An Analysis of Australian Students’ Use of Information and Communication Technology (ICT)
This paper presents the analysis of Australian students’ use of Information and Communication Technology (ICT). It described their ability in using a wide range of computer applications. Secondary data (PISA data) was used in this study where there were 12, 551 students from 321 schools across Australia involved in the survey. The data was descriptively analyzed using SPSS software to identify and compare students’ ICT use and ability. The findings revealed that the majority of students were competent in using ICT both for the context of their school learning and individual use. It was also evident in the data that most students used this technology on a frequent basis.
Keywords: Information and Communication Technology, ICT, competence, degree of use, frequency of use, integration
The integration of ICT (Information and Communication Technology) into teaching and learning has become a trend nowadays. This is marked by the exclusive inclusion of this technology into educational activities run by the schools across the world. Australia is one of the countries which has seen the importance of ICT for education. A large amount of funding has been invested to support schools carrying out the ICT integration program.
The idea for investing funding for technology equipment and facilities is well supported by many ICT specialists (e.g., Achacoso, 2003; Jared Keengwe & Anyanwu, 2007). But, it is argued that such investment only is not sufficient. As Keengwe (2007) suggests, the investment in ICT should be followed by continuous evaluation in terms of how such technology is used: technology is important, but what is more important is how it can be used to enhance the quality of teaching and learning.
A number of studies have been conducted to investigate and evaluate the use of ICT in education (see, for example, Jeong, 2001; J Keengwe, 2007; Li, 2007; McMahon, Gradner, Gray, & Mulhern, 1999), but many of these studies were conducted within small scopes (i.e., conducted at one or a few institutions only). There is not much information in regard to the investigation of ICT use conducted at the national and/or international scope.
This study reviews the findings of a study which observed the use of ICT by students from a number of schools across Australia. It also seeks to provide information and recommendations for other countries (particularly developing countries) as to how ICT is used in a developed nation.
Differences in Students’ Use of ICT
Some researchers have suggested that the factors such as gender and degree of exposure to ICT have influences on the ways that students use ICT (Looker & Thiessen, 2003; Reddick, Boucher, & Groseilliers, 2000; Rideout, 2000). Looker and Thiessen (2002), for example, in their study looking at gender differences and computer use among school students in Canada, have found that, generally, male students used computers more frequently than female students. Yet again, they were also more competent in using various computer applications than their female counterparts (see, for example, Bimber, 2000; Pritchard, 1998). However, Looker and Thiessen further argue that the findings suggested that there was no significant difference between these two groups of students. Reddick et. Al. (2000) also noted differences in computer use between “heavy users” and “light users” (p.18). According to them, students who are exposed to computers more often tend to be more competent in their computer use than those who rarely engaged with computers.
Key Research Questions
The Source of Data
The data were taken from PISA (Programme for International Student Assessment) datasets that were collected in 2003. PISA put its emphasis on gathering information about students’ performance in four main areas: reading, mathematics, science and problem solving. These areas were selected because they were considered most essential for students to prepare themselves “to meet the real-life challenges” (OECD, 2005, p.12). However, starting from 2000, PISA also collected information about students’ performance in ICT, the area that is elaborated in the present research.
The data were collected from more than two hundred and fifty thousand students aged 15 years of age spread across 41 countries. All the PISA datasets can be accessed online (www.pisa.oecd.org). The current research used one of the PISA datasets, that is, the data related to students’ ICT performance and Australia was selected as its country of focus. The literature has suggested that there have not been many researches conducted in Australia to examine students’ ICT performance within the nationwide context, with the exception of the work of Moyle (2006) who investigated ICT computer integration program in a number of schools in Australia.
In addition to collecting data about students’ ICT performance, PISA also gathered information about students’ backgrounds such as personal and family background (e.g., sex, age, social, economic background, etc), school background (e.g., private or public), learning background (e.g., motivation) and so forth. Due to its limitation, the current study only selected two background information or variables (sex and number of years engaged with computer) for the purpose of data analysis.
There were 321 schools and 12, 551 students participating in the PISA study in 2003. All of them were randomly selected from all states in Australia (New South Wales: 74 schools and 2982 students; Victoria: 62 schools and 2354 students; Queensland: 48 schools and 1934 students; South Australia: 34 schools and 1234 students; Western Australia: 42 schools and 1767 students; Tasmania: 20 schools and 804 students; Northern Territory: 16 schools and 583 students; Australian Capital Territory: 25 schools and 893 students). The present study aimed to examine the Australian students’ ICT use by using the PISA data collected in 2003.
Analysis of Data
The measure of central tendency was analyzed using descriptive statistics by computing the mean scores of students’ responses. A mean score is an average score that can be used to describe the trend in a sample or population (Dancey & Reidy, 2004; Pallant, 2007). It is, however, often misused in practice (Pallant, 2007). For example, people use mean score to calculate the mean of categorical variables (e.g., the number of males and females and marital status). One way of knowing the descriptive information about these variables (instead of calculating the mean), according to Pallant, is by identifying their frequencies (e.g., through frequency command in the SPSS).
Standard Deviation (SD) was also considered in this research to see the average distance of individual observations from the group mean (Harris, 1998). “The SD is a measure of how much the scores in the sample vary around the mean” (Dancey & Reidy, 2004,p.72). If individual observations vary greatly from the group mean, the standard deviation is large, and vice versa. , Dance and Reidy further add that the Standard Deviation can be very useful for further or in depth data analysis. In this research context, SD is used only to give an idea of the variability of individual scores.
Research Question 1 and 2
The findings revealed that the majority of students use ICT from once a week or a month to a few times a week (M = 2.91, SD = 0.76). The activities that students usually do a few times a week are “looking up the internet” (M = 2.09, SD = 0.91); “word processing” (M = 2.20, SD = 0.92); and “using e-mail or chat rooms” (M = 2.21, SD = 1.29). Meanwhile, examples of activities conducted once a week or a month are “playing games” (M = 2.70, SD = 1.29); “internet for group collaboration” (M = 2.94, SD = 1.32); “downloading software from internet” (M = 2.96, SD = 1.34); and “drawing, painting or graphics programs on a computer” (M = 3.16, SD = 1.25). Among the least frequently practiced activities (less than once a month) are “using educational software” (M = 4.04, SD = 1.09) and “computer programming” (M = 3.57, SD = 1.35). Table 1 provides the details of students’ frequency of computer use.
Students’ Frequency of ICT Use
1 Response Scale: 1= almost everyday, 2 = a few times each week,
The study also observed the differences in the frequency of computer use between male (N = 6335) and female students (N = 6216) and between the least experienced students (i.e., less than a year engaged with computers) and the most experienced students (i.e., more than five years). The findings, as shown in Figure 1, suggest that there is a difference of frequency of computer use between male and female students where the male students use computers more often (M = 2.76) than their female counterparts (M = 3.06).
Figure 1: Frequency of computer use and gender differences
The findings, as indicated in Figure 2, also revealed that students who have more years engaged with computers tend to use the tools more often than those of shorter years. Their mean scores are 2.84 (more than 5 years), 3.02 (3 to 5 years), 3.15 (1 to 3 years) and 3.34 (less than 1 year) respectively.
Figure 2: Frequency of computer use and number of years engaged with computer
Students’ ICT Ability
Response Scale: 1 = I can do this very well by myself, 2 =I can do this with the help from someone, 3= I know what this means but I cannot do it, and 4 = I don’t know what this means.
Students’ ICT Ability
Research Question 3 and 4
The findings, as shown in Table 2, have shown that most students have the ability to operate a wide range of computer applications (M = 1.31, SD = 0.33) such as “starting a computer game” (M = 1.12, SD = 0.42), “open a file” (M = 1.06, SD = 0.29), “create/edit a document” (M = 1.11, SD = 0.42), “managing files such as copying (M = 1.16, SD = 0.48) and deleting (M = 1.06, SD = 0.32)”, “internet (M = 1.05, SD = 0.27), “create presentation” (M = 1.30, SD = 0.62) and “write and send emails” (M = 1.11, SD = 0.43).
The overall mean score (M = 1.31, less than 1.5) also indicates that the students can do most of the computer applications very well by themselves. In other words, they do not need any assistance from other persons. The only applications that students may seek support or assistance are “using software to find and get rid of viruses” (M = 1.87, SD = 0.89), “create a computer program” (M = 2.20 , SD = 0.94), “using a spreadsheet to plot a graph” (M = 1.59, SD = 0.81), and “create a multi-media presentation” (M = 1.71, SD = 0.79). This could be the case because all these three applications require them to have advanced computer skills.
With regard to ICT ability and gender differences, the findings, as shown in Figure 3, suggested that male students are more competent in using ICT (M = 1.27 ) than the female students (M = 1.35). Despite their difference in ICT competence, both male and female students, as indicated by their mean score (M = less than 1.50), are categorized as competent users.
Figure 3. ICT ability and gender differences
The findings, as shown in figure 4, also revealed that differences in the number of years in using computers affect students’ ICT ability differences. The students with more years (in using ICT) are more competent than those with fewer years.
Figure 4. ICT ability and number of years using computer
The findings suggest that generally Australian students can be considered as the frequent users of ICT and this is a good indication for the successful implementation of ICT integration into teaching and learning. As Keengwee (2007) advises, the success of ICT integration does not only depend on how much money spent for the procurement of technology facilities, but also depends on how frequently they are used for facilitating teaching and learning.
The findings also indicate that Australian students are competent in using ICT. The majority of them do not have any difficulty in operating a wide range of computer applications including word processing, sending and receiving emails, creating and saving files and so forth. It is suggested that students’ ability in using ICT is also one important factor which can help determine the success of ICT integration (Marwan, 2008).
It is also revealed in the findings that male students generally use ICT more frequently than the female students. They are also more competent in ICT than their female counterparts. This is consistent with the findings of other studies (e.g., Looker & Thiessen, 2003; Reddick et al., 2000; Rideout, 2000) which indicate that male students are more competent in the use of ICT than the female students.
Implication and Recommendation for Future Research
Australian students’ ability and frequency of use of ICT may be as the result of effective introduction of ICT in the schools. If this is the case, the focus of ICT integration should be directed to achieve the effective attainment of knowledge through technology. That is, teachers should be aware that efforts need to be made to ensure that teaching using ICT can provide better learning outcomes than that of the conventional teaching.
The present study only looks at how frequently and how well students can use ICT. It, however, does not investigate the effectiveness of ICT in helping students gain better learning (or more knowledge). Thus, it opens up the possibility for the conduct of further study which investigates how well ICT can provide better learning outcomes for students.
In addition, the current research is conducted within the framework of quantitative inquiry. As argued, this type of research may have limitations in terms of the depth of information it can attain. Thus, further research for better understanding students’ use of ICT can also be conducted in the form of qualitative research.
Achacoso, M. (2003). Evaluating technology and instruction: literature review and recommendations. Texas: The University of Texas.
Bimber, B. (2000). Measuring the gender gap on the internet. Social science quarterly, 81(3), 868-876.
Dancey, C. P., & Reidy, J. (2004). Statistics without math for psychology (Third ed.). Harlow, England: Pearson.
Harris, M. B. (1998). Basic statistics for behavioural science research (Second ed.). Boston: Allyn and Bacon.
Jeong, C. H. (2001). Gender differences in computer attitudes: new evidence from Korea. International Review of Public Administration, 6(2), 115-123.
Keengwe, J. (2007). Faculty integration of technology into instruction and students' perceptions of computer technology to improve student learning. Journal of Information Technology Education, 6, 169-180.
Keengwe, J., & Anyanwu, L. O. (2007). Computer Technology-Infused Learning Enhancement. Journal of Science Education and Technology, 16(5), 387-393.
Li, Q. (2007). Student and teacher views about technology: A tale of two cities? Journal of Research on Technology in Education, 39(4), 377-397.
Looker, E. D., & Thiessen, V. (2003, 20 March 2008). The digital divide in Canadian schools: Factors affecting student access to and use of Information Technology Retrieved 20 March, 2008, from http://www.cmec.ca/stats/pcera/RSEvents02/EDLooker_OEN.pdf
Marwan, A. (2008). Teachers' perceptions of teaching with computer technology: Reasons for use and barriers in usage. International Journal of Instructional Technology and Distance Learning, 5(6), 35-42.
McMahon, J., Gradner, J., Gray, C., & Mulhern, G. (1999). Barriers to student computer usage: staff and student perceptions. Journal of Computer Assisted Learning, 15(302-311).
OECD. (2005). PISA 2003 data analysis manual: SPSS users. Retrieved 20 March, 2008, from www.pisa.oecd.org
Pallant, J. (2007). SPSS survival manual: a step by step guide to data analysis using SPSS for windows (Third ed.). NSW: Allen and Unwin.
Pritchard, R. A. (1998). Resistance in futile, or is it? Gender lessons from micro cyber community. Canadian Women's Studies, 17(4), 72-75.
Reddick, A. C., Boucher, M., & Groseilliers. (2000). The dual digital divide: The information highway in Canada. Ottawa: Public Interest Advocacy Center.
Rideout, V. (2000). Public access to the internet and the Canadian digital divide. The Canadian Journal of Information and Library Science, 25(2/3), 1-21.
About the Author:
Ardi Marwan is a lecturer at the State Polytechnic of Pontianak, Indonesia. He obtained his Masters degree in the field of education from Monash University in Australia. He can be reached at : email@example.com
|November 2008 Index|