and refer to a number of studies that relate race and gender to students’ perceptions of teacher credibility (Hendriks, 1997, Rubin, 1998, Patton, 1999, Centra and Gaubatz, 2000).
It is not difficult to see how the findings referred to in the foregoing in respect of racial identity, teacher and student perceptions, immediacy and affinity and the impact thereof on student attitudes, motivation and performance in the classroom are related. Although different authors and studies have different focus areas, the nexus appears to be the resultant attitude or perception of the student and their sense of affinity with the teacher in the classroom. Racial identity issues may impact the way in which the student reacts to teachers who exhibit racially biased attitudes and behaviours. Teacher perceptions of students may be racially prejudiced or otherwise negative and discriminatory, which in turn affects the student’s sense of affinity with the teacher. Teacher immediacy behaviours are only impactful in terms of student reactions to them. Thus it would appear that student perceptions are a key aspect in any discussion on factors that impact student performance in the multicultural classroom.
2.6 Social cognitive theory in computer education
Bandura’s theory related to model and observer characteristics and perceptions of credibility, affinity and similarity aligns with the findings of various researchers discussed in the foregoing sections on racial identity, immediacy and affinity in the multicultural classroom (McCroskey and Richmond, 1992, Chavous et al., 2003, Rucker and Gendrin, 2003, Glascock and Ruggiero, 2006, Wilson, 2006, Schrodt et al., 2009).
2.6.2 Self-efficacy and computer education
Apart from the constructs related to models and observers, one of the most influential constructs in SCT is ‘self-efficacy’ (Bandura, 1977b, 1994, 1995, 2000). Bandura describes self-efficacy as an individual’s perception of his or her own capability to achieve a task or learn a behaviour. Thus, a high level of self-efficacy is associated with more effective learning and the converse is also true- a low self-efficacy rating tends to impede learning. Bandura extends this concept to include the perceptions a reference group has of its own capabilities and calls this ‘collective self-efficacy’
(Bandura, 1995, 2000). A number of authors have referred to Bandura’s construct of collective self- efficacy in explaining culture based variations in academic achievement (Oettingen, 1995, Bandura, 2000, Tschannen-Moran and Barr, 2004, Klassen et al., 2010, Moseley and Taylor, 2011). With specific reference to technology and computer science education, researchers have coined terms such as ‘computer self-efficacy’, ‘computer anxiety’ and ‘technology self-efficacy’ to refer to the perceptions of capability individuals or reference groups have in respect of information technology specific skills (Marakas et al., 1989, Busch, 1995, Compeau and Higgins, 1995, Saleem et al., 2011).
A variety of studies have attempted to show a link between computer self-efficacy and academic performance in computer related education, with mixed results. Some of these studies have suggested that high levels of computer self-efficacy are positively related to academic achievement (Harrison and Rainer, 1997, Smith, 2002b). Other studies show no such direct link between computer self- efficacy and performance (Singh et al., 2010).
Ausburn et al. (2009) investigate the role played by what they term ‘technology self-efficacy’ in understanding gender effects in technology-based learning environments. These authors note that a variety of conceptual areas, including social and culturally influenced perceptions of and experiences with computer technology, provide useful insights into the differences across genders in research related to virtual environments and come together in self-efficacy theory. Ausburn and her colleagues refer to ‘technological self-efficacy’ as a determinant of an individual’s ‘performance and perception of that performance in a technology learning environment such as virtual reality (Ausburn et al., 2009). Commenting on gender differences in performance related to virtual learning environments, Ausburn et al. (2009) note the findings of various studies that have identified gender as a strong
predictor of technological self- efficacy, with females consistently shown to be more likely to rate self-perception of their computer skills lower than males (Temple and Lips, 1989, Busch, 1995, Hargittai and Shafer, 2006, Hogan, 2006, Bain and Rice, 2007). Women have also frequently reported less confidence and more anxiety with usage of spatially-related materials and computer software, have displayed higher levels of ‘computer anxiety than males, and generally view technology and computers as more difficult to master and less interesting than males do (Weil and Rossen, 1995, Whitley, 1996, Schumacher and Morahan-Martin, 2001, Gilbert et al., 2003, Rainer et al., 2003, Terlecki and Newcombe, 2005, Todman and Day, 2006). Attempting to explain the technology gap between genders, the American Association of University Women Educational Foundation (2000) identified teacher attitudes, public media, software manufacturers, and curriculum as factors contributing to gender technology self-efficacy deficits and lowered self-confidence of young females about technology and computing.
Various other studies have found that gender differences in computer self-efficacy levels are related to the complexity of the task at hand. Murphy et al. (1989) found, for example, that the difference in self-efficacy rating between the genders was highest when computers were used on an advanced level.
Busch (1995) found similarly in his study that at the more fundamental levels of Word Perfect and Lotus end user skills, males and females did not differ significantly in terms of self-efficacy expectations. Interestingly, Busch found not only that female students had lower self-efficacy in respect of more complex computing tasks than males, they also had less experience in programming and in paying computer games than their male counterparts. In addition, they tended to receive less positive reinforcement from friends and family and had less access than males to computers at home (Busch, 1995).
Busch (1995) makes an interesting observation regarding cultural differences in how computer self- efficacy is experienced, referring to the ‘process of socialisation’. Busch opines that gender differences in both self-efficacy and general attitudes towards computers are the result of social conditioning that begins in the home. Busch suggests that a ‘sex-role identity is formed in the first instance within the family where norms are internalized, attitudes are learned and a self-image is acquired’ (Busch, 1995). These behaviours are reinforced and developed in the school and work setting where society’s norms are imposed, including gender biases in respect of the types of career and interest areas that are ‘appropriate’ for males and females. Therefore, gender differences in attitudes toward computers and computer self-efficacy are thus the product of different social experiences and depend to a large extent on the norms of the particular culture an individual is a part of (Busch, 1995).
In line with Busch’s theory of cultural socialisation, various studies have shown that there do indeed appear to be significant differences between cultures in respect of gender disparities in computer self- efficacy and attitudes toward computers (Turkle, 1984, Collis and Williams, 1987, Elkjær, 1992, Makrakis, 1992). Collis and Williams (1987), for example, found that while both Chinese and Canadian students exhibited gender differences in computer self-efficacy and attitudes toward computers, Chinese students displayed fewer differences than their Canadian counterparts. Similarly, Makrakis (1992) compared gender differences in computer self-efficacy among Japanese and Swedish students and found that for both genders, Swedish students had higher levels of computer self-efficacy than Japanese students. Swedish male students were significantly more positive in their attitude toward computers than Swedish females, and there were no significant gender differences in attitudes for the Japanese students. Results such as these seem to support the theories of authors like Turkle (1984) who claim that gender differences are the product of socio-cultural expectations that differ from culture to culture and that determine models of ‘correct and appropriate’ behaviour for children of each gender.
Research would seem to suggest, then, that despite gains in their positive perceptions and usage of computers, females continue to lag behind males in technology and computer self-efficacy, which may continue to impact their performance in technology learning environments. Cooper (2006) reports on decades of literature related to gender disparities in computer and technology self-efficacy and suggests that it is fundamentally a problem of ‘computer anxiety rooted in gender socialization interacting with stereotype of computers as primarily a male dominated interest area’. In Cooper’s view, this computer anxiety and low technology self-efficacy rating accounts for the variations in computer related attitudes and performances that are frequently observed and reported in cross-gender computer studies (Cooper, 2006).
An interesting variation in the ‘gender difference in computer self-efficacy’ theme is explored by Saleem et al. (2011) investigate the role of personality traits as antecedents to computer self-efficacy and he role of gender as a moderating factor (see Figure 2-1). Once again, gender differences were found, with the traits of neuroticism, extraversion, openness, conscientiousness and agreeableness being shown to be significantly related to computer self-efficacy for women, but not for men.
Surprisingly (given the earlier research finding that females report lower computer self-efficacy than males (McIlroy et al., 2001, Schumacher and Morahan-Martin, 2001, Chau and Hu, 2002, Durndell and Haag, 2002)), Saleem et al. (2011) found that females scored significantly higher than males on computer self-efficacy, and suggest that this may be due to the fact that the sample comprised mainly
graduate students and faculty members who had previously had significant exposure to computers and technology.
While the research on gender differences in computer self-efficacy consistently suggests that females tend to have lower perceptions of their own computer-related capability, the findings on race-based disparities are not as consistent. While some studies suggest that minority groups have lower computer self-efficacy (Galpin et al., 2003), other studies either find no statistically sound basis for this conclusion or find that the opposite holds true in certain instances (Johnson et al., 2008). For example, Clifford (2007) found no statistically significant differences in the levels of computer self- efficacy of African-American, Hispanic American and Caucasian-American college students, although she did find that Hispanic students tended to have higher levels of computer related anxiety than the other racial groupings. Johnson et al. (2008) investigated, inter alia, the relationship between computer self-efficacy, ethnicity and gender among African-American and Anglo-American students
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Neuroticism
Extraversion
Conscientiousness
Agreeableness
Openness Computer self-
efficacy Gender
Figure 2-1 Saleem et al. research model (Source: Adapted from Saleem et al. (2011))
and found that African-American students scored higher on IT self-efficacy than their Caucasian counterparts, and that Anglo-American females had lower IT self-efficacy than any other group.
2.7 The unique South African context