The correlates of the digital divide and their impact on college student learning

Computers & Education 50 (2008) 421–436 www.elsevier.com/locate/compedu

The correlates of the digital divide and their impact on college student learning q Flora F. Tien a

a,*

, Tsu-Tan Fu

b,c

Center for Teacher Education, National Taiwan University, Roosevelt Road, Sec. 4, No. 1, Taipei City 106, Taiwan b Institute of Economics, Academia Sinica, Taiwan c Center of Survey Research, Academia Sinica, Taiwan Received 17 January 2006; received in revised form 21 July 2006; accepted 29 July 2006

Abstract By focusing on two dimensions of the digital divide—computer use and computer knowledge, this study explores four research questions: (1) What are the undergraduates doing with the computers they use at colleges? (2) How do undergraduates perform in regard to computer knowledge and skills? (3) With what is the digital divide among college students correlated? (4) What consequences does the digital divide have for student academic performance? In order to answer these research questions, a national survey was conducted. The survey investigated 3083 first-year college students of 12 4-year universities in Taiwan. A total of 2719 of them completed the questionnaires resulting in a response rate of 88.2%. In this study, the digital divide is measured in terms of computer use, which includes a variety of purposes for using computers and academic-related work as a proportion of total computer hours, and computer knowledge. Multiple regressions and a generalized ordered logit, i.e. a partial proportional odds model, are employed. The main findings include the following: (1) Undergraduates use computers not only for fulfilling their academic requirements and searching for information, but also for entertainment. On average, undergraduates spend about 19 h per week using computers, of which 5 h are academicrelated. (2) Most undergraduates perform at the middle average level in terms of computer knowledge. (3) No significant differences among correlates in relating to demographic and socioeconomic family background were found in predicting the various purposes in using computers. (4) Students who are female, whose fathers and/or whose mothers are from minorities, whose fathers are blue-collar workers or unemployed, who study in the fields of the humanities and social sciences, and who enter private universities are at a disadvantage in terms of computer skills and knowledge. However, female students, students whose mothers were less educated and students who enroll in private universities are more focused computer users in terms of allocating time to academic-related work. (5) Computer knowledge and devotion to using computers for academicrelated work have a moderate effect on college student learning, while the various other uses of computers do not. Of the different kinds of computer knowledge, it is the knowledge of software that helps students to learn the most. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Adult learning; Country-specific developments; Post-secondary education; Teaching/learning strategies

q This research was partially supported by the Ministry of Education Program for Promoting Academic Excellence of Universities under the grant number 91-H-FA08-1-4. This research is also partially supported by the National Science Foundation under the grant number NSC92-2413-H-002-007-FF. * Corresponding author. Tel.: +886 2 33665709/29378650; fax: +886 2 29392033. E-mail address: fl[email protected] (F.F. Tien).

0360-1315/$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.compedu.2006.07.005

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1. Introduction The ‘‘digital divide’’ is one of the ways in which inequality is measured in a knowledge society. It is a technology capacity gap that exists between those who are ‘‘information-rich’’ and those who are ‘‘informationpoor’’. Gender, age, ethnicity, social economic status, geographic area, and the education level of individuals are often reported as correlates that cause the gap (NTIA, 1995, 1998, 1999, 2000; Natriello, 2001; Servon, 2002). Countries and the groups within countries are often the units that researchers use to make comparisons. The former are often related to the international digital divide, and the latter to the domestic digital divide (Bridges.org, 2001). Although the digital divide is recognized as a social problem of inequality, there is no consensus among scholars on how to measure it (Bridges.org, 2001; Kaminer, 1997). Since the US Department of Commerce began to use the digital divide term in a series of reports in the mid-1990s, the digital divide has been referred to as the lack of access to information technology such as Internet access or computer ownership for specific groups (Papastergiou & Solomonidou, 2005; Tseng, 2003a). As the digital divide concept has been narrowly conceptualized as an access problem, it would be misleading to relate it solely to equipment purchasing. Such an access measure of the digital divide has been criticized by many scholars in that it neglects what people are doing with computers and what they are able to do with computers (Attewell, 2001; Harper, 2003; Light, 2001; Servon, 2002; Warschauer, 2002). In addition to the disparities in terms of access, which are referred to as the first digital divide by Attewell (2001), computer use is regarded as the second digital divide. After all, having the opportunity to access computers does not equal being able to use information technologies. Studies in recent years have paid more attention to the use of time and the purpose behind using computers, despite the focus of the digital divide being overwhelmingly on the issues of access to technology and social exclusion (Fairlie, 2005; Mansell, 2002). The results of these studies are mixed. In contrast to studies on computer access, American studies on computer use, for example, reveal that the poor and minorities are more likely than the affluent and the majority to report their daily use of computers in their school work (Attewell, 2001; Coley, Cradler, & Engel, 1997). Likewise, surveys on Canadian high school students indicate that, although rural youth are less likely to have access to computers in their homes, the frequency of computer use is not compromised by this trend (Looker & Thiessen, 2003). Whether or not other countries reveal similar results is an interesting research question that needs to be explored. Such studies will advance our knowledge in terms of understanding the nature of the digital divide. In their study in which they investigated the first digital divide for German people, Korupp and Szydlik (2005) suggested that exploring the second digital divide was needed and would become a pressing future issue. In this paper, we use Taiwan, where access is not a serious problem while computer use needs to be explored, as a case study for examining the correlates of the digital divide. The term ‘‘digital divide’’ also refers to gaps between groups in terms of their ability to use ICT effectively, due to differing knowledge and technical skills. Computer knowledge and skills represent the mental capability that a person has when it comes to handling computers, and is a form of human capital that he or she possesses when facing technological change. Mansell (2002) suggested that special attention with regard to the digital divide should be given to human capital development through knowledge advancement and training. When considering computer use, the presence of knowledge and the application of skill become pivotal elements (Harper, 2003). As the access does not guarantee the use of computers, the use of computers does not necessarily lead the users to have adequate knowledge and skills to operate computers. Computer knowledge and skills, therefore, become the third dimension of the digital divide. Previous large-scale surveys relied heavily on self-evaluation data to measure computer knowledge and skills. For instance, Torkzadeh and Lee (2003) used a Likert-type scale, inviting subjects to point out which description best fit their knowledge levels regarding computer hardware. Tseng (2003a, 2003b) asked subjects to report their skill level based on the different kinds of computer software that subjects operate. Duvel and Pate (2003) asked university students to rate themselves on a 3-point Likert scale where the response options covered the range from novice to average in terms of complete familiarity with the task. Since self-evaluated data are subjective and may not reflect subjects’ real levels of computer knowledge and skills, an effort to overcome this research limitation needs to be made. In this study, we utilize an objective test to measure the extent to which an undergraduate student possesses knowledge regarding computer software, computer hardware, and the Internet.

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In order to bridge the digital divide, many countries in the world, such as the United States, Finland, France, and Taiwan, rely on education—from the elementary education level to the higher education level—to train and enhance the ability of their future citizens to acquire information knowledge and skills. As the economy shifts to an information base (OECD, 1996), higher education plays a crucial role in knowledge transmission, knowledge creation, and knowledge transfer. Undergraduates are prepared to be the main future human power in a country. They must be conversant with information technology in order to face the challenges of a global knowledge-based economy. Therefore, undergraduates should be one important group of subjects when inquiring into the digital divide. This paper focuses on the higher education level. Specifically, one of the research questions in this paper to be investigated is the following: ‘‘What are the correlates of the digital divide for undergraduates in Taiwan?’’ Knowing the correlates of the digital divide is not sufficient in itself, and we need to know more about whether the digital divide has a positive or negative impact upon people (Bridges.org, 2001). In their suggestions for future research, Lewis, Coursol, and Khan (2001) expressed a crucial need for research that explores the impacts of technology on student learning. Previous studies concerned with the impacts of computer technology focused either on the changes that computer technology brought to institutions (Foster & Hollowell, 1999; Wells, Silk, & Torres, 1999; Zain, Atan, & Idrus, 2004), or the influence of computer technology on teaching (Albright, 1999; Benson et al., 2002; Chang, Chou, Chen, & Chan, 2004; Lassner, 2000; Li, 2003), or the impact of technology on curriculum design (Johnston & Webber, 2003; Jones & O’Shea, 2004), or Internet addiction (Chou & Hsiao, 2000). More studies on the impacts of ICT on undergraduate learning, such as their academic achievements, need to be conducted. Another research question posed in this study is therefore: ‘‘What are the impacts of the digital divide on undergraduates in Taiwan?’’. By investigating 125,000 American undergraduates, Kuh and Vesper (2001) examined the relationship between the impact of the use of IT and the acquisition of desired outcomes of college. They found that increased familiarity with computers was positively related to developing important skills and competencies such as thinking analytically and logically, synthesizing ideas and concepts, and social skills. However, three limitations existed in their studies. First, the questionnaire used did not differentiate among different types of IT, such as software, hardware, and the Internet. Examining the impact of different types of IT on learning would provide suggestions to educators regarding which dimension of IT education should be improved. Secondly, the study did not provide information regarding the amount of time and effort students expend using IT and for what purposes. However, it is important to know to what extent students engage in IT in their meaningful social interactions. What the undergraduates are doing with the computers they use needs to be asked. The third limitation was that the study did not inquire about academic achievement. Since academic achievement is one of the important cognitive outcomes of college learning, the relationship between the use of IT and academic performance needs to be investigated. In this study, we will concentrate our efforts on (1) exploring the impact of different types of IT on learning; (2) surveying the amount of time spent on and the various kinds of IT activities students engage in; and (3) investigating the impact of the digital divide on undergraduate academic performance. We have chosen Taiwan as the country for probing into these questions. According to the World Economic Forum (2005), Taiwan is ranked 15th among 104 countries in terms of its networked readiness index. This index measures ‘‘the degree of preparation of a nation or community to participate in and benefit from ICT developments’’. Based on the report, Taiwan is behind Singapore, Finland, the United States, Japan, Canada and the United Kingdom, and is ahead of the Netherlands, Austria, France, Korea, and China. In addition, based on the official statistics provided by the Executive Yuan in Taiwan (National Information & Communications Initiative Committee, 2005), the computer coverage rate of households is 73%, the Internet coverage rate of households is 61%, and the broadband network coverage rate of households is 47%. All three rates for schools at each educational level are 100%. The rate of Internet users among teachers and students in schools is 71%. The Taiwanese government has pushed an ‘‘e-Taiwan project’’ to embrace the global e-trend (National Information & Communications Initiative Committee, 2002). With a commitment to eliminate the digital divide, the project is aimed at nurturing a society that is rich in knowledge, culture, entertainment and learning opportunities. It also has the purpose of transforming Taiwan into an optimal high-tech green silicon island. Inquiring into the digital divide in relation to university education may, on one hand, provide valuable information for knowing how well Taiwan fulfills its goals at the higher education level. Once

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inequalities are identified, further actions can be taken. On the other hand, this inquiry provides valuable reference for other countries, especially countries whose infrastructures have been built while other aspects of the digital divide still need to be dealt with. As mentioned before, there are three dimensions of the digital divide – ICT access, ICT usage, and ICT applications (Bridges.org, 2001). Since the access is not a problem in Taiwan, for the purposes of this study, we only focus on computer use and computer knowledge. In Taiwan, all universities have Internet access and computer infrastructure on campus (National Information & Communications Initiative Committee, 2005). In addition, according to the results of the survey conducted in this study, more than 97% of undergraduates have their own e-mail address and about 90% of first-year undergraduates have their own computers. Given that Taiwan is one of the main personal computer manufacturers in the world, where manufacturers promote their products aggressively and students can usually purchase computers at cheaper prices, such a finding is not surprising. Rather than utilizing the material dimension of the digital divide, we focus on the usage and knowledge dimension of the digital divide. Specifically, this study attempts to characterize the digital divide among undergraduates and to identify the consequences that this difference has for student learning. The following research questions are explored: 1. Regarding computer use: What are the undergraduates doing with the computers they use at college? 2. Regarding computer knowledge: How do undergraduates perform in regard to computer knowledge and skills? 3. What are the correlates of computer usage and computer knowledge for undergraduates? 4. To what extent does the digital divide affect undergraduate academic performance?

2. Literature review 2.1. Correlates of the digital divide – computer use and computer knowledge Studies have shown that gender differences exist in computer use (Bimber, 2000; Colley & Comber, 2003a, 2003b; Fan & Li, 2005; Kelkar & Mathan, 2002). Compared to females, males are more interested in computers and are more self-confident in regard to their ability to use them. Makrakis and Sawada (1996) investigated Japanese and Swedish students and concluded that male students tend to have a more positive attitude toward computer use than females. In examining American undergraduates, Finn and Inman (2004) reported that gender differences were apparent in terms of the frequency of using the Internet, computer games, and software, but that the use of word processing was gender-neutral. Johnson (2003) concluded that no difference existed between females and males in terms of using technology and gaining access to technology in Singapore, while women in Malaysia were less comfortable using technology than men. Lee (2003) examined self-reported IT skills for Hong Kong undergraduates and reported that females were less confident in terms of their abilities and possessed lower IT skill levels than their male counterparts. In addition to computer use and computer knowledge, a gender gap was also found to exist for undergraduates in selecting computer science or computer engineering as majors in many countries such as Australia, Canada, and the United States (Randall, Reichgelt, & Price, 2002; Von & Nielsen, 2001). In Taiwan, half of the undergraduates are females. However, only 39% of those studying for a master’s degree and 26% of doctoral students are females (Ministry of Education, 2006). Although females have the same access to the undergraduate level as males, the percentages of females enrolling in computer scienceand engineering-related fields are relatively small. Only about 30% of the students studying in these fields are women. Obviously, females are underrepresented in the computer and engineering science-related disciplines. Similar to Colley’s findings (2003) for British students, Tsai and Lin (2004) reported that male students in Taiwan perceived the Internet as a toy, while female students viewed the Internet as more of a technology or a tool. However, no significant differences were found in terms of the behavioral aspects of using the Internet. Tsai and Lin’s study focuses on gender differences in relation to attitudes towards computers. Whether or not a gender gap exists in regard to computer use and computer knowledge for undergraduates in Taiwan needs to be explored.

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The divide also runs along racial, social and educational lines. In Germany, Korupp and Szydlik (2005) reported that people of Turkish ethnic descent were less likely to use computers or the Internet compared to ethnic Germans. Studies on the US have shown that even after adjusting for income and education, Blacks and Hispanics have lower computer usage, computer access, and computer ownership than Whites (Frehill, Benton-Speyers, & Cannavale, 2004). Van Dijk and Hacker (2003) revealed that significant gaps exist in terms of ethnicity, income and education level in both the Netherlands and the US. Like other countries (NTIA, 1995, 1998, 1999, 2000; Natriello, 2001; OECD, 2001; Servon, 2002), Tseng (2003a, 2003b) indicated when investigating the digital divide issue in Taiwan that people in general who are from minorities, whose parents have less education and lower incomes, and who live in areas with less economic development are disadvantaged groups. Based on these findings, ethnicity and family socioeconomic background, such as the father’s education and the father’s occupation, are included in this study to explore the digital divide further. In Taiwan, public universities differ from private universities in terms of faculty quality, student quality and resource acquisition. Not only do public universities have higher percentages of faculty possessing doctoral degrees and higher score requirements for incoming students, but they also have better research facilities. The extent to which public universities differ from their private counterparts in terms of the digital divide is a question that needs to be asked. Field differences need to be taken into account, too. The humanities and social sciences differ from the natural sciences and engineering in terms of disciplinary training and norms. How different fields differ from each other in terms of the extent of the digital divide deserves further attention. Based on the above framework, the following research hypothesis is posited: Research hypothesis 1: Gender, ethnicity, education of parents, the father’s occupation, university control, and field are correlated with computer use and computer knowledge among undergraduates. 2.2. Correlates of college student academic performance Studies have utilized the grade point average (GPA) (Amenkhienan & Kogan, 2004; Blair & Millea, 2004; DeBerard, Spielmans, & Julka, 2004; Ridgell & Lounsbury, 2004) or a single course grade in a specific course (Ridgell & Lounsbury, 2004) to measure undergraduate student academic performance. In this study, we employ cumulative average grades received by students at the end of the first semester to represent their academic performance. Different findings for gender effects on academic achievement have been reported by scholars. Surtees, Wainwright, and Pharoah (2002) indicate that a gender gap exists in academic attainment for British students in one elite university. In the United States, Hedges and Nowell (1995) reported that girls do better than boys academically, except in the areas of math and science. Riordan (1997) found that gender is not related to academic test scores. Based on these studies, gender is included for further investigation. Race is a prominent predictor of student academic achievement. According to Massey, Charles, Lundy, and Fisch (2003), the college GPA varies across different ethnic groups. Whites and Asians perform best in their college work, followed by Latinos and then Blacks. Whether or not members of the majority ethnic group in Taiwan, the Min-nan, succeed in college with higher scores than students from minority groups, such as Mainlanders, the Hakka, and aborigines, remains an open question. Families also shape student academic development. Learning differs across family socioeconomic status levels. Pascarella and Terenzini (1991) indicate that parental income and parental education affect college outcomes. Students coming from families with better-educated parents who occupy upper managerial or professional positions usually have better academic performance than students who come from families at lower socioeconomic status levels (Alexander, Entwisle, & Olson, 2001). In Taiwan, public universities enjoy more prestigious status than their private counterparts. How students studying in different kinds of universities differ in learning outcomes needs further investigation. The natural sciences and engineering differ from the humanities and social sciences in terms of academic training, research paradigms, and disciplinary norms. Whether student academic achievement varies in different fields when the effects of other correlates are simultaneously taken into account is a question for exploration. Active learning, such as asking teachers questions and discussing academic issues with peers, contributes to a healthy learning culture and should therefore have an impact on student academic achievement (Lamport, 1993). Farmer (2003) suggests that library research skills can help students to learn and succeed in college. Educators

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today worry that many students come to college without good skills for discerning the truth, and quite a few leave without having acquired them. Whether or not library searching skills affect the student’s academic work deserves further inquiry. As mentioned earlier, the main purpose of this paper is to explore the impact of the digital divide— computer use and computer knowledge—on student learning performance. The proportion of time spent using computers related to academic work may be positively correlated with learning performance. It is added to test whether students who spend a greater proportion of their computer time on academic work outperform their peers who spend less of their computer time studying. Students use computers not only for finishing their school assignments, but also for other purposes. Whether the various ways in which computers are used have an impact on student learning deserves further inquiry. Computer knowledge is also added into the models to be tested. We would like to know whether students with better computer knowledge also perform better than their peers with less computer knowledge. The following research hypothesis is submitted: Research hypothesis 2: Controlling for the effects of variables such as gender, ethnicity, education of parents, the father’s occupation, university control, field, active learning habits, and library searching skills, namely, the digital divide—including the proportion of time spent in academic-related computer work, the diverse purposes of computer use, and computer knowledge—significantly influence the academic achievement of undergraduates in Taiwan. 3. Research design 3.1. Subjects In this study, a survey was conducted to answer the research questions. The survey population was defined as full-time first-year undergraduates studying in 12 different disciplines at 12 universities. The 12 universities were: Chinese Culture University, Feng Chia University, Fu Jen Catholic University, Ming Chuan University, National Cheng Kung University, National Chengchi University, National Chung Cheng University, National Sun Yat-sen University, National Taiwan University, National Tsing Hua University, Tamkang University, and Tunghai University. These institutions differ from one another in terms of institutional control (public vs. private) and geographical location (northern Taiwan vs. other areas). The 12 disciplines investigated were: Chinese, foreign languages and literature, chemistry, mathematics, economics, sociology, business management, accounting, finance, electrical engineering, civil engineering and mechanical engineering. The questionnaires collected information regarding students’ demographic background, family socioeconomic status, experience of using computers, computer ownership, computer knowledge, interactions with faculty and peers as well as their academic grades in the first semester. A pilot study was conducted to improve the questionnaire’s design. A total of 3083 students were randomly selected and 2719 of them completed the questionnaires. The response rate was 88.2%. Three reasons mainly contributed to the high response rate. First, cover letters, accompanied by questionnaires, were sent to each student selected. The letter explained the purpose of the study and sincerely invited students to participate in the survey. Such a letter may have led students to feel that they were respected and that their participation was important to the study. Second, incentives were used. For each student who was willing to fill in the questionnaire, he or she received a gift worth one US dollar. The incentives used may have increased the motivation of students to participate in the questionnaire. Third, since the survey was sponsored by the Ministry of Education (MOE), the MOE sent formal proof letters to each department selected. Our questionnaire distributors brought copies of these letters as proof that they were not bad-intentioned persons. Students could check with department secretaries if they had any doubts. 3.2. Variables and statistical methods Two dimensions of the digital divide were utilized—computer use and computer knowledge. Computer use had two indexes. One concerned the diverse purposes behind using computers. It ranged from writing school assignments and WWW surfing to watching movies and shopping, being counted from 0 to 16. As the diverse

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uses of computers increased, the count value increased. Another index concerned the proportion of computer time devoted to academic-related work. It was measured by the number of computer hours spent in academicrelated work within a 1-week period divided by the total number of hours spent using computers within that 1-week period. Although the hours spent using computers were self-reported data, the results of this study were similar to those of Yo (2001) and Ko and Shaw (2005) in investigating the average hours undergraduates spend using computers. Another dimension of digital divide had to do with computer knowledge. Computer knowledge was measured in terms of a test score ranging from 0 to 18. The test comprised three aspects of knowledge, namely, computer hardware, computer software, and the Internet. In regard to the test item for computer hardware, for example, students were asked questions such as: ‘‘My computer screen display always responds more slowly than those of my friends. This could be because: (1) The CPU is slower; (2) There is less computer memory; (3) The graphics card is worse; (4) All of the above, or (7) Do not know’’. The computer software knowledge test contained questions such as: ‘‘In Excel, which button on the toolbar allows you to create a chart? (1) (2) (3) (4) (7) Do not know’’. The Internet knowledge item asked students questions such as: ‘‘During the US’s 9/11 incident, a great many banking institutions’ computers were damaged, but the data was immediately restored to normal. This was because they used which facilities from the Internet? (1) Remote simultaneous synchronized copy; (2) Remote surveillance; (3) Remote control; (4) Remote installation; or (7) Do not know’’. In addition to the test for computer knowledge, the survey also asked students to self-rate the levels of computer knowledge they own (beginner level, medium level, and advanced level) for themselves. The results show that the higher the computer score one get, the higher the level one evaluated for oneself. The results of one-way ANOVA confirms this trend (beginner group = 8.29, medium group = 10.47, advanced group = 12.08, F = 143.40, p < .001). The results at least indicate that the computer test has the reliability. In addition, we selected undergraduates from Departments of the Chinese Literatures and students from Departments of Electronic Engineering. We compared their performance on the computer knowledge test we employed. If the computer test has the validity, it should be able to distinguish students for the two groups. That is, students with electronic engineering background should perform better than students from Chinese literatures. The results indicate that the computer test has the concurrent validity (Chinese literature = 8.30; Electronic engineering = 11.68, t = 9.91, p < .001). The field term classified the disciplines investigated into two categories: (1) humanities and social sciences, and (2) natural sciences and engineering. The former included departments like Chinese, foreign languages and literature, economics, sociology, business management, accounting, and finance. The latter included departments such as chemistry, mathematics, electrical engineering, civil engineering and mechanical engineering. Active learning was composed of 12 items. In relation to these items, students were asked to report the frequency with which they engaged in particular kinds of learning behavior. These included asking teachers questions in class, expressing personal opinions in the classroom, doing oral presentations in class, rewriting assignments until the student’s own standard was satisfied, preparing reading assignments before class, making efforts to satisfy faculty members’ expectations, asking teachers questions outside the classroom, and discussing assignments with classmates after class, etc. Library utilization was concerned with asking students if they had ever entered and utilized the university library facilities to search for articles or books. Academic performance was measured by the self-reported average score across all subjects that a student obtained at the end of the first semester. The original question that students were asked was: ‘‘What was your academic average score last semester? (1) 90 and above, (2) 85–89, (3) 80–84, (4) 75–79, (5) 70–74, (6) 65–69, (7) 60–64, and (8) below 60. It was unusual for students to get scores above 90 or below 60. We then reclassified scores into different categories, namely, low (below 70), medium (70–79), and high (80 and above). 3.3. Statistical techniques Multiple regressions were also employed to test Hypothesis 1. With regard to testing Hypothesis 2, we at first decided to use an ordered logit. However, before doing that, we utilized the Brant test to test whether the parallel regression assumptions had been violated. The results showed that the assumptions of the proportional odds model had been violated, but the main problems seemed to be with the variable for sex. Based

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on this, we decided to adopt the partial proportional odds model, where the parallel lines constraint is only relaxed for the sex variable where it is not justified (Williams, 2006). The generalized ordered logit—partial proportional odds—model we employed can estimate models that are less restrictive than the ordered logit. It is also more parsimonious and interpretable than those estimated using the multinomial logistic regression. 4. Data analysis 4.1. Computer usage What did undergraduates use computers for? According to Table 1, writing school assignments was at the top of the list, followed by sending and receiving e-mails, and surfing the WWW. Computers helped to fulfill the entertainment needs of students. Nearly 70% of the students used computers to play games, as compared to 60% of them who used them for watching movies, and 15% of them for watching TV. Computers were also used as tools for accessing information. More than 70% of the students used computers to search for information that they wanted and about 40% of them used computers as reference tools such as dictionaries and encyclopedias. Computers also helped students to fulfill the needs of social interaction such as making Internet friends and producing cards and writing formal letters. About 15% of students used computers for enhancing their computer skills and posting news. A few students also used computers for financial management, Internet shopping and other activities.

Table 1 Descriptive statistics on the digital divide Variables

n

%

Mean

Variety of purposes Writing school assignments Sending e-mails WWW surfing Posting news Computer use in part-time work Enhancing computer skills Looking for needed information Using reference tools (electronic dictionaries, encyclopedias, etc.) Making Internet friends Making cards and formal letters Playing computer games Movie-watching TV-watching Shopping Financial management Others

2493 2333 2303 441 174 558 2160 1137 523 943 1852 1605 423 169 43 197

91.9 86.0 84.9 16.2 6.4 20.6 79.6 41.9 19.3 34.7 68.2 59.1 15.6 6.2 1.6 7.3

Hours spent using computers 6 h and below 7–13 h 14–20 h 21–27 h 28–34 h 35 h and above

525 618 609 259 296 359

19.7 23.2 22.8 9.7 11.1 13.5

Hours spent using computers for de academic-related work 1 h and below 2–3 h 4–5 h 6–7 h 8–9 h 10 h and above

423 892 651 262 81 398

15.6 33.0 24.0 9.7 3.0 14.6

Variables

n

%

Mean

Percentage of computer hours related to academic work 0–9% 11.8 10–19% 18.0 20–29% 21.9 30–39% 13.1 40–49% 8.3 50–59% 12.1 60–69% 4.9 70% and above 9.9 Computer knowledge 9.42 0 28 1.1 1 20 0.8 2 45 1.7 3 66 2.5 4 101 3.9 5 134 5.2 6 186 7.2 19.13

7 8 9 10 11 12 13

204 231 266 267 262 230 193

7.8 8.9 10.2 10.3 10.1 8.8 7.4

5.11

14 15 16 17 18

159 92 79 27 10

6.1 3.5 3 1 0.4

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What proportion of the hours spent using the computers was academic-related as opposed to the total hours spent on the computers? The percentage is calculated as hours spent using computers related to academic work per week divided by the total number of hours spent using computers per week. On average, students spent about 19 h per week using computers. About 5 of the average computer hours were spent on academic work in 1 week. As the detailed percentages in Table 1 indicate, half of the students spent less than 30% of their total computer hours on academic-related work. More than one-fifth of the students devoted more than 30% but less than 50% of their computer hours to academic-related work. Another 15% of students used more than 60% of their total computer hours on academic-related work. Table 1 also describes the descriptive statistics in relation to computer knowledge. The score distribution for computer knowledge approximated a normal distribution. This means that most people performed at the middle average level, while only a few students performed extremely well or extremely poorly near the poles. On average, students were able to answer half of the 18 questions correctly. About 50% of students answered 8–12 questions correctly. This finding indicates that most students had adequate computer knowledge. 4.2. Correlates of digital divide Table 2 reports the results of the multiple regressions for different dimensions of the digital divide. In order to test whether there was any problem of multicollinearity among the independent variables, additional VIF statistical tests were run. Most VIF values were smaller than 1.4 and some VIF values were greater than 2.0 and less than 2.2, which suggests that multicollinearity was not a problem in the models. As can be seen, only one independent variable reached the significance level in terms of predicting the diverse purposes behind using computers. Students who studied in public universities tended to use computers to fulfill more kinds of needs in their daily lives than their private university counterparts. With regard to the total number of hours spent on computers in 1 week, it was found that students who were male, studied in the fields of the natural sciences and engineering, and went to a public university tended to spend significantly more time using computers than their peers who were female, studied in the fields of the humanities and social sciences, and were registered in private universities. The picture changes, however, when the proportion of computer hours devoted to academic work is used as the dependent variable. Based on these results, students who were female, whose mothers were less educated, and who studied in private universities tended to spend a higher proportion of their computer hours engaged in academic work. Table 2 Results of multiple regressions for the digital divide Variables

Variety Coefficient

Sex (male = 1) Father’s ethnicity (Min-nan = 1) Mother’s ethnicity (Min-nan = 1) Father’s education Mother’s education Father’s occupation (reference = professional) White collar Blue collar Retired, unemployed Field (hum. and soc. = 1) University (public = 1) Constant N F Adjusted R2

.07 .13 .04 .02 .02

.00 .17 .21 .10 .27** 4.97*** 2632 3.12 .01

Note: At .05 < P < .10, *P < .05,

< .01,

**P

***P

Total hours Standard error

Coefficient

Standard error

Academic comp.

Knowledge

Coefficient

Coefficient

Standard error

Standard error

.10 .12 .12 .02 .02

6.01*** .18 .40 .07 .04

.68 .80 .79 .13 .13

.10*** .03 .00 .00 .01*

.02 .02 .02 .00 .00

1.77*** .36* .36* .04 .03

.15 .18 .18 .03 .03

.14 .13 .14 .11 .09 .30

.67 1.11 .86 2.46*** 4.23*** 15.34*** 2644 22.42 .08

.93 .83 .90 .71 .60 1.97

.01 .02 .03 .02 .09*** .56*** 2632 7.21 .02

.03 .02 .03 .02 .02 .06

.35 .41* .40* .78*** 1.57*** 7.20*** 2563 49.81 .16

.21 .19 .20 .16 .14 .44

< .001.

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With regard to computer knowledge, the results show that male students tended to perform better than female students in the computer knowledge test after controlling for the effects of other variables. Students who came from families whose fathers or mothers were in the majority ethnic group, or whose fathers were managers or professionals, displayed better computer knowledge than their peers whose fathers or mothers were from other ethnic groups, or were blue-collar workers or retired. Other things being equal, students studying in the humanities and social sciences obtained significantly lower scores in the computer knowledge test than did students in the natural sciences and engineering. The university control variable was found to have a significant effect on computer knowledge. After controlling for the effects of other variables, the students in public universities performed better in the computer knowledge test than the students in private universities. 4.3. The impact of the digital divide on academic performance Table 3 presents the results of the generalized ordered logit—the partial proportional odds—model for predicting different levels of students’ academic performance. Among the different independent variables, gender was the only variable that did not fit the parallel regression assumption. Gender was also found to be a significant predictor of learning outcome. Since Gamma_2 = coefficients of Eq. (2) coefficients of Eq. (1), the coefficient for gender in Eq. (2) was .76. That is, after controlling for the effects of other variables, females tended to perform better than males. Such gender disparity is most obvious in the low category (Eq. (1): low vs middle, high score groups), but the gap between them becomes smaller as the scores increase (Eq. (2): low, middle vs high score groups). Other family-related variables such as the father’s ethnicity, the mother’s ethnicity, the father’s education, the mother’s education, and the father’s occupation appeared to be non-significant in predicting student academic performance. The results also showed that the odds of being in a higher level of academic achievement category were 1.85 times higher for students studying in the humanities and social sciences than students Table 3 Results of generalized ordered logit–the partial proportional odds model for predicting different levels of students’ academic performance Variable

Coeff.

Beta Sex (male = 1) Father’s ethnicity (Min-nan = 1) Mother’s ethnicity (Min-nan = 1) Father’s years of education Mother’s years of education Father’s occupation (reference = professional) White collar Blue collar Retired, unemployed Field (humanities and social sciences = 1) University control (public = 1) Learn Library Variety Academic computer use Hardware score Internet score Software score Gamma_2 Sex (male = 1) Alpha Constant_1 Constant_2 Note: At .05 < P < .10, *P < .05,

**P

< .01,

***P

< .001.

Std. err

Exp(b)

1.07*** .06 .13 .02 .01

.12 .11 .11 .02 .02

.34 .94 1.14 1.02 1.01

.04 .09 .13 .61*** .79*** .05*** .44*** .02 .30** .02 .03 .11***

.13 .11 .12 .10 .09 .01 .10 .02 .11 .03 .03 .03

1.04 1.09 1.13 1.85 2.20 1.05 1.55 .98 1.35 1.02 .97 1.11

.31*

.12

1.61*** 4.10***

.37 .38

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studying in the natural sciences and engineering, holding other variables constant. The odds of receiving a higher level of academic grades were 2.20 times larger for students who registered in public universities than for their peers entering private universities, after controlling for the effects of other variables. Furthermore, students who demonstrated active learning habits performed better academically. Library research experience was a significant predictor, too. Other things being equal, students who used the library facilities to search for books and articles performed better than students who did not. It should be noted that some dimensions of the digital divide reached their significance levels in terms of predicting student academic performance. After taking into account the effects of other variables, the higher the proportion of time that was spent on academic-related computer activities, the higher the level of the academic grades that were received. However, the various purposes behind using computers failed to predict academic performance, while computer knowledge predicted academic achievement well. Among the different kinds of computer knowledge, the computer software knowledge significantly contributed to the learning outcomes. In general, students with more computer software knowledge performed better academically, ceteris paribus. 5. Discussions and conclusions This study has focused on exploring the correlates of the second and the third dimensions of the digital divide, namely, computer use and computer knowledge, for undergraduates in Taiwan. Furthermore, it has investigated the impact of the digital divide on student academic achievement. By employing different statistical methods such as multiple regression and a generalized ordered logit, answers to the research questions are obtained that are quite complex. Does a digital divide exist among undergraduates in Taiwan? As mentioned in Section 1, in regard to the material dimension of affordability or access as measured by Internet access and the ownership of computer equipment, the problem is not so obvious. Not only are the college campuses in Taiwan equipped with computer facilities, but also 9 out of 10 first-year undergraduates have their own e-mail addresses and their own computers. Regarding the usage and the knowledge dimension of the digital divide, whether or not the gaps exist depends on the measures the researchers employ. We found that students used computers not only for fulfilling their academic requirements or searching for information, but also for entertainment. Being computer users, undergraduates allocated the total amount of their computer time differently from each other, ranging from 6 h or less to 35 h or more in 1 week. Nevertheless, the computer users with many hours of use were not necessarily academic goal-oriented computer users. Some students devoted less than 9% of their computer time to scholastic-related work and spent more than 90% of their time on other computer activities. Some other students mainly concentrated on scholastic computer work and devoted less than 30% of their computer hours to other computer activities. On average, undergraduates in Taiwan spent about 19.13 h on computer work, 5 of which were academic-related. Such a result is similar to the finding of Yo (2001), who investigated 14 colleges and universities and also indicated that undergraduates on average spent about 19.13 h each week on the computer. This amount is somewhat less than in Ko and Shaw’s findings (2005), which reported a higher weekly average of 22.45 h of Internet use, and 6.89 h of academic-related computer work. The knowledge dimension of the digital divide suggests that most undergraduates in Taiwan perform at the middle average level. These findings suggest two things. First, the inquiry into the usage dimension of the digital divide needs to take into account both the quantity as well as the content of computer usage involved. Second, the extent of the gaps among specific groups in terms of their knowledge of and ability to use IT effectively also needs to be investigated in order to better understand the nature of the digital divide. No significant differences in terms of the correlates in both demographic and socioeconomic family background were found in predicting the various purposes behind using computers. However, students enrolled in public universities had wider applications of computers for meeting their different kinds of needs than students studying in private universities. This study also found that the proportion of time devoted to academic-related computer hours and computer knowledge varied significantly among individuals. It should be noted that the direction of some of the correlates was almost totally reversed in predicting the proportion of computer hours devoted to academic-related work and in predicting computer knowledge. Students who were female, whose fathers were from minorities, whose mothers were from minorities, whose fathers were blue-collar workers or

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unemployed or retired, who studied in the fields of the humanities and social sciences, and who had entered private universities were at a disadvantage in terms of their computer skills and knowledge. However, females, or students whose mothers were less educated and who were enrolled in private universities, were focused computer users in terms of allocating time to academic-related work. Such a finding indicates that people who were disadvantaged users under one digital divide index may not have been disadvantaged when another index related to the subject’s social role—the undergraduate student in this study—was employed. Again, it is necessary to note that a single index of the digital divide provides limited information. Both the quantity and the content of computer usage need to be considered. Based on these results, Hypothesis 1 is partially supported by the data. Gender is a crucial variable in predicting the digital divide. The disparity in academic-related computer time usage between males and females in Taiwan may reflect gender differences in the process of socialization, through which boys are encouraged to disassemble machines and play with computers while girls are not. According to Tsai and Lin (2004), who investigated Taiwanese high school students, it was found that males perceived the Internet as more of a toy, whereas females held more pragmatic views concerning the Internet. It is possible that such a gender gap in terms of attitudes towards computers also exists at the higher education level, so that female undergraduates are more academic goal-oriented computer users, while male students spend more time simply enjoying using computers. The different experiences in socializing may lead males to be more self-confident in their use of computers and therefore to have better computer knowledge. Breaking gender stereotypes and encouraging more girls to become more experienced in using computers in the earlier years of their education may help bridge this gap. The government in 2001 started to push its Nine-Year Curriculum, which was designed for all students from the 1st to 12th grades in Taiwan. There has been an emphasis on integrating special topics such as gender equality and education in relation to information literacy into class teaching (Ministry of Education, 2005). As time goes by, such a gender gap in terms of computer use and knowledge is expected to decrease. Ethnicity is found to be a significant predictor in terms of predicting computer knowledge, although it fails to reach significant levels in relation to computer use, from the aspects of both time and purpose. This result is somewhat different from that of Tien and Fu (2005), who reported that students whose fathers were not of Min-nan ethnicity obtained higher average computer scores. The factors that contributed to the differences were probably the different variables included in the model. For instance, in our study we included both the ethnicity of and the education of the two parents, while this earlier study only contained the ethnicity and education of the father. Whether or not people of Min-nan ethnicity, who account for more than 70% of the population, attach higher value to learning to use computers and hence exhibit higher scores in relation to computer knowledge deserves further investigation. A family’s socioeconomic background also has some impact on the proportion of computer hours used in relation to academic work and computer knowledge. In taking into consideration the effects of other variables, undergraduates who had less educated mothers tended to spend a higher proportion of their computer hours on academic-related work. The negative association between the mother’s education and computer use indicates that those belonging to the socially disadvantaged group may have been more goal-oriented computer users in terms of fulfilling their social role, namely, as undergraduates in this case. When predicting computer knowledge, the father’s occupation was found to reach a significant level. After taking into account the effects of other variables, the students whose fathers were professionals, managers, or top government officials tended to possess more computer knowledge than their fellow students whose fathers were blue-collar workers or retired or unemployed people. As can be seen, such results are similar to the traditional measure of the digital divide in terms of measuring access to computers, which suggests that people with lower socioeconomic backgrounds are at a disadvantage. Does the digital divide have an impact on college learning? Based on the results analyzed above, the answer to this question depends on which indexes of the digital divide we are discussing. No effect is found for the diverse purposes behind using computers. Nevertheless, this study also showed that devotion to scholastic computer work and computer knowledge helped students succeed academically. In controlling for the effects of other variables, students who devoted a greater proportion of their computer time to academic work tended to obtain higher academic grades. In addition, computer knowledge was found to have a moderate effect on college student learning, ceteris paribus. Among the different kinds of computer knowledge, it was the software

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knowledge that most of all helped the students to learn. Based on these results, providing more diverse computer software courses may serve as one way of facilitating student learning. According to the research results, Hypothesis 2 is also partially supported by the data. Compared to males, females were found to perform better academically when the effects of other variables were taken into account. Such a finding, on the one hand, is encouraging, since traditionally in Taiwanese society women have been considered to be inferior to men in terms of their social status. Barriers, such as the stereotype gender roles that appear in textbooks, and the educational expectations of parents for their sons being higher than those for their daughters, may place females in a disadvantaged position. However, the results indicate that females obtain higher academic grades than men even in spite of these barriers. The better academic performance of females, nevertheless, raises another question. As mentioned before, although women enjoy equal access to the undergraduate level of education in Taiwan, a smaller percentage of women enroll in master’s and doctoral programs. If the graduate school entrance examination is fair in terms of selecting people with better academic performance, why are women still underrepresented at the master’s level of education? The answers to such a question may be complex but deserve further study. In this paper, students were found to come from a diverse array of social origins. However, after controlling for the effects of other variables, ethnicity and family-related socioeconomic status were not found to influence college student academic performance. Students studying in public universities tended to receive higher grades than students studying in private institutions. Students in the fields of the humanities and social sciences tended to obtain higher grades than students in the fields of natural sciences and engineering. It also appears that students in public universities or in the fields of the humanities and the social sciences either performed better academically or else their grades were inflated. This study indicates that establishing good learning skills and habits is important, too. Students who utilized the library to search for books and articles displayed better learning performance than students who did not. In the study, however, nearly one-fourth (23%) of college freshmen did not utilize such library resources. This is a warning sign to college educators in Taiwan. If students do not know how to access knowledge on their own, i.e. by utilizing library resources, it is difficult to expect them to become life-long learners in a knowledge-based society. Providing training to students and helping them make full use of libraries should be among the top priorities of university educators. Other good learning habits, such as asking faculty questions and discussing academic work with classmates, etc. also affects the learning culture on campuses. In order to promote such a learning culture, faculty members can for instance help students organize student reading and discussion groups to stimulate the exchange of ideas. The results of this study allow us to answer an important question—how do we help undergraduates to learn? According to our findings, several strategies are suggested: (1) Students should be encouraged to spend more time using computers in relation to their academic work during college life; (2) Students should be taught basic library book and article searching skills; (3) Good learning habits should be inculcated in students; and (4) A variety of computer courses should be offered to students for them to learn and choose from, especially those related to the use of computer software. This study has implemented a large-scale survey for undergraduates in Taiwan, and has helped fulfill the research gap by focusing on measuring the digital divide. Not only have the purposes behind computer use and time allocation in relation to computers been examined, but attention has also been paid to computer knowledge. By providing empirical evidence, this study has revealed that the digital divide, one of the forms of social inequality, has influenced college student learning. This finding should surely encourage scholars to think about refining the ways in which the digital divide is measured. At a minimum, for countries like Taiwan in which computer infrastructure is not in any way lacking on college campuses, the material dimensions of the digital divide such as computer ownership and Internet access should no longer be the indicators of the digital divide. If they are, they may lead to a misperception of reality. That said, college educators in Taiwan need to make efforts to help students balance their computer life by offering more computer software courses. Since most of the literature on the digital divide is limited to the material dimensions of possession of or access to computers, this study provides a new perspective on the issue. Nevertheless, this study still has its limitations. For instance, we are interested in the effect of computer knowledge on student academic achievement. We measure student’s knowledge about computers, whereas computer knowledge is not equivalent to the use of computers by students for learning. Students who have abundant knowledge about operating computers may not use computers for the purpose of academic learning.

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Also, it is possible that students who use computers to assist learning but have limited idea how the computers work or the purpose of many of the buttons in software. Another limitation is that it does not expand the definition of the digital divide to explore student competence in using information. As the Internet has become widely accessible to undergraduates, the ability to use information efficiently has emerged as an important problem (Daley, 2003). Researchers can utilize the standards of information literacy competency for higher education suggested by the Association of College and Research Libraries (2000) to examine students’ ability in deciding what information is needed, locating information efficiently, evaluating information critically, utilizing information effectively, and accessing information ethically. Such an effort should add to our knowledge regarding the impact of the digital divide on student learning. 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