (Ring and Watson [11], Goddard [12]). Many large IMM projects are being converted to Web delivery. The technology for this exists; large resources (video, animations) can be on a local CD-ROM but the interactive elements can be Web-based. Substantial projects (e.g. ChemCAL, 70 hours of introductory chemistry, McTigue et al. [13]) are currently being reworked in this way.
8. Visualisation a ëfrillí in some fields Visualisation as a knowledge path The supremacy of text is changing. There is an increasing awareness that students are becoming more visually literate and that the ways in which they learn from visual material may be changing (e.g. McNaught, McTigue and Tregloan [14]).

Computers afford opportunities for dynamic use of visual material. For example, visual search engines on the Web are being produced (e.g. Appleís Hotsauce project [15]) which should allow more flexibility in our explorations of the loosely structured world of the Web; one can literally look around for information, rather than searching with pre-determined key words. This is done using the mouse to move into a specific domain of knowledge and then down through layers to reach specific Web sites.

9. Experimental methods limited to certain fields Simulation used as a means to explore knowledge in many fields Data mining using genetic algorithms or neural networks is now being used to rapidly sift enormous amounts of data relatively rapidly, looking for new patterns and relationships. These techniques are used in a wide range of disciplines from business to astronomy (Kiernan [16], Matthews [17]).

3 Pedagogical implications

My own interest is in the use of information technology in teaching and learning, though the distinction between research and educational uses of computers is not at all clearcut. Certainly, computers for use in educational instruction have developed considerably since the days when the computer was the instrument of study, rather than the instructional tool for study. Heppell [18] argues that there have been four stages in the development of computers over time, with a fifth stage now commencing. The educational community and society have moved from the question of ìwhat is a computer?î to ìwhat can I do with a computer?î. These stages are shown in Figure 1.

-----------------Time---------------------------------------->

1. Computers as a topic of study

2. Computer supports learning with task specific programs

3. Computer supports learning with generic, content free programs

4. Computer supports specific needs through component software

5. Pedagogy radically changes to reflect computersí potential

-----------------Developing role of computers----------->

Figure 1 Five stages in the development of computers (after Heppell [18], p. 101)

Some of these pedagogical changes relate to ways in which interactions between teacher and students can be conceptualised. Laurillard [19] describes a teaching and learning model containing the four aspects of discussion, interaction, adaptation and reflection. She argues that the use of multimedia can enhance the teaching-learning process effectively by facilitating ëconversationsí between students, teachers and instructional material. Many innovative curriculum projects in higher education involve stand-alone interactive multimedia modules. Others involve open learning modules using network delivery. There are also other curriculum strategies which can enhance student learning such as email discussion groups, online help processes, students producing their own multimedia presentations, and computer databases used for cooperative learning projects. Certainly the quality of some of these innovations is not high but there are enough examples now of projects (Wills and McNaught [20]) where improved student learning has been demonstrated for us to feel that the investment may indeed pay off!

These few brief comments can only highlight the pedagogical potential for using computers in a variety of ways to enhance student learning. Are these pedagogical questions just for individual teachers to consider, or are there larger policy questions for universities to consider?

4 Implications for university policy

Laurillard [21] examined the balance of student study activities in a UK university. She mapped the distribution of student learning activities by type of activity and nature of the process, using four categoriesóattending, practising, discussing and articulating. This data was are taken from an engineering department in a campus-based university. There were several differences found in other departments and in another university but the general pattern was usually the same, with attending being by far the commonest activity:

Table 2 Distribution of student learning activities in a UK university (Laurillard [21])

Attending

Practising

Discussing

Articulating
Lectures
15
0
0
0
Audio-

visual

1
0
0
0
Tutorials
3
0
1
0
Practice
0
10
0
0
Reading
2
0
0
0
Assign-

ments

0
0
0
8
Totals
21
10
1
8

Laurillard then describes how the use of information technology could be used to redistribute student learning time in a different way, so as to achieve a more effective balance between active and passive modes of learning. She describes this as follows:

ëTo the standard methods of university teaching we could add computer-assisted learning (CAL, to represent any form of computer-based teaching program), audio-graphics (to represent synchronous conferencing), computer-mediated conferencing (CMC, as the asynchronous form of conferencing), and interactive computer-marked assignments (ICMA) to cover those aspects of assessment that can be handled adequately by a program. The same total student study time can then be redistributed over a wider range of teaching methods, resulting in a more balanced distribution across the key types of learning activity, as shown again in the final totals for each line.í

Table 3 Possible redistribution of student learning activities (Laurillard [21])

Attending

Practising

Discussing

Articulating
Lectures
5
0
0
0
Audio-

visual

1
0
0
0
Tutorials
2
0
2
0
Practice
0
5
0
0
Reading
3
0
0
0
Assign-

ments

0
0
0
5
CAL
1
6
0
0
Audio-

graphics

0
2
1
0
CMC
2
0
1
1
ICMAs
0
0
0
3
Totals
14
13
4
9

This is a highly significant piece of research as it shows how the technology infrastructure in a university can be organised to maximise the potential for more effective student learning.

This piece of research can be embedded into a theoretical framework where we can view university policy making as an evolutionary scenario or a revolutionary scenario. Evolutionary changes are essentially ëmore of the sameí, an adaptation of new technologies and conditions into existing frameworks; revolutionary changes work towards the development of new models of communication and accountability.

The differences between the two scenarios can be considered for a range of policy issues, including:

Alley [22] dichotomises possible decision making about information technology policy into an evolutionary scenario or a revolutionary scenario (Table 4). As with Batsonís [3] epistemological discussion the use of a dichotomised framework can allow one to see and examine options. Both Batson and Alley presented their models to an email discussion group and the listings elicited several responses. Rapid qualifications, clarifications, alternativesóan ideal medium for planning on a regional or global basis.

Table 4 Evolutionary and revolutionary approaches to changes in higher education (Alley [22])

Comparison Dimension

Evolutionary Approach

Revolutionary Approach

Facility

technology


High bandwidth fixed network

Dial-up phone lines
Media focusVideoWeb, email, chat
Instructional

design

Distribute traditional teaching Invent new learning
Instigated byInstitution Individual staff
Funded out ofState/Institution special fund

(not base funds)

Dept./ Institution

base funds

Driven byProductivity for provider Quality for

clients (staff and students)

InvestmentCapital intensive Labour intensive
PlaceUser comes

to site

Site is with user
TimeFixed, prescheduled Unscheduled
Progress/

assessment

Lock-step pace Self-paced
SociologyRetains classroom

groups idea

Individual
Geographic

extent

City or state Planet Earth

There are congruences or patterns which exist between epistemological, pedagogical and policy perspectives. The consequence of these patterns for the design of on-campus and open learning systems of education is that there are now a much greater number of options which are available for course design and delivery. It is hoped that the university of the future will use this flexibility to create a variety of pathways for students to follow. This may relate to the ways in which students mix on-campus and distance courses, to flexibility in the design of individual courses, to changes in assessment options and to altered relationships between the work place and university education.

Both Laurillard [21] and Alley [22] describe differing ways in which students could mix on-campus and distance courses. They show the need for flexibility in the design of individual courses, and for changes in assessment options. While they do not explicitly discuss altered relationships between the work place and university education, the options for part-time and mature age students could clearly be much greater.

As the distinction between on-campus and distance courses blurs (with maybe very real changes to student funding formulas!), so also might the distinctions between educators and employers. A current example of a partnership between business and a university is the involvement of a large accounting firm in the teaching of accountancy at The University of Melbourne.

Several surveys of professions have indicated concerns about studentsí professional skills. Computers offer potential for assessment to move further away from recall towards higher order cognitive skills in ways that may prepare students better for their future careers. One example is a veterinary microbiology course at The University of Melbourne where the entire course is centred around a suite of computer-based case studies. Students use extensive data bases to diagnose and treat particular bacterial diseases. For both the assessments during the course and for the final examination, the balance has changed from requiring mostly recall and comprehension to having the majority of the assessment based on higher order cognitive skills with a focus on professional diagnostic skills (McNaught, Whithear and Browning [23]).

So, the image I want to end on is a positive if fluid one. I have an increasing number of questions and only partial answers to any of them. Information technology offers enormous potential to us in viewing knowledge and in designing flexible and appropriate higher education systems. A time of intense exploration lies ahead. Let us not set our universities in electronic concrete that locks us into modes of delivery that are too specialised and inflexible. Rather let us keep to a vision of global sharing of knowledge and systems in a manner that allows the needs of individuals learners and communities to be met.

5 References

1. McQuillan, P. Computers and pedagogy: The invisible presence. Journal of Curriculum Studies, 26(6), 631-653 (1994).

2. Turkle, S. The Second Self: Computers and the Human Spirit. Simon and Schuster, New York. (1984).

3. Batson, T. Posting to the AAHESGIT (American Association for Higher Education Special Group on Information Technology) listserv 21 September. (1995).

4. Thomason, N., Cumming, G. and Zangari, M. Understanding central concepts of statistics and experimental design in the social sciences. pp. 59-81 in K. Beattie, C. McNaught and S. Wills (Eds.). Interactive Multimedia in University Education: Designing for Change in Teaching and Learning. Elsevier, Amsterdam. (1994).

5. ultiBASE (University Learning and Teaching In Business Art Society and Education) Web site. http://ultibase.rmit.edu.au/

6. Beaumont, I. and Brusilovsky, P. Adaptive educational hypermedia: From ideas to real systems. pp. 93-98 in H. Maurer (Ed.). Educational Multimedia and Hypermedia, 1995. Association for the Advancement of Computing in Education. Proceedings of Ed-Media 95óWorld conference on Educational Multimedia and Hypermedia, Graz, Austria, 17-21 June. (1995).

7. Eklund, J. Cognitive models for structuring hypermedia and implications for learning from the world-wide web. pp. 111-118 in H. Maurer (Ed.). Educational Multimedia and Hypermedia, 1995. Association for the Advancement of Computing in Education. Proceedings of Ed-Media 95óWorld conference on Educational Multimedia and Hypermedia, Graz, Austria, 17-21 June. (1995).

8. Kozma, R. B. Learning with media. Review of Educational Research, 61(2), 179-211 (1991).

9. Gaines, B. R. and Shaw, M. L. G. Concepts maps as hypermedia components. University of Calgary. Web site on concept mapping

http://ksi.cpsc.ucalgary.ca/articles/ConceptMaps/

10. OLTC (Open Learning Technology Corporation) Web site. Learning with software: pedagogies and practices. http://www.oltc.edu.au/cp/default.html

11. Ring, J. and Watson, A. The virtual campus: ECUís developmental path. pp. 205-221 in K. Beattie, C. McNaught and S. Wills (Eds.). Interactive Multimedia in University Education: Designing for Change in Teaching and Learning. Elsevier, Amsterdam. (1994).

12. Goddard, J. Perspectives on videoconferencing. pp. 205-214 in J. Pearce, A. Ellis, C. McNaught and G. Hart (Eds.). Learning with Technology. Proceedings of the Australian Society for Computers in Learning in Tertiary Education ë95 conference, The University of Melbourne, 3-7 December. (1995).

13. McTigue, P.T., Tregloan, P. A., Fritze, P.A., McNaught, C., Hassett, D. and Porter, Q. Interactive teaching and testing tutorials for first year tertiary chemistry. pp. 466-471 in H. Maurer (Ed.). Educational Multimedia and Hypermedia, 1995. Association for the Advancement of Computing in Education. Proceedings of Ed-Media 95óWorld conference on Educational Multimedia and Hypermedia, Graz, Austria, 17-21 June. (1995).

14. McNaught, C., McTigue, P. and Tregloan, P. Studentsí understanding of moving visual images in interactive multimedia. Paper presented at From Virtual to Reality. Apple University Consortium Academic Conference, The University of Queensland, 24-27 September. Paper published on CD-ROM and on the Web at http://www.uow.edu.au/auc. Abstract on p. 25 of the conference proceedings book which has CD-ROM enclosed. (1996).

15. Apple Computers. HotSauce Web site. http://hotsauce.apple.com/

16. Kiernan, V. Growing money from algorithms. New Scientist, no. 1954, 3 December, 25-27 (1994).

17. Matthews, R. Panning for data. New Scientist, no. 2031, 25 May, 26-29 (1996).

18. Heppell, S. Eyes on the horizon, feet on the ground? In C. Latchem, J. Williamson, and L. Henderson-Lancett (Eds.), Interactive Multimedia: Practice and Promise. Kogan Page Ltd., London. (1993).

19. Laurillard, D. Rethinking University Teaching: A Framework for the Effective Use of Educational Technology. Routledge, London. (1993).

20. Wills, S. and McNaught, C. Evaluation of computer based learning in higher education. Journal of Computing in Higher Education, 7(2), 106-128 (1996).

21. Laurillard, D. The changing university. ITFORUM paper no. 13. Posted to the ITFORUM listserv on 30 April. (1996).

22. Alley, L. Posting to the AAHESGIT (American Association for Higher Education Special Group on Information Technology) listserv 21 February. (1996).

23. McNaught, C., Whithear, K. and Browning, G. The role of evaluation in curriculum design and innovation: A case study of a computer-based approach to teaching veterinary systematic bacteriology and mycology. pp. 295-308 in K. Beattie, C. McNaught and S. Wills (Eds.). Interactive Multimedia in University Education: Designing for Change in Teaching and Learning. Elsevier, Amsterdam. (1994).