The antecedents of e-learning outcome: an examination of system quality, technology readiness, and learning behavior.

Ho, Li-An

INTRODUCTION

The development of the Internet and computer technology has generated a breakthrough in time and geographical limitations of learning. Many developed countries such as England, Germany, and the United States have gradually integrated information technology into educational settings to support teaching and learning since the 1980s (Starr & Milheim, 1996). In developing countries such as Taiwan, e-learning has gained public awareness since 2000 through the popularity of educational resources websites, such as EduCities (www.educities.edu.tw) and Etoe (etoe.edu.tw). According to Lee, Tseng, Liu, and Liu (2007), e-learning can be used as a supplemental learning tool in support of conventional teaching (e.g., face-to-face instruction), or as a stand-alone means of facilitating individualized learning (e.g., distance education). Both methods aim to improve students' learning efficiency and effectiveness.

Due to the increasing pressure on the formal educational system at all levels in keeping with the 21st century and the global trends of educational reform, Taiwan's government has recognized the urgency for educational change in order to sustain the overall quality of education and national competitiveness. The Ministry of Education (MOE) has therefore initiated curricular and instructional reforms in elementary and junior high school education. According to MOE (1997), the reformed curriculum aims to equip students with knowledge and skills for developing lifelong learning capabilities. Thus, one of the ten core objectives is to help students utilize current technology. Furthermore, since 2006 MOE has dictated that information technology (IT) and related courses be incorporated into grades 1 to 9 curricula. However, many shortcomings have surfaced in the process, such as the short life cycles of information and communication technology, extensive investment in IT infrastructures, lack of teaching and learning support, lack of computer literacy of teachers and students, and slow or little learning outcome (National Science & Technology Program Office for e-Learning, 2006).

Past research indicates that the factors that influence the outcome of e-learning include student characteristics, such as proactive personality and learning goal orientation (Kickul & Kickul, 2006), learning strategy (Santhanam, Sasidharan, & Webster, 2008), learning motivation (Meissonier, Houze, Benbya, & Belbaly, 2006), effective or appropriate e-learning environment (Gregg, 2007; Wangpipatwong & Papasratorn, 2007), technology acceptance, and system quality (Chang & Tung, 2008). With regard to learning behavior, there is much research on students' attitudes (Sun & Willson, 2008), instructional technology (Wang, 2008), learning resources (Ouyang & Zhu, 2008), learning resources (Ouyang & Zhu, 2008), learning environment (Leung & Fung, 2005), learning methods (Wen & Stefanou, 2007), group collaboration (Webb, Nemer, & Ing, 2006). In addition, a number of studies have found a correlation between learning behavior and learning outcome (e.g., Koopmans, Doornbos, & van Eekelen, 2006; KSnings, Brand-Gruwel, & van Merrienboer, 2005; Leung & Fung, 2005). However, beyond general assertations that the quality of the e-learning system and students' technology readiness and online behavior will lead to positive learning outcome, existing literature offers no testable theoretical model to explain this connection. As "noted, this study attempts to determine which factors result in positive learning outcome through the proposal and empirical validation of a theoretical model. The model incorporates four major dimensions, namely (1) e-learning system quality (eLSQ), (2) students' technology readiness (TR), (3) leadership behavior (LB), and (4) learning outcome (LO). A structural equation modeling approach was employed to test this model.

LITERATURE REVIEW

E-learning System Quality and Learning Behavior

Over the past decade, innovation in educational technology has provided for more online cooperative learning behavior (Duffy & Cunningham, 1996). Pollack (2007) points out that distance education is using new technologies to increase learning and maximize collaboration between teachers and students as well as among students. In their study, Yli-Luoma and Naeve (2006) proposed a sensitive e-learning method based on four phases of knowledge conversion: socialization, externalization, combination, and internalization. They suggested that different emotions and behaviors may be induced during various e-learning phases. For example, the teacher-student interaction in the socialization phase activates exploratory learning behavior. A number of studies demonstrate that certain attributes of an e-learning system will stimulate students' motivation to learn or solicit certain learning behavior, such as reading, searching, browsing, or collaborating (Jung, Herlocker, Webster, Mellinger, & Frumkin, 2008; Given, Ruecker, Simpson, Sadler, & Ruskin, 2007; Finder, Dent, & Lym, 2006).

E-learning System Quality and Learning Outcome

In a study assessing the effectiveness of web-based lessons by teacher perceptions and outcome data of participating students, Stewart (2007) found that the design, content, and usability of web instruction significantly influence students' posttest scores. Kebede (2007) contends that well-designed Web-based geographic information system applications can promote the full potential of emerging technologies to facilitate achievement of the users. In addition, a number of researchers find interactivity to be one of the most important functions of computer-mediated learning. For example, in their study, Proske, Narciss, and Korndle (2007) discovered that interactivity provided within a web-based learning environment is positively related to student achievement. Furthermore, past research demonstrates the correlation between IT system quality and learning satisfaction. Davis et al. (1989) proposed the Technology Acceptance Model (TAM) and' argued that system attributes such as system friendliness and functionality have a significant impact on users' satisfaction levels. In fact, past research suggests that a number of factors influence learner satisfaction with e-learning, including organization and clarity of digital content, breadth of digital content's coverage, learner control, instructor rapport, enthusiasm, perceived learning value, and group interaction (Lee et al., 2007; Kickul & Kickul, 2006; Smith, 2006).

Technology Readiness and Learning Behavior

The literature suggests that personality characteristics have significant moderating effects on online consumer intentions or behavior. For instance, Ranaweera, Bansal, and McDougall (2008) contend that trust disposition, risk aversion, and TR have fundamental relevance to online behaviors. TR is considered a useful tool for identifying users who exhibit both innovative attitudes and behaviors (Matthing, Kristensson, Gustafsson, & Parasuraman, 2006). In their study, Lin, Shih, and Sher. (2007) integrate TR into the TAM model in the context of consumer adoption of an e-service system and discover that readiness and acceptance of technology have a significant effect on consumers' adoption of technological innovations. Moreover, similar correlations between TR and LB may also be recognized in educational settings. For example, van der Rhee, Verma, Plaschka, and Kickul (2007) argue that although learning-goal orientation does not influence students' online learning intentions, students who are more technology-ready do place higher value on participating in a variety of online courses. Garland and Noyes (2005), Keller and Cernerud (2002), and Chau (2001) discovered that computer attitudes, such as confidence, self-efficacy, and perceived usefulness, are influential facets of online LB.

Technology Readiness and Learning Outcome

A number of studies have investigated the use of technology in education settings by analyzing the antecedents and outcomes of technology application. For instance, Park and Wentling (2007) discovered that learners' computer attitudes influence their perception of the usability of e-learning courses, and that this perception has an impact on their degree of skill development and thus also the transfer of learning. Levin and Hansen (2008) argue that students' perceived value of utility determines their attitude toward course technologies, and that their use of course technology has a positive relationship to the LO. Furthermore, Sivo, Pan, and Hahs-Vaughn's (2007) study indicates a strong positive relationship of student attitudes toward Web-based instruction and the effect on three dimensions, namely end-of-course grades, online frequency, and future preference to take a web-based course. Similar conclusions may also be found in the work of Marcolin, Compeau, Munro, and Huff (2000), Agarwal and Prasad (1997), and Lin, Kuo, Kuo, Ho, and Kuo (2007).

Learning Behavior and Learning Outcome

Past research indicates that certain online behavior will determine the outcome of students' achievement (e.g., Hoskins & van Hooff, 2005). In their research, KSnings et al. (2005) found that students' perceptions of a learning environment influences their subsequent LB, which consequently affects the quality of learning achievement. In addition, Whisler (2005) states that online interaction, including instructor-to-learner, learner-to-learner, learner-to-content, and learner-to-learning interface, is a critical component of students' satisfaction. In fact, how students learn to use technology (i.e., be familiarized with the learning environment) is also crucial to their LO. Webber (2004) identifies a link between orientation to learning and various outcomes of learning activities. Moreover, Lei (2004) compared students' LO and perceived effectiveness with their e-learning behaviors as well as their computer attitude, computer experience, and demographic characteristics. The results show that lengthy computer experience does significantly affect students' achievement scores. In addition, students appear to benefit from frequent online interactions with peers, instructors, or content material. Furthermore, students' self-regulatory behaviors as well as formative assessment along with self-reflection are important aspects of 5th grade science learning (King, 2003).

METHOD

Research Structure and Hypotheses

The relevant hypotheses of the model and questionnaire design are presented below. The research model is shown in Figure 1.

H1: E-learning system quality positively influences learning behavior of junior high school students.

H2: E-learning system quality positively influences learning outcome of junior high school students.

H3: The impact of students' learning behavior on learning outcome will be stronger under the influences of e-learning system quality.

H4: Junior high school students' technology readiness positively influences their learning behavior.

H5: Junior high school students' technology readiness positively influences their learning outcome.

H6: Students' learning behavior regarding learning outcome will be stronger under the influences of technology readiness.

H7: Junior high school students' online learning behavior positively influences their learning outcome.

[FIGURE 1 OMITTED]

Questionnaire Design

The questionnaire is composed of five parts including: eLSQ, TR, LB, LO, and personal background (i.e., gender, grade level, and e-learning experience). A five-point Likert scale was used (1 = strongly disagree to 5 = strongly agree). Details of the dimensions are as follows:

E-learning system quality. This study adopted the three-factor model of IT quality dimension proposed by Medina and Chaparro (2007). The model includes the most studied elements in the modern world, namely information quality, system quality, and service quality. While the quality of information refers to the appropriateness, updated-ness, usefulness, accuracy, completeness, and relevance of the online course content, the quality of system refers to the friendliness, flawlessness, efficiency, and adaptability of the e-learning system. Finally, the service quality is defined as the tangible aspects of the system--staff reliability, responsibility, and empathy as well as students' confidence in the online staff.

Technology readiness.

Parasuraman (2000) defines TR as the tendency to embrace and use new technology to accomplish goals in professional as well as personal lives. This study adapts Rarasuraman's four-dimension TR model which includes optimism, innovativeness, discomfort, and insecurity, and makes some adjustments. The factors included are optimism, innovations, comfort level, and sense of security. While optimism refers to the positive view of technology and the belief that it offers more control, innovations refer to the tendency of learners to be a technology pioneer who is open to new things or ideas. Comfort level is defined as perceived control of technology (i.e., students are not overwhelmed by the use of new technology). Finally, sense of security refers to trust of technology in its ability to work properly or as expected.

Learning behavior refers to the approach to the challenge of various learning situations (van Gelderen, van der Sluis and Jansen, 2005). This study adopted the LB construct applied in the study conducted by Leung and Fung (2005). According to Leung and Fung, learning behaviors are classified into six categories, including coordination (i.e., work with peers, share with peers, get involved, being happy), academic performance (i.e., study confidence, academic goal, active, less distracted), attention (i.e., relaxed, refreshment, concentration, energetic), online preference (i.e., excited in virtual classroom, feel time passes quickly, remain in virtual classroom), discipline (i.e., perform learning activities appropriately and express opinions appropriately and politely), and goal achievement (i.e., creativity, persistence).

Learning outcome. This study adopts van Gelderen et al.'s (2005) three measures in assessing junior high school students outcome of learning, namely job goal achievement, satisfaction, and skill development. Goal achievement refers to the extent to which the self-perceived results of the students are consistent with their expectations as well as their teachers and parents. Secondly, satisfaction refers to the extent to which students are satisfied with the e-learning system, their learning initiatives, as well as the strategies they use in order to learn online. Finally, skill development refers to whether the knowledge and skills learned online can be applied to real-life situations.

Sampling

The information used in this research consists of questionnaire responses from participants in 10 urban junior high schools located in six school districts in the city of Tainan in Taiwan. The survey targeted students in grades 7 to 9 who have had e-learning experience in school. The number of questionnaires distributed at each school was based on probability proportionality to the total number of students who have participated in e-learning courses. A total of 600 questionnaires were distributed, of which 389 were returned; 376 were valid for analysis (62.7%). Non-response analysis was conducted to ensure there were no non-response biases. Results revealed no differences between respondents and non-respondents. Table 1 shows the sample characteristics.

RESULTS

Reliability and Validity Tests

Cronbach [alpha] reliability estimates were used to measure internal consistency of the multivariate scales (Nunnally, 1978). In this study, Cronbach [alpha] of each construct was greater than 0.907, which indicates strong reliability for our survey instrument (Cuieford, 1965). Since the item-to-total correlations of each measure was at least 0.614, the criterion validity of each scale in this study is considered to be satisfactory (Kerlinger, 1999). Table 2 shows the descriptive statistics for each dimension.

Both exploratory and confirmatory factor analyses were used to ensure reasonable construct validity. The results of exploratory factor analysis are presented in Table 3. The confirmative factor analysis which consists of the convergent and discriminant validity followed Campbell and Fiske's (1959) criteria. The results show that the correlations are all greater than zero and large enough to proceed with discriminant validity. Furthermore, discriminant validity was examined by counting the number of times an item correlates higher with items from other factors than with items from its own factor (Aldawani & Palvai, 2002). Campbell and Fiske suggest that this number should be less than 50 percent. Results also show adequate discriminant validity. Jointly, the constructs in this study exhibit both convergent and discriminant validity.

Analysis of the Structural Equation Model

The structural equation modeling approach is a multivariate statistical technique for testing structural theory (Tan, 2001). This approach incorporates both observed and latent variables. The analysis for the present study was conducted using LISREL 8.52, utilizing the maximum likelihood method. In the proposed model (Figure 1), eLSQ and TR are considered exogenous variables, and LO is considered an endogenous variable. LB serves as both an endogenous variable (to eLSQ and TR) and exogenous variable (to LO). The individual questionnaire items were aggregated into specific factor groups. The following four rules were utilized for the hypotheses' structure: (1) each observed variable has a nonzero loading on the latent factor within the structure, but with a loading of zero toward other latent factors, (2) no relationship among measurement errors for observed variables, (3) no relationship among the residuals of latent factors, and (4) no relationship among residuals and measurement errors. Reliability results are illustrated in Table 4.

Additionally, the analytical results of the LISREL model reveal a satisfactory fit for our sample data. The final result of LISREL analysis is shown in Figure 2.

The final SEM model analysis is presented in Figure 2. The absolute fit measures (GFI=0.98 AGFI=0.97 and RMSEA=0.044) indicate that the structural model either meets or exceeds recommended levels, and thus represents a satisfactory fit for the sample data collected. The Chi-square statistic divided by the degrees of freedom also indicates a reasonable fit at 1.74. It can be concluded that the proposed model maintains good construct validity (see Table 5 for the statistics of the fit test of the model). Based on Figure 2, five of our seven hypothesized relationships (H1, H3, H4, H6, & H7) show statistical significance.

[FIGURE 2 OMITTED]

DISCUSSION

Based on the analysis, a number of observations can be made, with all of the observations having been positive effects. It is shown that both e-learning system quality and technology readiness have a direct and significant effect on learning behavior; thus the validity of Hypothesis 1 and Hypothesis 4 is demonstrated. These results thus support the observation that two dimensions, namely service, system and information quality of e-learning systems and the self-perceived technology readiness of junior high school students positively affect their online learning behaviors. The analysis also shows that learning behavior has a direct and significant effect on learning outcome, establishing Hypotheses 7 as valid. The results show that online learning behaviors of junior high school students result in improved skill development, goal achievement, and satisfaction levels, an observation which supports the work of van Gelderen et al. (2005), Hoskins and van Hooff (2005), and Whisler (2005). Existing research consistently offers evidence of the correlation between learning behavior and achievement. For instance, Worrell and Schaefer (2004) note that learning behavior scores can predict achievement scores of academically talented students.

The analysis has shown that neither the quality of an e-learning system nor students' technology readiness has a direct and statistically significant effect on learning outcome. Hypothesis 2 and Hypothesis 5 are thus rejected. The failure of both hypotheses are partially supported by the observation made by Lin et al. (2007), that IT investment and acceptance have no direct influence on adult learners' learning outcome. The finding of their study indicates that well-designed systems and competent leaders do not directly result in better performance. Only if certain learning behaviors are followed can better outcomes be achieved.

Finally, statistical analysis shows that the effect of learning behavior on learning outcome is stronger under the influence of e-learning system quality as well as technology readiness. Hypothesis 3 and Hypothesis 6 thus are shown to be valid. This finding is in line with the work of Meissonier, Houze, Benbya, and Belbaly (2006) in which they found that motivation and self-discipline of students are the main drivers of e-learning outcomes. Similar results can be found in studies conducted by Smith (2005) and Chang and Tung (2008), that technology compatibility, perceived usefulness, perceived ease of use, system quality, and learners' computer efficacy are critical factors in students' behavioral intentions (or learning preference) to use the online learning courseware, which consequently affect the actual behavior as well as the outcome of their learning.

CONCLUSION

This study focused on analysis of e-learning system quality, technology readiness, online behavior, and learning outcome of students in urban junior high schools. Specifically, the study was designed to determine the effect of students' technology readiness and self-perceived e-learning system quality on the perception of learning behavior. In turn, the effect of students' online behavior on their learning outcome is also examined. An empirical investigation using structural equation modeling shows that both the students' perceived quality of e-learning system and their technology readiness are positive and important aspects in reaching better goal achievement and skill development as well as higher learning satisfaction. However, it must be highlighted that the self-perceived system quality and technology readiness do not directly result in better learning outcome. Rather, these factors serve as catalysts in stimulating students' online learning behavior (Hung & Cho, 2008). Appropriate learning behaviors, in turn, serve as channels for better outcome of e-learning. E-learning system quality and junior high school students' technology readiness can thus be seen as links in a chain, with learning behaviors in the middle linking those factors with learning outcome (Lu & Yeh, 2008; Kaya, 2007; Siemsen, 1993).

While the empirical data collected have largely supported the proposed model, it is necessary to point out the limitations of this research. Even though the participants consisted of well-informed and active junior high schools e-learning students, the existence of possible biases cannot be discounted. Furthermore, it is evident that the platforms, content, and hardware equipment used can differ among schools in different areas or even those in the same areas which offer dissimilar e-learning models (e.g., Dahl & Vossen, 2008; Lu & Yeh, 2008; Hu, Chen, Zeng, Hao, Min, & Liu, 2008). Thus, the data collected may not be fully representative of other scenarios.

In conclusion, the study can suggest that junior high school principals and teachers can improve their students' learning outcome via e-learning by facilitating proper learning behaviors, such as promoting better interaction between peers and helping students remain focused on online activities in order to accommodate the needs of students with different levels of technology readiness. 'Also, they can provide well-designed e-learning systems that match the content of the subject matter and accommodate preferences of the students. Furthermore, principals and teachers are reminded that the quality of the e-learning systems and students' technology readiness are supported by appropriate leadership behavior, without which better learning outcome cannot occur.

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Requests for reprints should be sent to Li-An Ho, Department of Educational Technology, Tamkang University, 151, Yin-Chuan Road, Tamsui, Taipei Hsien 251, Taiwan, R.O.C. E-mail: lianho@mail.tku.edu.tw

Table 1. Sample characteristics

 Percentage
Construct Classification Number (%)

Gender Male 204 54.26
 Female 172 45.74

Grade level 7th grade 193 51.33
 8th grade 104 27.66
 9th grade 79 21.01

e-learning Yes 227 60.37
experience No 149 39.63

Table 2. Survey structure and description statistics for dimension

 Number
 of items
 per Std. [alpha]
Dimension dimension Mean Dev. Order value

e-Learning
system quality 14 3.518 0.586 2 0.931

Technology
readiness 16 3.370 0.457 4 0.907

Learning
behavior 27 3.523 0.490 1 0.955

Learning
outcome 11 3.503 0.490 3 0.928

Table 3. Validity and reliability for the questionnaire

 Item-to-
Dimension % of Cumulative Total [alpha]
Factor Variance % Correlation Value

e-Learning system quality

Services 53.094 0.672 0.905
 quality
System 12,581 0.723 0.905
 quality
Information 10.672 0.754 0.936
 quality 76.347

Technology readiness

Comfort level 42.917 0.678 0.864
Optimism 11.082 0.632 0.854
Sense of 8,487 0.671 0.837
security
Innovativeness 6.846 0.614 0.835
 69.331
Learning behavior

Coordination 47,390 0.666 0.917
Attention 8.570 0.829 0.940
Discipline 6.273 0.775 0.922
Academic 5.630 0.780 0.911
 performance
Online 4.664 0.720 0.900
preference
Goal 3.720 0.737 0.877
 achievement 76.247

Learning outcome

Goal 58.432 0.837 0.945
achievement
Satisfaction 12.026 0.710 0.892
Skill 9.379 0.628 0.839
 development 79.837

Table 4. Observed indicator reliability of factors

 Observed
 indicator
Dimensions Factors reliability

e-Learning Information quality 0.61
system quality System quality 0.68
 Services quality 0.68

Technology Optimism 0.65
readiness Innovativeness 0.62
 Comfort level 0.73
 Sense of security 0.63

Learning Coordination 0.78
behavior academic performance 0.74
 Attention 0.70
 Online preference 0.7g
 Discipline 0.57
 Goal achievement 0.59

Learning Skill development 0.68
outcome Goal achievement 0.71
 Satisfaction 0.80

Table 5. Fit test of the model

Measures and Indicators

Absolute Fit Measures

Chi-Square with 98 Degrees of Freedom=170.33 (P=0.00)
Goodness of Fit Index (GFI) = 0.98
Root Mean Square Error of Approximation (RMSEA) = 0.044
P-Value for Test of Close Fit (RMSEA < 0.05) = 0.79
Expected Cross-Validation Index (ECVI) = 0.66
90 Percent Confidence Interval for ECVI =(0.57 ; 0.76)
ECVI for Saturated Model = 0.73
ECVI for Independence Model = 6.54
Adjusted Goodness of Fit Index (AGFI) = 0.97

Incremental Fit Measures

Normed Fit Index (NFI) = 0.93
Non-Normed Fit Index (NNFI) = 0.96
Comparative Fit Index (CFI) = 0.97
Incremental Fit Index (IFI) = 0.97
Relative Fit Index (RFI) = 0.91

Parsimonious Fit Measures

Parsimony Normed Fit Index (PNFI) = 0.76
Parsimony Goodness of Fit Index (PGFI) = 0.71
Critical N (CN) = 294.87
Normed chi-square 170.33 / 98 = 1.74