october 2012 - TOJET the Turkish online journal of educational ...

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TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 RESEARCH METHOD Participants Thirty university students aged between 18 and 22 from a suburban university in Kaohsiung, Taiwan participated in the study. They were invited to attend ten-week online English learning courses (one-hour lesson per week) via a videoconferencing software, Joinnet. Joinnet is client software, connecting to multimedia meeting and messaging servers, for video conferencing, white-board presentation, synchronized web browsing, desktop, and application sharing. The students’ English abilities have been tested by a TOEIC (Test of English for International Communication) test to make sure that their English abilities are in the same level. In the first lesson, the students first used the software and were taught how to use it. They were separated into six groups and attended the same online courses which were taught by the same teacher. There were five students in each course. Students can see their classmates’ and the teacher’s videos and talk each other synchronically via the software. The teacher taught the courses through electronic white board, video conferencing, and instant messaging functions of the software. In addition, the students attended a pre-test of English grammar before the online courses and a post-test after the ten-week courses. The multiple choice questions in the pre-test and posttest papers are developed by two English teachers. The questions and items in two test papers are very similar and evaluate the same English grammar knowledge. Moreover, they needed to fill out the questionnaire of this study after finishing the courses. Instrument development The questionnaire contained 19 self-reported items related to eight research constructs. In order to ensure content validity, items selected for the constructs were largely adapted from prior research. All the questionnaire items used a five-point Likert-type scale, ranging from (1) strongly disagree to (5) strongly agree. The scale items for learning self-efficacy were taken from Joo et al. (2000) with the wording slightly modified to fit the requirements of this study. Items for personal outcome expectations and affect were adapted from Compeau and Higgins (1999). Furthermore, normative beliefs and subjective norms were measured using the instruments of Venkatesh and Brown (2001) and Venkatesh and Morris (2000) respectively with modified wording to suit the context of videoconferencing. Perceived human-human Interactivity and human-system Interactivity were measured by six items developed from the study of Lu et al. (2010). The items to measure continuance intention to attend videoconferencing course were developed from past research (Hsu and Lin, 2008). Finally, learning effectiveness was measured by the difference in scores between the pre-test and post-test. A pre-test were undertaken to validate the instrument. The pre-test involved four respondents who were experts in the field of e-learning. Respondents were asked to comment on the length of the instrument, the format, and wording of the scales. Eventually, comments from them led to a few minor modifications of the wording and the item sequence. Data analysis Partial least squares (PLS) analysis is used to conduct the proposed model and hypothesis testing. As in structural equation modeling (SEM), the PLS approach allows researchers to assess measurement model parameters and structural path coefficients simultaneously. Different from covariance-based SEM, PLS focuses on maximizing the variance of the dependent variables explained by the independent ones in place of reproducing the empirical covariance matrix (Haenlein & Kaplan, 2004). Additionally, PLS makes minimal demands in terms of sample size to validate a model compared to SEM. In our model, because all items are viewed as effects, not causes, of latent variables, they are modeled as reflective indicators. The sample size of PLS requires ten times the largest number of independent variables impacting a dependent variable or the largest number of formative indicators (Chin, 1998). In this study, the largest number of independent variables estimated for a dependent variable is three. Therefore, our sample size of 30 meets the requirement for the PLS estimation procedures. Construct validity Construct validity, including convergent and discriminant validity, is widely used to validate research constructs. Convergent validity can be assessed by examining composite reliability and average variance extracted from the measures (Hair, Anderson, Tatham, & Black, 1998). Many studies have used 0.7 as threshold reliability of the measures. Table 1 shows that all measures displayed high factor loadings, which are greater than 0.7, on their respective constructs. As shown in Table III, our composite reliability values range from 0.920 to 0.991, exceeding the threshold of 0.7 (Nunnally & Bernstein, 1994). Table 2 also shows that the average variances extracted from the constructs ranged from between 0.8 to 0.986, which are above the acceptability value 0.5 (Fornell & Larcker, 1981). Hence, the results demonstrate that the measurement items were reasonably convergent on their respective constructs. Copyright © The Turkish Online Journal of Educational Technology 159
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 As recommended by Fornell and Larcker (1981), the result in Table 3 confirms the discriminant validity: the square root of the average variance extracted for each construct exceeded the squared correlation between any pair of distinct constructs. The smallest square root of average variance extracted from the constructs (0.894) is apparently greater than the largest correlation between constructs (0.759). Consequently, the measurement model demonstrated adequate reliability, convergent validity and discriminant validity. Table 1. Descriptive Statistics Construct/ Indicators Affect (AFF) Mean Std. Dev Loading AFF1 3.90 0.80 0.92 AFF2 3.77 0.77 0.94 Perceived Human-Human Interactivity (HHI) HHI1 4.00 0.79 0.80 HHI2 4.13 0.73 0.96 HHI3 4.10 0.71 0.97 Perceived Human-System Interactivity (HSI) HSI1 3.90 0.89 0.82 HSI2 3.70 0.84 0.95 HSI3 3.90 0.80 0.91 Continuance Intention (INT) INT1 3.70 0.65 0.97 INT2 3.80 0.76 0.96 Normative Belief (NB) Norm1 3.77 0.68 0.95 Norm2 3.63 0.72 0.97 Norm3 3.60 0.72 0.96 Performance outcome expectation (POE) POE1 3.63 0.77 0.99 POE2 3.60 0.77 0.99 Learning Self-efficacy (LS) LS1 3.13 0.97 0.94 LS2 2.90 1.13 0.91 Subjective Norms (SN) SN1 3.57 0.68 0.99 SN2 3.60 0.68 0.99 Learning Effectiveness LE 7.50 6.04 1.00 Table 2. Analysis of convergent validity Construct Cronbach’s Alpha Composite Reliability Average Variance Extracted AFF 0.850 0.930 0.868 HHI 0.908 0.939 0.837 HIS 0.879 0.923 0.800 INT 0.918 0.961 0.924 POE 0.986 0.993 0.986 SE 0.827 0.920 0.852 SN 0.982 0.991 0.982 NB 0.956 0.973 0.919 Table 3. Analysis of discriminant validity AFF HHI HIS INT POE SE SN NB AFF 0.932 HHI 0.433 0.915 HIS 0.567 0.282 0.894 INT 0.525 0.465 0.027 0.961 POE 0.403 0.347 0.260 0.542 0.993 SE 0.081 0.223 -0.135 0.342 0.376 0.923 SN 0.512 0.485 -0.073 0.755 0.554 0.362 0.991 NB 0.552 0.508 0.301 0.537 0.534 0.216 0.759 0.959 Copyright © The Turkish Online Journal of Educational Technology 160
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