April 2012 Volume 15 Number 2 - Educational Technology & Society

Uzunboylu et al. (2009) examined the integration of mobile technologies, data services, and multimedia messaging
systems to develop environmental awareness. The results showed the environmental awareness of students increased
significantly, and attitudes toward maintaining clean environments and preventing pollution improved. Most
importantly, in their conclusion they pointed out ML will likely provide a suitable platform for use in a wide range of
educational settings. Accordingly, to investigate the effect of different applications of ML in educational settings is
emerging as important issue.
Motivation and significance of the study
Every year, traffic accidents result in huge loss of life, assets and social cost. As the vehicle population continues to
grow within a limited space, traffic-jams and traffic violations have occurred and need to be solved. Specifically,
because most traffic incidents result from negligence or violations by road users, enhancing traffic education thus
becomes very important. Therefore, Iversen (2004) examined whether attitudes toward traffic safety issues are
predictors of future risky behavior in traffic. The results of research show a high correlation between the dimensions
of attitudes and behavior.
Traffic education can be defined as the process including all rules which the people must know to be protected in
traffic (Yilmaz & Celik, 2008). Generally, learners have more learning opportunities in educational environments.
However, traffic education has not received enough attention and has been conducted on an irregular basis in
schools. Besides, people usually understand what situations may cause a traffic violation problem; however, they are
apt to make mistake carelessly. From the reflection perspective, Dewey (1933) pointed out reflection could support
learners a persistent and careful thinking action during a problem solving process. In this study, traffic violation
reflection can be viewed as the improving process in which the learners must understand what situations may cause a
traffic violation problem, according to the traffic laws, and further propose possible ideas or solutions to avoid such
violation.
To the best of our knowledge, no studies have been conducted on a relevant teaching approach to enhance traffic
violation reflection based on the integration of the learning theory and mobile technology. Therefore, a better
understanding of how to use existing technologies and learning theory to improve traffic education with limited time
and resources, providing instructional resources/materials, and creating better traffic violation reflection, is worth
examining.
Theoretical foundations
Social constructivist theory
Social constructivist theory assumes learners act and reflect within an environment and specifically emphasizes the
importance of culture and context in understanding what occurs in society and constructing knowledge based on
learners’ understandings (Derry, 1999). The learning mode is constructivist in approach, whereby the learners
actively participate in their own learning process and construct their own knowledge. Moreover, Vygotsky (1978)
believed an individual’s knowledge construction only occurs in a social and cultural environment, where one has to
interact consistently with others to construct one’s knowledge.
Constructivist learning environments also demand a meaningful and authentic context for social and collaborative
activities (Neo & Neo, 2009) and learners play a more active role as learners as they contribute to developing their
own knowledge. Within such constructivist framework, learning takes place as learners progressively differentiate
concepts into more complex understandings and also reconciles abstract understanding with concepts acquired from
experience (Kwon & Cifuentes, 2009). Wen et al. (2004) found good online constructivist learning environments
challenged learners’ existing concepts when they had to use inquiry learning and reflective thinking.
Therefore, the present study utilized social constructivist theory as a learning strategy to promote peer interactions
for peer groups in an ML environment. Learners can pose questions and reflection related to traffic problems,
collaborate with peers, learn new knowledge, and then improve their traffic violation reflection.
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