Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

By Sun, Jerry Chih-Yuan; Kuo, Cian-Yu et al. | Educational Technology & Society, January 2017 | Go to article overview

Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game


Sun, Jerry Chih-Yuan, Kuo, Cian-Yu, Hou, Huei-Tse, Lin, Yu-Yan, Educational Technology & Society


Introduction

As of 2014, there were 2.4 billion Internet users globally (Internet World Stats, 2014). However, studies show that Internet users are often unaware of the dangers of the phishing attacks they are exposed to (Arachchilage & Love, 2013). As of 2013, Internet phishing through email scams had decreased by 16% from 2012 (Fossi et al., 2013); however, Internet scams using social sites had increased by 3% (Anti-Phishing Working Group, 2013). Social networking websites are gradually replacing emails as the new primary means for Internet scams (Fossi et al., 2013). Victims of Internet phishing include people of all age groups, among which young people constitute the most vulnerable group (Kumaraguru, Sheng, Acquisti, Cranor, & Hong, 2010). Statistics from Livingstone and Haddon (2009) showed that 60% of all social site users were between 9-16 years old, while more recent data from 2013 further showed that the number of young people who were social sites users increased by 29% in one year and was continuing to increase (Harper, 2014). This heavy use of social sites accentuates young people's vulnerability to phishing through these sites.

Tools for detecting phishing may not provide effective defenses (Kumaraguru et al., 2007a). Therefore, some academics deem anti-phishing education, in which users learn correct Internet phishing concepts and defense methods and are equipped with basic defense abilities, to be the most effective means of combating phishing (Kumaraguru et al., 2007b; Kumaraguru et al., 2010; Sheng, Holbrook, Kumaraguru, Cranor, & Downs, 2010; Sheng et al., 2007) as well as fostering good general Internet safety, operating habits, and awareness (Wirth, Rifon, LaRose, & Lewis, 2007). Defense against Internet phishing is a problem-solving process that requires judgment and reasoning abilities (Kumaraguru et al., 2007a). The advantage of game-based learning materials is the construction of life-like situations of phishing and game-based learning scenarios; therefore, game-based learning is an effective teaching method to help learners enhance their anti-phishing abilities (Kiili, 2005; Sheng et al., 2007). The game-based learning method affects learners' flow experience and problem solving abilities, and can promote effective learning behaviors; for example, simulation games help learners enter a flow state during the computational problem solving activities (Liu, Cheng, & Huang, 2011) and facilitate the flow experience when playing games as a group in an interactive social game environment (Inal & Cagiltay, 2007). There have been previous studies focusing on the use of anti-phishing games in teaching among university students (Yang, Tseng, Lee, Weng, & Chen, 2012), but there has been no study exploring the effective learning behavior of children or teenagers through anti-phishing educational games. Therefore, the present study, using young people as the research subjects and an anti-phishing game as the teaching method, sought to understand whether the flow experience in games can contribute to effective learning behaviors.

Mayer (1992) proposed that it is relatively difficult for teachers to understand learners' metacognitive processes such as learning strategies through summative assessment (e.g., learning achievement tests), and that it is therefore difficult to improve learners' learning states or enhance their learning achievement using these methods. Thus, it may be more effective to help educators understand and improve learners' learning states and strategies using quantified dynamic learning behavior, presented in the form of a time order (Liu et al., 2011), as well as applying sequential analysis to explore the correlation between learners' behaviors and to infer overall behavioral patterns (Bakeman & Gottman, 1997). Learning behavior patterns are beneficial in terms of identifying the reason for unsatisfactory learning states and learning achievement, which can in turn help teachers provide learners with suitable guidance for better learning outcomes (Hou, 2012b). …

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