Teaching and Learning Forum 2000 [ Proceedings Contents ]

University students' perceptions of information technology

Elizabeth Santhanam
Centre for Staff Development
University of Western Australia
and

Carolyn Leach
Department of Genetics
University of Adelaide
    In the present age of information explosion, more and more academics, departments, faculties and/or universities are resorting to using computers and the internet in teaching/learning activities. Use of such technology is seen by some as not only aiding the processes of teaching and learning but also as being an inevitable step in the progression of higher education. However, what do we know about students' knowledge and skills, or their perceptions, relating to computers and other forms of information technology?

    An investigation was carried out to survey some aspects of science students' backgrounds, including their views regarding use of computers, familiarity with software and hardware. This pilot study at the University of Adelaide produced outcomes that may surprise some academics. The results suggest a gender bias relating to students' perception of their ability to use computers. Developing confidence in the use of computers seems to be necessary for some students, particularly the female students, if universities are aiming to develop self directed learning among students by using use computers and the World Wide Web.

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Introduction

Innovative teaching is usually promoted as being part of effective teaching and the major teaching innovation implemented by many academics in recent times is the use of computers and internet services. Online delivery of either part or whole course materials is no longer something that only the technology buff dabbles in. Many universities are convinced of the benefits such an innovation would bring and they are willing to invest large amounts of resources to facilitate the process. The main argument used in favour of computer based media for instruction is that they facilitate student learning better than the traditional methods or that they supplement the traditional methods. A survey of 1,000 higher education institutions in United States found "pervasive use of computers", even though relatively few academics were involved in distance education (Russell et al., 1995). The situation in Australia may be similar to that in the United States. Conferences on higher education, such as those organised by the Australian Society of Computers In Learning In Tertiary Education [http://www.ascilite.org.au/conferences.html], provide ample evidence of the information technology juggernaut.

While academics/departments/faculties/universities are trying to utilise computers and other information technology (IT), there are issues that require attention. One such issue is the need for higher education to be inclusive i.e. teaching should cater to all students and not just the 'privileged'. Australian universities espouse to the principles of equity in education. How inclusive is the use of computer based media for instruction? What do we know of student backgrounds in relation to computer knowledge and skills? Isn't this information necessary to facilitate student learning through computer based media, since the "design of learning material for any medium should always begin with the definition of objectives and analysis of student learning needs" (Laurillard, 1993, p. 181-182)?

Background

In Laurillard's view, "our use of IT based media over the last twenty years has been prodigious but is not matched by our understanding of it, because the emphasis has been on development and use rather than on research and evaluation" (1993, p. 223). Although there have been some studies that attempted to look at the effectiveness of IT based media in education (Lewis & Markwood, 1985; McClure & Lopata, 1995), few have investigated the readiness of students for such media. Surveys of student attitudes indicate that graduate students (Braswell, 1988; Inoue, 1998) and students who had prior computer courses or who owned computers (Geissler & Horridge, 1993) are more positive towards computer use in higher teaching/learning than other students.

In a pilot study at the University of Adelaide, student backgrounds relating to computer use, knowledge and skills as well as some factors that may influence student perceptions of their ability were investigated. Student perceptions of their ability may be an indicator of their real ability, so it can be used to identify groups of students who may require special help before implementation of computer based teaching. The outcomes of this study could be useful to the University of Adelaide and to high schools preparing students for university entry. Similar outcomes could be expected in other Australian universities.

Method

First year students taking courses offered by the Faculty of Science were invited to complete a questionnaire at the time of course registration. Out of 366 student volunteers, 347 had completed their Year 12 in South Australia and their responses were used for detailed data analyses. The raw data were examined using the SPSS software for variables that may influence students' rating of their ability to use computers.

Results

Students entering the Faculty of Science were mainly urban school leavers. Table 1 below shows the variables that were used in multiple regression analysis, to find out how student rating of their ability to use computers (dependent variable) is related to the other student variables.

Table 1: Student variables relating to information technology access/ability

Variables MeanStd devN
Ability to use computers (1=very poor, 7=very good) 3.711.58344
Access to computer at home (0=no, 1=yes) 0.840.37345
Access to Internet at home (0=no, 1=yes) 0.220.41342
Age (in years) square 389.89274.10347
Austudy/ Abstudy application (0=no, 1=yes) 0.430.50336
Done IT course in school (0=no, 1=yes) 0.460.50347
Sex (0=female, 1=male) 0.500.50347
Residence in Australia (1<5yrs, 2=5-10yrs, 3>10yrs, 4=whole life) 3.730.65343
SES (1=high, 2=medium, 3=low) 1.670.68347
English Language ability (1=poor, 4=average, 7=very good) 6.341.06342
Loge(computer use hrs/week) 1.180.92257

If data were missing, cases were excluded pair-wise in the statistical analyses. Estimation of socio-economic status (SES) was based on home postcodes and the Australian Bureau of Statistics index of education and occupation for the state. The squared values of Age of student in years and the natural logarithm values of Computer use in hours were used to obtain a better fit for the regression model. All the variables were entered in a block for the multiple regression analysis.

The independent variables in the multiple regression model explain only about a quarter of the variance in the dependent variable, so there is a large proportion of residual variance (Tables 2 & 3). This outcome is to be expected, given the low levels of correlations between variables (Table 4). The highest correlation value of 0.453 is between student rating of their ability to use computers and the natural logarithm of hours in a week spent on using computers.

Table 2: Regression Model Summary

Regression (R) R2 Adjusted R2 Std. error of the estimate
0.5130.264 0.2331.38

Table 3: Analysis of Variance (ANOVA) Summary


Sum of squares dfMean squareFSig.
Regression163.701 1016.378.5570.000
Residual457.235 2391.913

Table 4: Pearson's Correlations Between Variables


Comp
ability
Comp
access
Internet
access
Age2 Aus/
Abs-
study
IT course SexResid.
period
SESEng lang ability Loge (comp use)
Comp ability 1









Comp access 0.184**1








Internet access 0.238**0.226** 1







Age squared -0.057-0.166** 0.0371






Austudy/ Abstudy -0.073-0.187** 0.0030.081 1





IT course 0.126*0.008 -0.069-0.107 0.0491




Sex 0.159*0.059 0.106-0.077 -0.047-0.025 1



Resid. period 0.0210.023 0.000-0.074 -0.102-0.059 -0.010 1


SES -0.098-0.141* -0.157*-0.044 0.136*-0.018 -0.173**0.007 1

Eng Lang ability 0.0230.080 -0.0420.054 0.0140.059 -0.0810.190** 0.0211
Loge (comp use) 0.453**0.155* 0.348**0.181* -0.0320.056 0.0490.027 -0.0450.006 1
** p<= 0.001, * p<= 0.01

Among the student background variables that were investigated, the main predictors of student rating of their ability to use computers are the time spent on using computers and gender (Table 5). The age of students and whether or not students have completed a course in information technology in schools are the next best predictors. More time spent in using computers generally increased student rating of ability to use computers; increase in time from 0 to 10 hours per week corresponded with a sharp increase in the ability rating, but further increase in time had a marginal effect on the rating. Male students tended to rate themselves higher on computing ability than female students. The highest proportion of above average ratings for computing ability was for the 19 to 25 year old group, followed by the below 19 and finally the above 25 year old groups. Students who had completed a course in IT are more likely to rate themselves above average for ability to use computers than those who had not.

Table 5: Coefficients of dependant variables on Ability to use computers


Unstandardised
coefficients
Standardised
coefficients
tSig.Collinearity statistics
BStd. errorBetaToleranceVIF
(Constant)2.404 0.782
3.0760.002

Access to computer0.272 0.2580.0631.0550.293 0.8571.167
Access to internet0.266 0.2340.0701.1360.257 0.8161.226
Age squared-0.001 0.000-0.107-1.8210.070 0.8851.130
Austudy/Abstudy-0.110 0.183-0.034-0.5980.551 0.9311.074
Done IT course0.306 0.1790.0971.703 0.0900.9561.046
Sex0.364 0.1800.1152.027 0.0440.9491.054
Resid. period-0.004 0.139-0.002-0.0270.978 0.9381.066
SES-0.087 0.134-0.038-0.651 0.5160.9171.090
Eng lang ability0.044 0.0850.0300.516 0.6060.9341.070
Loge(computer use)0.728 0.1050.4246.929 0.0000.8231.215

Students' familiarity with computer software and hardware was also explored, and the responses are summarised in Tables 6 and 7. Almost all students who responded to this question were familiar with a Word Processor and about half of them were familiar with Spreadsheet, but few were familiar with World Wide Web (WWW) and/or Email. The most familiar computer hardware was a Personal Computer (PC).

Table 6: Familiar computer software

Software% responses (N=329)
Word processor98.8
Spreadsheet54.7
World wide web28.6
Database24.0
Graphics22.8
Email22.5
Desk top publishing18.8
Programming10.6
Projects10.0
Image processor7.3

Table 7: Familiar computer hardware

Hardware% responses (N=329)
PC66.6
Mac14.9
Other12.5
Mac and PC3.6
PC and other2.4

Discussion

The results of this pilot study indicate that entry-student backgrounds in relation to computer use, access and skill are varied. If we assume that student rating of ability to use computers is a reflection of their true ability, then mature age female students who have not done a school IT course are most in need of assistance for building confidence and enhancing ability to use computers. This outcome is partially supported by other studies, as differences were found in the attitudes and study behaviours of male and female students (Meyer, 1995; Severiens & Dam, 1994), and mature age students seem to require assistance in adapting to the university environment (Hayes, King & Richardson, 1997; Lafferty, 1996).

Although the Faculty of Science at the University of Adelaide provides opportunities for students to become familiar with some computer software, such as Word Processor and Spreadsheet packages, the need for helping students to become familiar with these packages is not as great as for some of the other packages. Email and WWW are frequently used in computer based instruction and/or assessment, and yet the outcomes of this study suggest that the majority of entry students are not familiar with such packages. Even among final year and postgraduate students, a few may be unfamiliar with Email use (Boles, 1999). Besides familiarity with the packages, lack of access may reduce the effectiveness of the medium. For instance, although the majority of students (84%, N=345) surveyed in this study had access to computers at home, only a minority (22%, N=342) had access to the Internet.

Are students disadvantaged by their inability to access and/or use computers, and if so what can be done? The answer to the above question requires further investigation into student backgrounds and the current practices at the local level. What is clearly apparent from this investigation is the diversity in student backgrounds relating to access to information technology. There also seems to be a grave need among some students for assistance to develop skills in the use of computer softwares prior to embarking on computer based instruction. Any induction programme should recognise student diversity and "should be organised primarily for students, rather than for the institution's convenience" (Billing, p. 132). If computer based instruction or computer mediated learning is to take place effectively, then access to, and familiarity in the use of, the medium is of importance.

References

Billing, D. (1997). Induction of New Students to Higher Education. Innovations in Education and Training International, 34(2), 125-134.

Boles, W. (1999). Classroom Assessment for Improved Learning: A Case Study in Using Email and Involving Students in Preparing Assignments. Higher Education Research and Development, 18(1), 145-159.

Braswell, R. (1988). Attitudes of the Returning University Student Towards the Use of Computers. ERIC database: ED320560

Gessler, J.E. & Horridge, P. (1993). University Students' Computer Knowledge and Commitment to Learning. Journal of Research on Computing in Education, 25(3), 347-365.

Hayes, K. King, E. & Richardson, J.T.E. (1997). Mature Students in Higher Education: III. Approaches to Studying in Access Students. Studies in Higher Education, 22(1), 19-31.

Inoue, Y. (1998). The University Student's Preference for Learning by Computer-Assisted Instruction. ERIC database: ED420309.

Lafferty, G. (1996). Equity, Access and Independent Learning: Maximising the Outcomes for Mature Age Students. Australian Journal of Adult and Community Education, 36(2), 103-111.

Laurillard, D. (1993). Rethinking University Teaching: A framework for the effective use of educational technology. London: Routledge.

Lewis, R. J. & Markwood, R. (1985). Instructional Applications of Information Technologies: A Survey of Higher Education in the West. ERIC database: ED270094.

McClure, C. R. & Lopata, C. (1995). Performance Measures for the Academic Networked Environment. ERIC database: ED405867.

Meyer, J.H.F. (1995). Gender-group Differences in the Learning Behaviour of Entering First Year University Students. Higher Education, 29(2), 201-215.

Russell, S. H. et al. (1995). Study of Communications Technology in Higher Education, 1994. Final Report [and] Executive Summary. ERIC database: ED404931.

Severiens, S.E. & Ten Dam, G.T.M. (1994). Gender Differences in Learning Styles: A Narrative Review and Quantitative Meta-analysis. Higher Education, 27(4), 487-501.

Please cite as: Santhanam, E. and Leach, C. (2000). University students' perceptions of information technology. In A. Herrmann and M.M. Kulski (Eds), Flexible Futures in Tertiary Teaching. Proceedings of the 9th Annual Teaching Learning Forum, 2-4 February 2000. Perth: Curtin University of Technology. http://lsn.curtin.edu.au/tlf/tlf2000/santhanam1.html


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