Development of a Web-based System to Support Self-Directed ...

Learning effectiveness was analyzed by giving the students exercises to work on in the lab setting. Table 4 shows the
mean scores for technical skills, which included “operation of machines,” “selection of process parameters,” and
“process planning,” before and after attending the microfabrication course with the implementation of the interactive
web-based environments. The Experimental group (i.e., blended learning environment) achieved significant
improvements in its scores for “operation of machines” (t = -5.046, p = 0.000), “selection of process parameters” (t =
-3.613, p=0.001), and “process planning” (t = -3.778, p = 0.000), whereas the Control group (i.e., the conventional
learning environment) has shown no significant improvements (see Table 4).
In order to obtain more insight on the above quantitative findings, the 2-way ANOVA was used to analyze the
students’ technical skills, which included “operation of machines,” “selection of process parameters,” and “process
planning.” In the aspect of operation of machines, both the group effect and the pretest-posttest design have delivered
findings that have been found to be statistically significant, as shown in Table 5, which indicates that a blended
learning environment is instrumental in producing improved learning outcomes. Table 6 shows that the group effect
has not been drastic (F = 1.147, p = .288,), while the pretest-posttest design generated noticeable effects (F = 8.936,
p = .004), suggesting that in general, the students’ test scores have improved. However, the effects of a blended
learning environment have not been found to be as drastic in the learning of the selection of process parameters, and
this might have been associated with the technical obstacles encountered with the applied technique of virtual reality
that have mainly concentrated on the simulations of operations of object, aside from the sequences of these
operations.
Table 5. Analysis of variance for operation of machines
Source SS df MS F p
Group 5.601 1 5.601 4.481* .038
Pre-Post-test 12.721 1 12.721 22.231** .000
Interaction
1.868 1 1.868 3.264 .075
Pre -post-test
error
subject 91.239 73 1.250
residual 41.772 73 .572
total 149
p < .05, p < .01
Table 6. Analysis of variance for selection of process parameters
Source SS df MS F p
Group 1.138 1 1.138 1.147 .288
Pre-Post-test 8.604 1 8.604 8.936** .004
Interaction
3.004 1 3.004 3.120 .082
Pre-Post-test
Error
subject 72.422 73 .992
residual 70.289 73 .963
Total 149
**p < .01
The lack in real-time presentations of the produced scientific results pose great hindrance in keeping students from
adjusting the process parameters effectively for that they were unable to distinguish the varying machining outcomes
produced by the different process parameters utilized. However, such technical issues may be greatly mitigated with
the software and hardware technologies becoming more advanced in the future.
In the aspect of the operation of machines, the analysis has illustrated that interactions have been taking place
between the group effect and the pretest-posttest design (Table 7; F = 6.095, p = .016, interaction is significant P),
which thus requires the analysis be scrutinized in a more precise manner. Nonetheless, the simple main effect of the
group factor has demonstrated that the pretest scores produced by the Control group shares virtually no differences
from that that was produced by the Experimental group, while the posttest scores suggest a tremendous amount of
differences between the two groups (Table 8). Moreover, the simple main effect of the pre-post-test factor suggests
that the conventional teaching techniques served little purposes in improving test scores amongst students in the
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