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TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 development, there are more and more digital devices using in the processing of learning, some research focus on Ubiquitous learning, which students can learn at every time and everywhere (Huang, Chen & Wang, 2012). Besides Ubiquitous learning, it is also important about the learning material and theory itself. The learning style theory (Kolb, 1985) has been cited widely in academic researches (Demirkan, & Demirbas, 2008; Wang et al., 2006). Kolb (1985) constructed the learning process into two perspectives and four directions. One is apprehension perspective which includes the directions of concrete experience (CE) and abstract conceptualization (AC) according to the consideration of experience acquisition. The other is transformation perspective which includes directions of reflective observation (RO) and active experimentation (AE) according to experience transformation (Kolb & Kolb, 2005a). In light of CE-AC and RO-AE, learning style is classified into four quadrants which are accommodator, assimilator, converger and diverger (Kolb, 1985). Please refer to Kolb’s learning style quadrant shown in Figure 1. Active Experimentation AE Concrete Experience CE Accommodator Diverger Converger Assimilator Abstract Conceptualize AC Figure 1. Kolb’s learning style quadrant Chou and Wong (2000) noted that there was significant interaction between e-learning approaches and learning styles. Federico (2000) found that there was significant correlation between learning style and e-learning performance. Kraus et al. (2001) also found that e-learning performance was enhanced once the curriculum of elearning was suitable for the needs of specific learning style. Terrell (2002) and Meyer (2003) argued that learning style has decisive influence on e-learning performance. Papanikolaou et al. (2006) argued that learning style should be considered in the design of e-learning platform in order to promote learning motivation and performance. In summary, learning style is an important learner characteristic to be considered in e-learning. Akdemir & Koszalka (2008) stated learning styles play critical roles that influence student retention and success in web-based learning environment. Besides all, Ku & Chang (2011) discussed the recent studies about learning style, and support the view that learning style is an ongoing issue of great importance to educational research obviously. Self-efficacy Self-efficacy is the belief in success (Bandura, 1986). Such belief is generated by the self assessment on the ability to accomplish specific task. Therefore, self-efficacy represents the confidence level in accomplishing specific task successfully. People are more capable of achieving specific goal continuously while they have high self-efficacy. Jerusalem & Schwarzer (1992) argued that self-efficacy is the self-conscious control ability to adapt to pressure while face problems. Therefore, self-efficacy is a resource of pressure adaptation. People with high self-efficacy have better self-conscious control ability, which results in controlling challenging environment effectively (Gecas, 1989; Greenglass et al., 1999; Kear, 2000). Usually people with higher self-efficacy are more possible to carry out challenging tasks. Some research apply digital game to study, the motivational materials be proved enabling the application and maximize (Moon, Jahng & Kim, 2011). Furthermore, they are more capable of recovering from frustration in order to carry out tasks successfully instead of giving up at halfway (Bandura, 1992, 1997; Jerusalem & Schwarzer, 1992; Kear, 2000; Scholz et al., 2002). Lent (1984) noted that students with high self-efficacy achieve better learning performance. Hutchins (2004) noted that self-efficacy is the key factor for learners to acquire and sustain skills continuously. In autonomic elearning context, self-efficacy plays important role in learning performance promotion (Hsu, 2007; Thompson & Lynch, 2003). In summary, self-efficacy is an important learner characteristic to be considered in e-learning. Copyright © The Turkish Online Journal of Educational Technology Reflective Observation RO 211
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 Learning Efficiency The traditional assessment in education primarily deals with learning performance which presents a learner’s achievement measured by the test score on task. Learning performance is one assessment dimension of cognitive load. Higher cognitive load often results in lower test score and less learning performance (Pass & van Merriëboer, 1994). For many practical cases, it is feasible for two people to achieve the same learning performance levels with devoting different effort levels. Hence, both people have identical learning performance but expertise might be higher for the person who performs the task with less effort than for the person who devotes substantial effort. Therefore, an appropriate diagnostic technique of expertise should include assessments of effort and performance. Kalyuga and Sweller (2005) developed a dynamic diagnostic technique named cognitive efficiency (E) which is defined as E=P/R, where R is the effort rating and P is the performance rating on the same task. But cognitive efficiency is not a real-time based technique because the effort is not able to be assessed at real-time base. Therefore, Hsu et al. (2009) developed learning efficiency which is a learning progress diagnosis technique. It is defined as learning performance divided by learning effort equals to learning efficiency. Consequently, the learning effort of a leaner is able to be assessed and quantitatively measured by self selecting learning pathes at dynamic real-time based approach. In Hsu and Chang’s research (2011), the learning effort is represented as a visualized learning effort curve. By comparing the learning effort curve modes generated by the high learning efficiency and low learning efficiency groups in e-learning process, the progress of learning effort tends to descend for the high learning efficiency group. In contrast, the progress of learning effort tends to ascend for the low learning efficiency group (Hsu & Chang, 2011). Such finding is in accordance with the arguments of cognition load theory that lower effort results in higher performance (Kalyuga et al., 2000; Mousavi et al., 1995; Sweller et al., 1998). METHOD Subjects 178 university students from Taiwan participated the e-learning activity on IC3 Mentor e-learning platform. 125 of 178 were qualified as the subjects based on the readiness of learning records and the assessment results of learning style inventory and self-efficacy scale. Tool The research tool includes learning style inventory, self-efficacy scale, IC3 Mentor e-learning platform, rapid assessment quantification technique and learning effort quantification technique. Subjects were classified according to learner characteristics by applying the assessments of learning style inventory and self-efficacy scale. The learning records of each individual subject generated in the e-learning process on IC3 Mentor were converted into learning effort and learning efficiency that were presented in numerical data format by applying rapid assessment quantification technique and learning effort quantification technique. 1. Learning Style Inventory (LSI) The learning style inventory developed by Kolb (1984) had been examined by Cronbach α coefficient test with the results of .82, .71, .83 and .78 for accommodator, assimilator, converger and diverger accordingly (Kolb, 1985). Many scholars also claimed that LSI is an effective research tool with high reliability (Commings & Wirley, 2001; Demirbas & Demirkan, 2007; Wells et al., 1991). The validity of LSI is also very high (Sewall, 1986). It takes about 10 to 15 minutes to answer 12 questions in LSI; therefore, LSI does not cause too much loading on subjects. In this study, LSI is used to assess subjects’ learning style and classify subjects into four learning styles which are accommodator, assimilator, converger and diverger accordingly. 2. Self-Efficacy Scale (SES) The self-efficacy scale developed by Zhang and Schwarzer (1995) had been applied on 293 university students and the Cronbach α coefficient test with the result .91 indicated that SES is an effective research tool with high reliability. And there is only a single component to be extracted by principle component analysis, which represents high validity (Scholz et al., 2002). There are 10 questions in SES and every question is assessed by the 4-point scale which is incorrect for 1, fairly correct for 2, mostly correct for 3 and fully correct for 4. Total score points will be around 10 to 40 points. Subjects with higher SES scores represent better self-efficacy. In this study, SES is used to assess self-efficacy and classify subjects into high and low self-efficacy groups. 3. IC3 Mentor E-Learning Platform IC3 Mentor is the e-learning platform of IC3 (Internet and Computing Core Certifications) which is a global ICT (Information and Communication Technology) certification applied over 128 countries worldwide (Certiport, 2008). IC3 Mentor is a learning/assessment blended learning system. Learners can self-determine the learning path in the multi-layer learning structure of IC3 Mentor. Copyright © The Turkish Online Journal of Educational Technology 212
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