E-Learning and gender

1 JORGE ARENAS-GAITÁN Department Business Management and Marketing University of Seville Av. Ramon y Cajal, 1 41018 Seville SPAIN Tlf: (+34) 954554427 [email protected] F. JAVIER RONDAN-CATALUÑA Department Business Management and Marketing University of Seville Av. Ramon y Cajal, 1 41018 Seville SPAIN Tlf: (+34) 954554427 [email protected] PATRICIO E. RAMIREZ-CORREA Department Systems Engineering and Computation University Catholic of the North Av. Angamos 0610, Antofagasta CHILE Tlf: (55)355167 [email protected] 2 E-Learning and Gender Abstract: - This study is to examine gender differences and the adoption of technology in tertial education students. We have used TAM model to measure the acceptance and use of elearning of the respondents. ANOVA and Partial Least Squares (PLS) was used, specifically, the PLS multi-group analysis, to compare differences between groups. In summary, results show that students’ behavior of acceptance of e-learning technology do not manifest statistically significant differences between women and men. Key-Words: TAM model, e-learning platforms, gender analysis, tertiary education, Partial Least Square, multi-group analysis 1. Introduction The adoption of e-learning environments by university students is growing. While most institutions fully Web programs are less than 5% of total enrollment, the number of students enrolled in at least one course with a relevant online presence would be from 30% to 50% of total enrollment, and this is a growing trend. The purpose of this growth is to supplement traditional instruction (Harris et al, 2003), making possible to develop methods for more portable and flexible learning (Zhang and Nunamaker, 2003). According to this idea, many universities around the world are using e-learning environments in the last decade. Furthermore, in learning platforms where the information is reaching critical amounts, the user demands for more personalized and adaptive system interaction (Aroyo and Dicheva, 2004), and perhaps gender would be one of the variables to use for discriminating information. In terms of Web technology, (Sanchez-Franco et al., 2006) indicated that women and men would differ on the level of trust, risk aversion and information processing, but also in their attitudes of use and instrumental motives of acceptance of Web environments. In this sense, 3 we propose that diagnostic usefulness and perceived ease of use of e-learning environments must be a first step towards correcting possible deviations and promoting the appropriate use of these constructs in university teaching. And in turn, results may help to the proper design of such environments to respond to the different motivations of students. In a broader context, gender is considered a cornerstone to explain the inequalities and identities in modern society (Walby, 1990). In the background of adoption of information technologies, and particularly from the theoretical perspective of Technology Acceptance Model (TAM), the literature recognizes that gender is a key element to understand the differences in perceptions of usefulness and ease of use as determinants of technology adoption (Venkatesh et al., 2003). But with regard to e-learning platforms. Does gender affect on how college students adopt information technology to provide learning solutions efficiently and effectively? Unfortunately, the effect of gender roles in TAM has been scarcely research (Ong and Lai, 2006), even less in relation to e-learning platforms. And as He and Freeman (2009) manifest, evidence on the effect of gender is far from conclusive. This lack of findings justifies the purpose of this work. The main objective of this study is to explore gender differences in the adoption of technology in virtual learning platforms for a sample of college students. In addition a model based on TAM in this type of educational tools is tested. To achieve these goals, a review of the literature on e-learning platforms and technology acceptance model is developed. Based on such review a model based on TAM to measure the acceptance and use of e-learning by respondents is proposed. Third, the results of applying the Partial Least Squares (PLS) analysis to the TAM model on the entire sample, and the sub-samples of women and men are presented. ANOVA was used to compare constructs measurement, and PLS multi-group analysis was used to compare differences between groups. Finally the main conclusions are exposed 2. Literature review 2.1. E-Learning in higher education E-Learning is defined as an Internet-enabled learning process (Gunasekaran et al., 2002). The 4 first courses over the Web started to emerge in 1995 and there has been a rapid expansion of on-line learning since then. One of the main reasons for the widespread use of on-line learning in many institutions is that most students now have access to the Internet. The University of British Columbia, in Vancouver, Canada, offered its first credit courses delivered entirely over the Internet to distance education students in 1996. The same year Murray Goldberg developed a software package called WebCT designed to enable Web based courses to be offered over the Internet (Bates, 2005). In order to support e-learning, various Web-based learning systems have been developed for colleges and universities. Such as the Web Course Homepage System (WebCH), Blackboard Learning System, the System for Multimedia Integrated Learning (Smile) and Web Course Tools (WebCT). However, Webbased learning must take into consideration that education has activated a shift from the teaching paradigm to the learning paradigm. As a result, students are becoming more independent from the teacher. Of course, there are many benefits of using online technologies in higher education, all students are guaranteed to receive the same presentation material and get the same view of the professor and material. Course information can be viewed at students’ convenience and as many times as they wish (Bender and Vredevoogd, 2006). Unfortunately, much of the development of Web-based learning is carried out without a true understanding of issues that are proper to Web-based learning (Hadjerrouit, 2006). In general, Internet-based activities have been incorporated into regular face-to-face classes as an added resource, without reducing classroom time, but in many cases teachers have reduced the number of face-to-face classes (Bates, 2005). A popular format for teaching both in the classroom and online is blended learning. This type of learning involves both traditional face- to-face instruction, where both students and faculty are present at the same time in a traditional campus of “brick and mortar”, supplemented with communication via the Internet in a electronic classroom of “wire and chip” (Bender and Vredevoogd, 2006). For lecturers and students, the implications of e-learning are extensive. However, in the case of the former some studies (Alonso Diaz y Blazquez Entonado, 2009) have found no important differences between the functions of teachers in the online teaching and face-to- face teaching. And if these differences do exist, they are likely to be due to the teacher’s involvement and the institution’s commitment in the programming of the instruction method. With regard to the latter, increasingly universities must provide quality and flexibility to meet the diverse needs of students – this will inevitably involve tailoring courses to suit differing 5 educational needs and aspirations. Another implication of virtual learning is the increase of international competition for students by many universities; new communication methods are useful tools that encourage internationalization of tertiary learning (O’neill et al., 2004). Furthermore, e-learning reflects the new dynamic response to the needs of a knowledge society and implies freedom and equality to access knowledge beyond cultural and social boundaries (Raza and Murad, 2008). Therefore, it is necessary to investigate whether there are gender differences in the use or perception of e-learning. In case they exist it would be indispensable to implement policies of integration or adaptation of these technologies. 2.2. E-Learning and TAM Proposed by Fred Davis (Davis, 1989), TAM posits that individual behavior intention to use information technology is determined by the perceived usefulness and perceived ease of use. Also, perceived ease of use is directly impacted by perceived usefulness. Since then, several revisions and expansions have developed the original model. The most popular developments have been TAM2 (Venkatesh and Davis, 2000) and TAM3 (Venkatesh and Bala, 2008). The literature presents several studies using TAM to assess users’ acceptance of e-learning technology. In most of these studies, TAM was extended using factors predictors or moderators, such as: subjective norms (Van Raaij and Schepers, 2008; Park, 2009); computer self-efficacy (Ong and Lai, 2006; Chang and Tung, 2008; Park, 2009); perceived playfulness; (Roca and Gagné, 2008); cognitive absorption (Saade and Bahli, 2005); (Liu et al., 2009); system features (Chang and Tung, 2008); (Liu et al., 2009); (Park, 2009); computer anxiety (Van Raaij and Schepers, 2008); gender (Ong and Lai, 2006); motivational factors (Park et al., 2007; Roca and Gagné, 2008); personal innovativeness (Van Raaij and Schepers, 2008); technical support (Ngai et el., 2007); perceived credibility; and compatibility (Chang and Tung, 2008). 2.3. Proposed Model We have proposed a model based on the basic TAM, which relates the constructs of Perceived Usefulness (PU), Perceived Ease of Use (PEOU) and Behavioral Intention (BI). It also includes the effect of BI on the Use of the Platform e-Learning (USE). This basic TAM model is enriched with three antecedents on the one hand, result demonstrability (RES) precedes PU, 6 and secondly, Perception of External Control (PCE) and Perceived Enjoy (ENJ) precede PEOU. The proposed TAM model including 7 hypotheses is shown in Figure 1. Figure 1: Proposed Model                         The TAM model has been used successfully in the context of e-learning (Saadé et al., 2007). In particular, Halawi & McCarthy results (2008) suggest that students use e-learning environment (USE), if they perceive it useful (PU) and easy to use (PEOU). Previously, Ngai et al. (2007) indicate that the perceived ease of use (PEOU) and perceived usefulness (PU) are the main factors affecting the attitude of university students to use e-learning (BI). Also, Hayashi et al. (2004) had verified that the perceived usefulness (PU) directly affects student's intention to continue using e-learning (BI). Considering the importance of a replica in a sample culturally different from those already explored, and based on these previous studies, the following hypotheses are proposed: H1: PU is positively related to BI in adopting e-learning in higher education. H2: PEOU is positively related to PU in adopting e-learning in higher education. H3: PEOU is positively related to BI in adopting e-learning in higher education. H4: BI is positively related to USE in adopting e-learning in higher education. 7 Venkatesh and Bala (2008) proposed that result demonstrability (RES) is an antecedent of perceived usefulness (PU). On the other hand, the same authors proposed that perception of external control (PCE) is an antecedent of perceived ease of use (PEOU). Perceived enjoyment using information technology (ENJ) is defined by Roca and Gagné (2008) as the degree to which the activity of using an information technology is perceived as pleasant by itself, apart from the intrinsic instrumental value of the technology. Venkatesh and Bala (2008) proposed that perceived enjoyment using information technology (ENJ) is positively related to perceived ease of use (PEOU). In the context of e-learning environments, results of (Roca and Gagné, 2008) reported that perceived enjoyment (ENJ) directly affects perceived ease of use (PEOU). Based on these results, the following second group of hypotheses are proposed: H5: RES is positively related to PU in adopting e-learning in higher education. H6: PCE is positively related to PEOU in adopting e-learning in higher education. H7: ENJ is positively related to PU in adopting e-learning in higher education. The evidence about the effect of gender on the acceptance of information technology is not conclusive (He and Freeman, 2009). Results of previous studies show conflicting evidence in relation to whether gender affects or not the likelihood of using a computer system. For example, results of Taylor (2004) indicate the existence of such effects, and on the contrary, results of Morris et al. (2005) indicate that these effects may disappear, especially in a young population. Also in Web environments not clear evidence on gender-related effects exists. Kim and Forsythe (2000) reported not statistically significant differences between men and women in the process of adopting a particular Web technology. In contrast, there is previous evidence of gender-related effects in the context of the adoption of e-learning. Particularly, Ong and Lai (2006), based on a sample of Taiwanese workers, show that men's scores on the perceived usefulness (PU), perceived ease of use (PEOU) and behavioral intention to use elearning (BI) are higher than scores of women. In addition, perceived usefulness (PU) influences the behavioral intention to use e-learning (BI) more strongly for men than for women. And, similarly, perceived ease of use (PEOU) influences the perceived usefulness of e-learning (PU) more strongly in women than in men. Considering the controversy explained above and the importance of a replica in a culturally 8 distinct sample, and based on the study of Ong and Lai (2006), the following hypotheses are proposed: H0a: Statistically significant differences between men and women exist in the scores of adoption of e-learning variables in college students. H0b: Statistically significant differences between men and women exist in relationships between variables of the adoption of e-learning in college students. 3. Methodology Empirical research was based on a nonrandom sampling method. Data were collected in Spain and Chile through an online questionnaire from May to July 2009. The online questionnaire was sent to students of a main University from south of Spain and of a main University from north of Chile. Spanish respondents were taking courses in the areas of Marketing and Business Management using e-learning platform WebCT. Chilean respondents were enrolled in courses of Engineering using e-learning platform ClaroLine. The exclusion of invalid questionnaires provided a final sample size of 389 students, 201 males and 188 females. The average age of interviewees was 23.12 years old and they have been studying for 4.03 years (on average) at the University. So, they are students with extensive experience as college students. The applied measurement scales have been widely tested in other research. Specifically, to measure TAM construct the scales proposed by Venkatesh and Bala (2008) have been adapted. Partial Least Squares (PLS) approach is a type of Structural Equation Modeling (SEM) that was used to test the proposed research model (Chin, 1998; Tenenhaus et al., 2005). Initially, the proposed model was validated for the whole sample (389 cases). Then the sample was divided into two groups: males and females. T Student test was used to analyze if gender differences in the different constructs of the model exist. Then Multi-group PLS analysis was run to compare differences between groups. SmartPLS software was used for this analysis (Ringle et al., 2005). 4. Results 9 Results of the descriptive statistics are shown in Table 1. The scale used is a 5-point Likert type, except variable USE that is measured in minutes per week. SPSS software was used for this test. As you can see in Table 1, T test results indicated statistically significant differences between the scores of men and women in some variables: use, perceived ease of use, perception of external control and behavioral intention (this one near the limit of 95% of confidence level). As the variables did not meet the requirement of normality, nonparametric techniques, specifically Mann-Whitney test, were applied to corroborate T test results. The estimates provided similar results. Therefore, hypothesis H0a is partially accepted. Scores for females in the four significantly different variables are higher than for males. Table 1: Descriptive Statistics and T Test Gender Mean Tip. Dev. T test (sig.) PU Male 3.8520 .85450 -1.327 Female 3.9574 .69964 (0.183) PEOU Male 4.1306 .81512 -1.327 Female 4.3019 .64814 (0.022) PCE Male 3.8934 .63170 -1.327 Female 4.0536 .60668 (0.011) ENJ Male 3.2289 .94141 -1.327 Female 3.2801 .72524 (0.546) RES Male 3.4301 .89195 -1.327 Female 3.5780 .74044 (0.077) BI Male 4.0471 .83089 -1.327 Female 4.2041 .79955 (0.060) USE Male 56.77 55.232 -1.327 Female 84.77 101.081 (0.001) A PLS model is described by two models: the measurement model and the structural model. As a previous step to the structural model analysis is necessary to analyze reliability and validity of the measurement model. Factor analysis (using varimax rotation) was applied and every individual item is grouped inside the correspondent construct, achieving an explained total variance of 0.729. With regard to content validity is based on the theoretical and empirical evidence supported 16 • Liu, S., Liao, H. and Pratt, J.A. (2009). 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