COMPUTER ATTITUDES AND COMPENTENCIES AMONG PRIMARY AND SECONDARY SCHOOLS STUDENTS

Abstract

This chapter provides an overview of research from the last 15 years on attitudes and competencies regarding computer use by primary and secondary school students. For many scholars in this field, the low participation of women in computer related training courses and professions, has been the reason to focus mainly on the ‘gender gap’ in computing and on gender differences in computer attitudes especially. However, despite the numerous studies on this subject, consensus about explanations for these gender differences in computer attitudes is still lacking. Three main theories will be discussed: socialisation, gender specific behaviour and the influence of teaching. Compared to computer attitude studies, the availability of studies and theories on students’ computer competencies is much more limited. Students’ own assessment of their abilities in general computer use has received more attention in this field of research, although this subject is also often addressed from a gender perspective.

Keywords: Computer attitudes; Computer competencies; Gender differences; Primary education; Secondary education

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Introduction

Research on students’ use of information technology often results from concerns about the so called ‘digital divide’. The digital divide refers to differences in computer use, attitudes and competencies with regard to gender, age, ethnicity or social economic status (Broos & Roe, 2006). In most western countries the participation of females in IT professional careers and pathways is not only low but is also still falling (Anderson, Lankhear, Timms & Courtney, in press). Therefore, in the last thirty years, the most frequently reported socio-demographic parameter of the digital divide has been gender. One of the main topics in this field is girls’ and boys’ attitudes toward information technology (which is mostly referred to as ‘computer attitudes’). In the United States in particular, differences between female and male students in attitudes toward computers has been the subject of many studies since the 1980s (Volman, 1994). At first, the interest for computer attitudes was mainly directed to being one of the influencing factors explaining gender differences in computer use and in computer competencies. During the last twenty years, computer attitudes have become the main focus of study by itself. Nowadays, the number of studies on the ‘gender gap’ in computer attitudes, exceeds the number of studies on computer competencies and abilities by large (Imhof, Vollmeyer & Beierlein, 2007; Kay, 2006). Studies on computer attitudes, competencies or use among students with different cultural or social economic backgrounds are also still scarce, although the differences between these groups seem to be substantial as well (Volman, Van Eck, Heemskerk & Kuiper, 2005).

This chapter provides an overview of the main research results of studies on (gender) differences in computer attitudes (including computer anxiety) and studies on (gender) differences in computer competencies during the last fifteen years (see for summaries of earlier studies e.g. Volman, 1994). Although the majority of studies are conducted among university and college students or among adults, the main focus in this chapter will be on students in primary and secondary education.

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The first and major part of this chapter is directed to studies on computer attitudes. Despite the large interest of researchers for students’ computer attitudes, the results of these studies are often inconclusive or sometimes contrasting. Therefore, the benefits as well as the limitations of these studies will be discussed in the first section. An overview of the main results and outcomes is provided in section 2. Section 3 focuses on research on student’ computer competencies, including their own perceived competencies. The last section (4) summarizes the main results and addresses possibilities for future research in this field.

Measuring computer attitudes

As stated before, despite the large number of studies on students’ computer attitudes, the results of these studies are often inconclusive or even in contrast to each other. For example, although the majority of studies show less positive computer attitudes of girls compared to that of boys, the difference is often very small or negligible (Meelissen, 2005; Whitley, 1997). Furthermore, some studies show no gender differences in computer attitudes at all or find more positive attitudes among girls compared to those of boys (King, Bond & Blandford, 2002; North & Noyes, 2002; Whitley, 1997). One of the explanations could be related to the measuring of the construct ‘computer attitudes’ in those studies. The research literature on attitudes toward computers shows a large variation in the use of computer attitudes scales (Christensen & Knezek, 2000; Dryburgh, 2000; Durndell, Haag & Laithwaite, 2000). In general, the operationalization of computer attitudes is based on the attitude theory of Fishbein and Azjen (1975), in which three aspects are distinguished. The first aspect is the affective aspect and refers to someone’s feelings about the subject. In terms of computer attitudes, the affective aspect can be measured by statements about liking computers or enjoyment in computer use. The perceived relevance of computers could be an operationalization of the cognitive aspect of attitudes or on other words, someone’s perceptions and opinions about the subject. The last aspect refers to the behaviour toward the subject. In computer attitudes research, this aspect can be measured by

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instruments measuring computer anxiety or self-confidence in computer use.

Often, only one or two of these aspects are measured in computer attitude studies. One of the most used instruments (or versions of this instrument) is the Computer Attitude Scale or CAS (Gressard & Loyd, 1986). In CAS three subscales are distinguished; enjoyment in computer use, computer anxiety and self-confidence in computer use. The first scale refers to the affective aspect; the other two refer to the behavioural aspect. However, computer anxiety could also be regarded as a potentially influencing factor on students’ attitudes (e.g. Durndell & Haag, 2002;, King et al., 2002). The same goes for concepts such as gender stereotyped views on computers, self-efficacy or self-confidence. These concepts are used as sub-scales of computer attitudes or as potentially influencing factors on the dependent variable computer attitude (e.g. Comber, Colley, Hargreaves & Dorn, 1997; Levine & Donita-Schmidt, 1998). The large variation is discussed by Kay (1993):

“One method of developing a common language among attitudes scales is to develop a composite of the various constructs already identified. This process is hindered somewhat by a noticeable absence of theoretical justification provided by researchers in support of their constructs. In a number of studies independent constructs are identified in a post hoc fashion. Without some theoretical substance, it is difficult to pick and choose from the computer attitudes grab bag.” (Kay, 1993, p. 372).

Researchers in this field seem to pay little attention to the effects of their choice for an attitude instrument. In the research of Levine and Donita-Schmidt (1998) and Pope-Davies and Vispoel (1993) it turned out that self-confidence, which is often used as a sub-scale of computer attitudes, fits better as a subscale of computer anxiety and that anxiety and attitudes are two different concepts. Gender differences seem to be much more substantial for computer anxiety (girls are more anxious than boys) than for liking computers and relevance of computers (Charlton, 1999). On the

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other hand, a Scottish study among primary school children shows that gender differences only occurred in enjoyment and relevance of computer use and not in self-confidence (Todman & Dick, 1993). There is not only variation in the operationalisation of computer attitude, but also in the actual measuring. In most cases, computer attitudes or subscales of computer attitudes are measured by presenting students with statements on computers and asking them to which extent they agree with these statements (e.g. Meelissen, 2005; Shashaani, 1994). In the study of Whitley (1996), gender differences only occurred for the negative statements (female students agreeing more with negative statements about computers than male students), while there were no differences regarding the positive statements. Therefore, Whitley (1996) emphasized that negative and positive statements in a computer attitudes scale should be well balanced. Often this is not the case.

Another possible explanation for the lack of agreement in the research literature on computer attitudes has to do with the meaning of the word “computer” in the computer attitude scales. If it is presented just as ‘computer’, the respondent could associate the computer with a variety of attractive or less attractive hardware devices, software and uses. Including a distinction in the different uses of computers in the attitude scale is not that common in the research literature ( Richter, Naumann & Groeben, 2000; Van Eck, 2002). Volman (1994) measured the attitudes of Dutch secondary school students before and after a computer literacy course. Students who finished the course, no longer associated computers with playing computers games, but regarded computers mainly as an instrument for teaching and learning. According to the research of King et al., (2002), students in lower secondary education regarded computers above all as an instrument for playing games. Their attitudes changed during secondary school, as computers were used more serious for educational purposes and the use became more vocationally oriented. Differences in associations with the word ‘computer’ could lead to differences in attitudes. In other words, the interpretation of the results of many studies is hindered by the fact that the researcher does not really

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know what each respondent is thinking of when he or she gives an opinion about ‘the computer’. The lack of this distinction could be a threat to the validity of the computer attitudes scale used.

Finally, in terms of generalization of the results for the research population, the eloquence of the outcomes of studies on computer attitudes is often not that strong (Dryburgh, 2000; Meelissen, 2005). Although a case study approach with in-depth interviews or lessons observations seem to be less common in this research field than a survey approach, survey instruments are frequently administered among convenience samples of students from a few classes or schools. But also in the case of larger, random samples, the possibility to generalize to the research population is often not addressed in the research design. Very few studies recognise the limitations of their sample and incorporate these limitations in the interpretation of their results (e.g. North & Noyes, 2002).

Taken into account these limitations of computer attitude studies, the main theories and research results on (gender) differences in computer attitudes are described in the next section. The section gives an overview of the main and recent publications on students’ computer attitudes in primary and secondary education. As stated before, computer attitudes are often analysed from the perspective of gender differences in computing. Therefore, a substantial part of the studies described in this article are carried out with this perspective in mind. Often, these studies do provide insight in the factors influencing computer attitudes in general as well.In the next section, the research literature is summarised on the basis of three main topics. These are: the socialisation processes, gender-specific behaviour; and the influence of schools and teachers.

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Students’ computer attitudes

The socialisation theory

Perceived ‘masculinity’ of computers

The rationale for research on ‘the digital divide’ between males and females is often based on females’ low participation rates in computer science courses and IT-professional careers (Anderson, et al., in press). IT-professions and computers are assumed to be unattractive for females because of their ‘male image’. It is argued that this male image has a negative effect on the attitudes of girls as a result of gender differentiated socialization (e.g. Brosnan & Lee, 1998;, Charlton, 1999). Children are taught by their environment (parents, other family members, peers, and in the media) what “correct” behaviour is for boys and what ‘correct’ behaviour is for girls. Knowing a lot about computing and liking computers has a different meaning for girls and boys. For example, boys with high computer interest and skills have historically received more appreciation from their environment than girls with the same skills and interest.Some researchers have found evidence that it is not the physical attributes traditionally associated with gender (being male or female) that determine student computer attitudes. To a large extent their attitudes are determined by the gender with which the child identifies him- or herself (e.g. Brosnan & Lee, 1998; Charlton, 1999; Todman & Day, 2006). In other words, not the sex of a child but the ‘psychological gender’ is regarded as the main factor determining students’ computer attitudes. Because computers are often seen a male domain, children who identify themselves as more masculine may have more positive attitudes towards computers, as opposed to children who identify themselves as more femine.

In the 1960s, females were just as involved in the area of early computer science as males. Females played an important part in the introduction of the binary system and in the development of the computer language Cobol

(Corston & Colman, 1996). Female computer programmers were even preferred to male programmers because of their supposed ‘accuracy’ and

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‘patience’. This changed completely during the 1970s and 1980s in most of the industrialised western countries. Computers and computer science became associated with more typically “male” areas, like mathematics, science and technology. For example in the Netherlands, the introduction of computers in secondary schools was often initiated by mathematics and science teachers. Those teachers also became responsible for the computer science lessons (Beentjes, Vooijs, & Kruse et al., 1995). A study carried out among secondary school females in the US, showed that negative attitudes towards mathematics are strongly related to negative attitudes towards computers and girls are less positive about mathematics than boys (Shashaani, 1995). Not only the association of computers with mathematics and technology was responsible for the male image of computers, but the use of the first personal computers at home played a role as well. In the beginning of the 1980s almost no (user friendly) software was available, which meant that computers were predominantly used for programming and playing games (Teague & Clarke, 1995). Males spent considerably more time than females with these computers. According to Teague and Clarke, males are more interested in experimenting with new techniques, even when the practical use of that technique is not clear. For females, the direct practical use is much more important than it is for males. Furthermore, most of the computer games that were available, were more directed to the interests of boys than to the interest of girls, which emphasised the male image of computers (Charlton, 1999).

However, Charlton (1999) also argues that the male image of computers is no longer a valid explanation for gender differences in computer attitudes. Nowadays, computers are widely available, have become more user-friendly, and computer use has become much more varied. In 2003, in most western and also some non-western countries both girls and boys spend more than one hour per day playing computer games and more than half an hour per day using the Internet (Mullis, Martin, Gonzalez & Chrostowski, 2004). The variety of available computer games and intensity of gaming has increased enormously. Among English primary and lower secondary school

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students, playing computer games turns out to be the favourite out-of-school activity for both girls and boys (Mumtaz, 2001;, Colley, 2003). In the Netherlands, primary school boys (grade 5) reported more intensive computer use outside school hours than girls did. However, although boys spent on average five hours per week, girls still spent on average three hours per week with the computer (Meelissen, 2005). Both girls and boys spent most of that time gaming.

Empirical evidence on how the image of computers is perceived by girls and boys is not widely available. In 1994, Shashaani collected data about gender stereotyped views on computing among almost 1800 American secondary school students. It turned out that only boys regarded computers as a typically male domain, while girls did not. In 1997, an English study among primary and secondary school students showed that on average, girls explicitly disagreed with gender stereotyped statements about computers and that most of the boys were neutral toward this subject (Comber et al., 1997). However, the majority of Dutch boys in grade 5 (in 1999) were convinced that boys knew more about computers than girls did (Meelissen, 2005). Furthermore, about a third of the girls agreed with them. Girls with less gender-stereotyped views on computers were expected to have more positive computer attitudes. However, gender stereotyped views on computers turned out to be unrelated to either girls’ or boys’ computer attitudes (Meelissen, 2005).Another method for finding out if computers are (still) regarded as typically masculine, is applied in a study by Mercier, Barron and& O’Conner (2006). With surveys, drawings and interviews, sixth- and eight-grade students were asked to generate representations of computer users in pictures or in words. Both girls and boys were more likely to draw a male user. Frequently, the drawing showed a boy wearing glasses and sometime the word ‘nerd’ was written under the drawing. When asked if these students saw themselves as such a ‘computer-person’, around 80% of the students disagreed. There were however, no significant gender differences.

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It is also not yet clear if the male image of computers can be regarded as a worldwide phenomenon or just as a characteristic of western industrialised societies. In the beginning of the 1990s, an international study, called COMPED (COMputers in EDucation) included questions about computer attitudes in the student questionnaires (Pelgrum, Janssen Reinen & Plomp, 1993). COMPED showed that in most western countries, boys in primary and secondary education were more positive about the relevance of computers compared to girls. However, Japanese girls in upper secondary school regarded computers as more relevant to themselves than to their male classmates. With regard to enjoyment in computers these gender differences were even more substantial (girls were less positive). In Japan and India these differences were less convincing compared to the differences in the western countries. The absence or the smaller differences in attitudes between girls and boys in Japan and India could indicate the male image of computers is not a world wide phenomenon. A study supporting this assumption, is a small scale survey among primary and secondary school students in South Africa. It showed no gender differences in computer attitudes, neither did students perceive computers as a typically male domain (Bovee, Voogt & Meelissen, 2007). According to the majority of both male and female students, females have the same abilities in computer use, in learning computer science and in pursuing a computer related career.

The influence of the social environment

According to the socialisation theory, parents, other family members, friends and also teachers are expected to have a substantial influence on students’ computer attitudes (e.g. Shashaani, 1993). A Dutch study from 1995 analysed the relation between computer use of parents and that of their primary school children (Beentjes et al., 1995). Fathers and brothers spend more time with the computer at home than mothers and sisters. In general, the amount of time that parents spend together with their children at home with the computer, was positively related with out of school computer use of those children. For girls, the frequency of the use by their mother turned out to be more important than that of their father. More

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recently, a Dutch study of the computer attitudes of primary school children showed no relation at all between the intensity of computer use at home by the parents and the computer attitudes of their sons or daughters (Meelissen, 2005). However, the support of parents with regard to the computer use of their children, turned out to be very important for children’s attitudes. Although boys experienced more encouragement from their parents to use computers than girls did, the effect of encouragement was stronger for girls’ computer attitudes than for boys’ computer attitudes. In an earlier study of Shashaani (1994), a similar relation was found; the computer attitudes of girls in secondary school were less positive if their parents were convinced that ‘knowing a lot about computers’ was less important for girls.

In the same study of Shashaani (1994), the relation between Socio-Economic Status (SES) (educational level and profession of the parents) of the student and computer attitudes was also analysed. This study showed a very modest positive influence of SES on computer attitudes of students (Shashaani, 1994). However, these results were not found in a more recent Flemish research among upper secondary school students. In this study, no relation between SES and computer attitudes was reported (Braak & Kavadias, 2003).

The socialisation theory assumes that older students are more exposed to the influence of the social environment than younger students, and therefore gender differences in computer attitudes increase with age (Shashaani, 1993). This could explain why the low interest of girls in computers is mostly visible in the under representation of girls in computer science courses and IT related professions. Several studies have found that gender differences in computer attitudes and computer anxiety increases with age (e.g. Chua, Chen & Wong, 1999;, Comber et al., 1997). Analyses on data from about 10,000 Grade 3-12 students in the US, revealed that in grade 4 and grade 5 girls enjoyed computers even more than boys did, but in the higher grades of primary school, these differences were the other way around (Christensen, Knezek & Overall, 2005). There are also studies

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showing a less linear correlation between attitude and age. A Scottish study showed that in general, students’ attitudes towards computers became less positive during primary schools years (Todman & Dick, 1993). Girls enjoyed computers less than boys, however, the extent of these gender differences stayed the same throughout primary school. A small scale study among 5- to 13-olds students in the USA, revealed a remarkable relation between age, gender and gaming (Cherney & London, 2006). Boys’ out of school activities, such as gaming, became a little more “masculine” with age, but girls’ leisure activities became less “feminine”. With age, girls became more interested in playing computer games that could be regarded as masculine, such as action adventures, human and fantasy violence or sports.Compared to younger girls, older secondary school girls in Australia indicated that computers were more interesting and more relevant, because the computer use at school has become more serious and useful in the higher grades (King et al., 2002). As stated before, males seem to be more interested in experimenting with new techniques, even when the practical use of that technique is not clear (Teague & Clarke, 1995; Christensen, Knezek & Overall, 2005). For females, the direct practical use is much more important than it is for males. For secondary school students, this difference between males and females seems to become more apparent during their school career.

Gender specific behaviour

Studies on computer attitudes from the 1980s and 1990s are often reporting about the importance of computer access for the explanation of (gender) differences in attitudes. The assumption was that more access would lead to more experience and more experience would lead to more positive attitudes (e.g. Comber et al., 1997). However, there are also indications that persons with a negative reaction to computers will feel even worse when they are reinforced with repeated exposures to them (Garland & Noyes, 2005).In many western countries, access to computers seems to be is no longer a relevant issue. According to the latest Trends in International Mathematics

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and Science Study (TIMSS-2003), more than 75% of the primary school students from 25 nations has access to a computer at home (Mullis et al., 2004). Therefore, the discussion about the relation between computer access, computer experience and computer attitudes, seem to have been shifted from computer access and experience to the ‘quality’ of computer experience (e.g. McIlroy, Bunting, Tierney & Gordon, 2001). Girls and boys experience computer use differently because of gender specific behaviour (e.g. Bamossy & Janssen, 1993; Volman, 1997). Especially the differences in the way girls and boys interpret their own performance in computer use could be an important factor explaining the lower interest of girls for computers. Some observational studies show that girls and boys attribute success and failure in computer use very differently. Girls blame themselves for failure and attribute success to external causes such as “luck”, or “easy task”. For boys, it is the other way around. They blame the situation in case of mistakes and ‘boast’ about their successes. Boys show the teacher what they can do with computers while girls show the teacher what they cannot do with the computer (Bamossy & Janssen, 1993). In her study among Dutch lower secondary students, Volman (1994) called this behaviour of boys in the classroom during computer literacy lessons “the expert repertoire”. Girls showed the “outsider repertoire”, which not only included reservations about their own computer competencies, but also avoiding any signs of computer expertise. In research about gender differences in mathematics achievement the same line of reasoning is applied. The “learned helplessness paradigm” states that gender differences in mathematics achievement and in affective factors can be explained by psychological differences in attribution styles between boys and girls (Boaler, 1997). Compared to boys, girls are much more likely to blame themselves for failure in mathematics and as a consequence, show less confidence in mathematical tasks and enjoy mathematics less. Boaler (1997) argues against this paradigm, because girls themselves are seen as the main factor causing the inequity. She also refers to it as “blame the victim paradigm”. Although the differences in success attributions between girls and boys with regard to computers seem to be empirically supported, this paradigm has one important weak point. It is not clear if and why this

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behaviour is specific for ‘male’ subjects like mathematics and computers. The international comparative study of reading literacy (PIRLS) for example, showed that in most countries girls are more confident than boys in their reading abilities (Mullis, Martin, Gonzalez & Kennedy, 2003). Obviously, this is influenced by girls’ higher achievement in this subject compared to that of boys. It is however unclear, if and to what extent the described gender differences in success attributions are also playing in a role with regard to self-confidence in reading.

The influence of schools and teachers

In general, it is assumed that computer use for educational purposes is not only important for (improving) teaching, but teaching is important for students’ attitudes toward computers as well. Several authors suggest that the teaching approach could play an important role in reducing gender differences in computer attitudes (Brosnan & Lee, 1998;, Comber et al., 1997; McILroy et al., 2001,; Mumtaz, 2001). Volman (1994) showed in her study the importance of ‘gender inclusive’ teaching methods. In her research she found that the computer literacy lessons she observed in Dutch secondary schools, were nowhere near gender inconclusive education. Teachers treated girls differently from boys. When girls needed help, teachers were much more inclined to help them by demonstrating how it should be done than they were inclined to help boys in that way. Boys were more stimulated than girls to find it out for themselves. Boys were also asked more questions by the teachers and teachers had higher expectations of the computer knowledge and skills of boys.

It is assumed that the students’ computer attitudes are partly determined by the attitudes, confidence and abilities of the teacher to integrate technology in a meaningful way in his or her lessons (McIlroy et al., 2001;, Torkzadeh & Dijke, 2002). For example, primary school students in England, found computer use in school not attractive because of the limited use of computers in the classroom; computers were mostly used for traditional drill and practices programmes or word processing. The use of the computer(s) at home turned out to be much more advanced (Mumtaz,

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2001). Some authors argue that especially female teachers are important for the computer attitudes of girls, because a female teacher could be regarded as a role model for girls (Brosnan & Lee, 1998; Janssen Reinen & Plomp, 1993). A Dutch study on gender differences in attitudes in primary education (grade 5) showed that the computer experiences (number of years using computers) of the teacher had a small positive effect on girls’ computer attitudes, but only if the teacher was female (Meelissen, 2005). However, although female teachers assessed their own computer skills considerable less positively than male teachers did, their perceived computer competencies turned out to be unrelated to the computer attitudes of girls. Therefore, it was not confirmed in this study that the gender of the teacher is really important for the computer attitudes of girls and boys.

Students’ computer competencies

The limited interest in the research field for students’ computer competencies also indicated with the terms computer performance, computer ability or computer achievement, is in big contrast to the large attention of researchers in computer attitudes (Imhof et al., 2007; Kay, 2006; Tsai & Tsai, 2003). Furthermore, for measuring competencies an even wider range of measures is used, often specially directed to the target group. This makes it very difficult to compare the results of these studies and draw general conclusions from them. Studies in which computer competencies are measured, are mostly targeted at university or college students or other groups of (young) adults and not at primary or secondary students (e.g. Garland & Noyes, 2005; Imhof, et al., 2007; Kay, 2006). In this section, a few examples are discussed of research in which student competencies in general computer skills and Internet skills were studied. Furthermore, this section will provide some theories about students’ own assessment or expectations of their success in performing computer related tasks. This assessment perceived by the students is often indicated with the term ‘self-efficacy’.

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Competencies

One example of a study measuring general computer knowledge and skills of primary and secondary school students is the international comparative COMPED-study (Pelgrum, et al., 1993). In 1992, these students were tested in the domain of ‘functional computer knowledge’. In all ten participating countries (including Japan and Indonesia), boys outperformed girls in this test. An updated version of this test was used in a Dutch large-scale study in primary (grade 5) and lower secondary school (grade 8), called the ICT-monitor (Ten Brummelhuis, 1998). Unfortunately, possible gender differences in the tests scores were not reported, but this study indicated that most of the computer skills were learned outside school and not in the classroom. It turned out that the differences between primary (grade 5) and lower secondary students (grade 8) in computer competencies was rather small, although the tested students in secondary education just finished a compulsory computer literacy course at school. The achievement test addressed most subjects of the course.

A more recent study among Taiwanese grade eight students looked at the relation between students’ learning strategies and computer achievement (Tsai & Tsai, 2003). It was concluded that the so called higher-order learning strategies (as well as metacognition, or ‘thinking about thinking’) resulted in higher computer achievement. Especially the role of study aids (for example: “I compare class notes with other students to make sure my notes are correct”), turned out to have a positive influence, not only on computer achievement, but also on computer attitudes and confidence in computer use. Students who are aware of and are able to use helpful learning resources (other students or on-line resources) seem to become more successful and confident computer users, compared to students with limited use of such study aids.

Self-efficacy

With regard to computer competencies, the main interest seems to be directed to ‘self-efficacy’: student’s own assessment or expectations of their

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success in performing a computer related task. This concept was introduced halfway through the 1970s. According to Bandura (1986) self-efficacy refers to “people’s judgements of their capabilities to organize and execute courses of action required to attain designated types of performances” (p. 391). Sometimes self-efficacy is regarded as a subscale of computer attitudes (Kay, 2006). Just as the concept ‘computer attitudes’, the concept self-efficacy can be operationalized in many different ways. Often it is a list of questions referring to the respondents’ confidence in carrying out general computer tasks (e.g. Durndell, Haag & Laithwaite, 2000; Torkzadeh, Chang & Demirhan, 2006). Another example is asking students if they are able to do a whole range of computer activities without help from others (e.g. Meelissen, 2005). Other self-efficacy instruments are task specific measures for a specific target group (Torkzadeh et al., 2006). Just like studies on computer attitudes, self-efficacy is often studied from a gender perspective. Girls seem to show a lower self-efficacy compared to boys, especially in the more complex computer tasks (e.g. Meelissen, 2005). In a Dutch study among grade 5 students, the majority of both girls and boys indicated that they were able to perform the most common computer activities such as copying text and saving documents, word processing, or using a drawing program (Meelissen, 2005). Boys showed more self-efficacy than girls in tasks which were (in 1999) less common and more complicated such as sending an attachment with an email, forwarding an email and downloading programmes or documents from the Internet. Furthermore, it turned out that self-efficacy had a small effect on computer attitudes in general, but the effect on the attitudes of girls was more substantial than the effect on the attitudes of boys.

Another Dutch study measured the skills of information technology of primary and secondary school students from different ethnic backgrounds (Volman et al., 2005). Although the instruments used were not intended as an operationalization of self-efficacy, the study did not measure student’s actual computer skills, but reported student’s own assessment by asking them how good they consider themselves in the presented general

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computer tasks. The gender differences in this study turned out to be very small. Compared to boys, girls in primary schools considered themselves less good in surfing and downloading files from the Internet. Girls from secondary schools considered themselves also less equipped with skills for downloading from the Internet and they were less handy with burning CDs. All other computer tasks showed no significant differences. The differences between ethnic-minority students and majority students were much more extensive than the gender differences. For example, ethnic-minority students reported fewer skills in word processing, working with illustrations and using the Internet.

Summary and prospects for future research

Due to a lack of agreed-upon instruments for measuring students’ attitudes, self-efficacy and computer competencies and the shortage of studies using representative, random samples, the results in this research field are often inconclusive or based on limited empirical evidence. One of the most addressed topics in computer research is gender differences. Although in most studies girls show less positive attitudes, lower self-efficacy, and less frequent use of computers, the statement that there is a gender gap in computing and girls are behind boys, is less straightforward than it seems to be. In general, gender differences in computer attitudes are very small, and often girls do not show negative attitudes toward computer, but are (a little) less positive. With regard to self-efficacy, boys tend to overestimate and girls tend to underestimate their own abilities (Comber et. al., 1997). More importantly, gender differences in attitudes, out of school computer use, and self efficacy in computers can only be described as ‘being behind’ if use and attitudes of boys are regarded as ‘the norm’. There is an increasing gender gap with regard to the participation of women in IT-courses and professions, but to which extent this is related to or caused by the reported differences between primary and secondary school girls and boys, is not clear. Other factors like the attractiveness of IT-professions or the unavailability of female role models in the IT-field could be just as or even more important. The question remains if the supposedly lower interest of girls in primary and secondary education for computers, is really such a

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major problem that it justifies the ongoing large interest of researchers for this subject. The study of Volman et al., (2005) showed that research among students from different cultural and socio- economic backgrounds seemed to be just as or even more relevant from a ‘digital divide’ point of view.

To strengthen the foundation of theories like the socialisation theory, the ‘learned helplessness paradigm’ or the assumed influence of school and instructional characteristics on computer attitudes, it would be useful to measure computer attitudes and competencies of students in large-scale international comparative studies, such as the COMPED-study. These studies have several advantages over small scale, often explorative, studies that are now most common in this research field. First, it provides the possibility to develop (internationally) reliable test instruments to measure student achievement (amongst students from different age groups) in basic knowledge and skills in information and communication technology, as well as theoretical based and empirically valid attitude scales. In those scales, distinctions should be made in the different uses and purposes of computers and the positive and negative statements should be well balanced. Secondly, the COMPED-study collected data on three levels: school, class and student. This makes it possible to analyse non-school related and school-related factors in conjunction with students’ computer literacy. Finally, the international comparison provides the opportunity to find out if (gender) differences in computer attitudes and competencies are a characteristic of the western culture, or more a worldwide phenomenon.

The design envisioned is close to that of TIMSS (Trends in Mathematics and Science Study, Mullis et al., 2004) and PIRLS (Progress in International Reading Literacy Study, Mullis et al., 2003). Especially the ability to analyse trends turned out be very important for the success and participation of countries in these studies. Therefore, it would be an important addition for this research field to include students’ computer literacy achievement tests and computer attitudes instruments in future COMPED-studies, and repeat those studies regularly, to analyse and compare developments in all parts over the world. Furthermore, with this

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design it is possible to find more conclusive evidence of the presumed influence of the social environment (including schools and teachers) on the interest of girls in pursuing an educational and professional career in information and communication technology.

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Literature

Anderson, N., Lankhear, C., Timms, C., & Courtney, L. (in press). Because it’s boring, irrelevant and I don’t like computers’: Why high school girls avoid professionally-oriented ICT subjects. Computers & Education. 

Bamossy, G. J., & Jansen, P. G. W. (1993). Children apprehension and comprehension: Gender influences on computer literacy and attitude structures towards personal computers. Discussion paper. Amsterdam: Tinbergen Instituut.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory: Englewood Cliffs,: N.J:. Prentice-Hall.

Beentjes, J. W. J., Vooijs, M. W., & Kruse, C. L. (1995). Computerattitude en computergebruik in de vrije tijd: Verschillen tussen jongens en meisjes [Out of school computer attitudes and computer use: Ddifferences between girls and boys]. Nederlands tijdschrift voor opvoeding, vorming en onderwijs, 11(1), 31-46.

Boaler, J. (1997). Reclaiming school mathematics: Tthe girls fight back. Gender and Education., 9(3), 285-306.

Bovee, C., Voogt, J., & Meelissen, M. (2007). Computer attitudes of primary and secondary students in South Africa. Computers in Human Behavior, 23(4), 1762-1776.

Braak, J. v., & Kavadias, D. (2003, September 059-5-03). ‘Jongens achter het scherm'. De invloed van socio-demografische determinanten van computergebruik bij Brusselse scholieren [Boys behindg the screen. The influence of social-demographic determinants of computer use among Brussels’ students]. Paper presented at the Onderwijsresearchdagen (ORD), Kerkrade.

Broos, A., & Roe, K. (2006). The digital divide in the Playstation generation: Self-efficacy, locus of control and ICT adoption among adolescents. Poetics, 34(4/5), 306-317.

Brosnan, M. J., & Lee, W. (1998). A cross-cultural comparison of gender differences in computer attitudes and anxieties: Tthe United Kingdom and Hong Kong. Computers in Human Behavior, 14(4), 559-577.

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Charlton, J. P. (1999). Biological sex, sex-role identity, and the spectrum of computer orientations: A re-appraisal at the end of the 90s. Journal Educational Computing Rresearch, 21(4), 393-412.

Cherney, I., & London, K. (2006). Gender-linked differences in the toys, television shows, computer games, and outdoor activities of 5- to 13-year-old children. Sex Roles, 54(9/10), 717-726.

Christensen, R., Knezek, G., & Overall, T. (2005). Transition points for the gender gap in computer enjoyment. Journal of Research on Technology in Education, 38(1), 23-32.

Christensen, R., & Knezek, G. (2000). Internal consistency reliabilities for 14 computer attitude scales. Journal of Technology and Teacher Education, 8(4), 327-336.

Christensen, R., Knezek, G., & Overall, T. (2005). Transition points for the gender gap in computer enjoyment. Journal of Research on Technology in Education, 38(1), 23-32.

Chua, S. L., Chen, D. T., and & Wong, A. F. L. (1999). Computer anxiety and its correlates: Aa meta-analysis. Computers in Human Behavior, 15(5), 609-624.

Colley, A. (2003). Gender differences in adolescents' perceptions of the best and worst aspects of computing at school. Computers in Human Behavior, 19(6), 673-682.

Comber, C., Colley, A. M., Hargreaves, D. J., & Dorn, L. (1997). The effects of age, gender and computer experience upon computer attitudes. Journal of Educational Computing Research, 39(2), 123-133.

Corston, R., & Colman, A. M. (1996). Gender and social facilitation effects on computer competence and attitudes towards computers. Journal Educational Computing Research, 15(1), 67-91.

Dryburgh, H. (2000). Underrepresentation of girls and women in computer science: Classification of 1990s research. Journal of Educational Computing Research, 23(2), 181-202.

Durndell, A., & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in Human Behavior, 18(2), 521-536.

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Durndell, A., Haag, Z., & Laithwaite, H. (2000). Computer self efficacy and gender: A cross cultural study of Scotland and Romenia. Personality and Individual Differences, 28(1), 1-9.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, invention and behavior: An introduction to theory and research. San Francisco, CAalifornia: Addison-Wesley publishing company.

Garland, K., & Noyes, J. (2005). Attitudes and confidence towards computers and books as learning tools: A cross-sectional study of student cohorts. British Journal of Educational Technology, 36(1), 85-91.

Gressard, C. P., & Loyd, B. H. (1986). Validation studies of a new computer attitude scale. Journal of the Association for Educational Data Systems, 18, 295-301.

Imhof, M., Vollmeyer, R., & Beierlein, C. (2007). Computer use and the gender gap: The issue of access, use, motivation, and performance. Computers in Human Behavior, 23(6), 2823-2837.

Janssen Reinen, I. A. M., & Plomp, T. (1993). Some gender issues in educational computer use: Results of an international comparative survey. Computers and Education, 20(4), 353-365.

Kay, R. H. (1993). An exploration of theoretical and practical foundations for assessing attitudes towards computers: Tthe Computer Attitude Measure (CAM). Computers in Human Behavior, 9(4), 371-386.

Kay, R. (2006). Addressing gender differences in computer ability, attitudes and use. Journal of Educational Computing Research, 34(2), 187-211.

King, J., Bond, T., & Blandford, S. (2002). An investigation of computer anxiety by gender and grade. Computers in Human Behavior, 18(1), 69-84.

Levine, T., & Donitsa-Schmidt, S. (1998). Computer use, confidence, attitudes, and knowledge: Aa causal analysis. Computers in Human Behavior, 14(1), 125-146.

McIlroy, D., Bunting, B., Tierney, K., & Gordon, M. (2001). The relation of gender and background experience to self reporting computer anxieties and cognitions. Computers in Human Behavior, 17(1), 21-33.

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Meelissen, M. R. M. (2005). ICT: meer voor Wim dan voor Jet? De rol van basisonderwijs in het aantrekkelijk maken van informatie- en communicatietechnologie voor jongens en meisjes [ICT: Mmore for Mickey than for Minnie? The role of primary education in making information and communication technology more attractive for girls and boys]. Doctoral dissertation., Enschede, the Netherlands: University of Twente.

Mercier, E. M., Barron, B., & O'Connor, K. M. (2006). Images of self and others as computer users. Journal of Computer Assisted Learning, 22(5), 335-348.

Mullis, I. V. S., Martin, M. O., Gonzalez, E. J., & Chrostowski, S. J. (2004). TIMSS 2003 international mathematics report. Findings from IEA's trends in international mathematics and science study at the fourth and eight grades. Boston: TIMSS & PIRLS International Study Center, Boston College.

Mullis, I. V. S., Martin, M. O., Gonzalez, E. J., & Kennedy, A. M. (2003). PIRLS 2001 international report. IEA's study of reading literacy achievement in primary schools in 35 countries. Boston: TIMSS & PIRLS International Study Center, Boston College.

Mumtaz, S. (2001). Children's enjoyment and perception of computer use in the home and the school. Computers & Education, 36(4), 347-362.

North, A. S., & Noyes, J. M. (2002). Gender influences on children's computer attitudes and cognitions. Computers in Human Behavior, 18(2), 135-150.

Pelgrum, W. J., Janssen Reinen, I. A. M., & Plomp, T. (1993). Schools, teachers, students and computers: Aa cross-national perspective. Den Haag, The Netherlands: IEA.

Pope-Davis, D. B., & Vispoel, W. P. (1993). How instruction influences attitudes of college men and woman towards computers. Computers in Human Behavior, 9(1), 83-93.

Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16(5), 473-491.

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Shashaani, L. (1995). Gender differences in mathematics experience and attitude and their relation to computer attitude. Educational Technology, 35(3), 32-38.

Shashaani, L. (1994). Socio-economic status, parent's sex role types, and the gender gap in computing. Journal of Research on Computing in Education, 26(4), 433-452.

Shashaani, L. (1993). Gender-based differences in attitudes toward computers. Computers & Education, 20(2), 169-181.

Shashaani, L. (1994). Socio-economic status, parent's sex role types, and the gender gap in computing. Journal of Research on Computing in Education, 26(4), 433-452.

Shashaani, L. (1995). Gender differences in mathematics experience and attitude and their relation to computer attitude. Educational Technology, 35(3), 32-38.

Teague, J., & Clarke, V. (1995). The failure of the educational system to attract girls to formal informatics studies. In D. Watson & D. Tinslet (Eds.), Integrating information technology into education (pp. 229-237). London: Chapman & Hall.

Ten Brummelhuis, A. C. A. t. (1998). ICT-Monitor 1997-1998, Basisonderwijs [ICT-Monitor 1997-1998. Primary Education]. Enschede: OCTO, Universiteit Twente.

Todman, J., & Day, K. (2006). Computer anxiety: The role of psychological gender. Computers in Human Behavior, 22(5), 856-869.

Todman, J., & Dick, G. (1993). Primary children and teachers' attitude to computers. Computers and Education, 20(2), 199-203.

Torkzadeh, G., Chang, J. C.-J., & Demirhan, D. (2006). A contingency model of computer and Iinternet self-efficacy. Information & Management, 43(4), 541-550.

Torkzadeh, G., & Dijke, T. P. van. (2002). Effects of training on Iinternet self-efficacy and computer user attitudes. Computers in Human Behavior, 18(5), 479-494.

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Tsai, C., & Tsai, M. (2003). Student computer achievement, attitude, and anxiety: The role of learning strategies. Journal of Educational Computing Research, 28(1), 47-61.

Van Eck, E. (2002). ICT en diversiteit: ICT-gebruik door leerlingen en docenten in het BO en VO [ ICT and diversity: ICT use by students and teachers in primary and secondary education]. (Vol. SCO-rapport; 664). Amsterdam: SCO-Kohnstamm Instituut.

Volman, M., Van Eck, E., Heemskerk, I., & Kuiper, E. (2005). New technologies, new differences. Gender and ethnic differences in pupils' use of ICT in primary and secondary education. Computers & Education, 45(1), 35-55.

Volman, M. (1997). Gender-related effects of computer and information literacy education. Journal of Curriculum Studies, 29(3), 315-328.

Volman, M. (1994). Computerfreak of computervrees. Sekseverschillen en egalitair informatiekunde-onderwijs [Computer freak or computer fright. Gender differences and equality in information and computer literacy education]. Doctoral dissertation, Amsterdam, the Netherlands: University of Amsterdam.

Volman, M. (1997). Gender-related effects of computer and information literacy education. Journal of Curriculum Studies, 29(3), 315-328.

Volman, M., Van Eck, E., Heemskerk, I., & Kuiper, E. (2005). New technologies, new differences. Gender and ethnic differences in pupils' use of ICT in primary and secondary education. Computers & Education, 45(1), 35-55.

Whitley, B. E. (1997). Gender differences in computer-related attitudes and behavior. A meta analysis. Computers in Human Behavior, 13(1), 1-22.

Whitley, B. E. (1996). Gender differences in computer-related attitudes:I it depends on what you ask. Computers in Human Behavior, 12(2), 275-289.

Whitley, B. E. (1997). Gender differences in computer-related attitudes and behavior. A meta analysis. Computers in Human Behavior, 13(1), 1-22.

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