Chun-Hsin Chang & Chi-Ruei Tsai QR Codes in Vehicle Course 119
Journal of Research in Education Sciences 2019, 64(3), 119-141
doi:10.6209/JORIES.201909_64(3).0005
Comparing the Learning Progress of Creating
and Using QR Codes in a Vehicle
Maintenance Course
Chun-Hsin Chang
Chi-Ruei Tsai
Department of Industrial Education and Undergraduate Program of Vehicle and Energy Engineering,
National Taiwan Normal University
Institute for Research Excellence in Learning Sciences, National Taiwan Normal University
Department of Education and Human Potentials Development, National Dong Hwa University
Abstract
To understand how QR code technology can be integrated into an engineering course and affects learning outcomes in a vehicle maintenance course, this study divided students into two groups: one that created QR-code content (hereafter “creating-QR code”) and another that used QR-code content (hereafter “using-QR code”). This study then examined which approach was more beneficial for learning outcomes. The students were required to either search for or scan information and would thus exhibit two types of epistemic curiosity (EC), namely “deprivation-type EC” and “interest-type EC,” in relation to information seeking. Teachers administered pretests to identify participants’ abilities, and students were segmented into either a high- or low-level ability group. Results revealed that with respect to learning progress, the creating-QR code group outperformed the using-QR code group in learning about vehicle maintenance. In addition, comparison of the results of the two types of EC indicated that students in the creating-QR code group employed a higher degree of “deprivation-type EC” than those in the using-QR code group. However, there were no differences between the two groups with regard to “interest-type EC.” These results suggest that as one form of flipped learning, teachers can assign students the task of creating QR codes. This can enable knowledge consolidation and improve student learning outcomes in a vehicle maintenance course.
Keywords: e-learning, epistemic curiosity, learning outcomes, QR code, vehicle maintenance
Corresponding Author: Chi-Ruei Tsai, E-mail: bass2143@gmail.com
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Introduction
The rise of mobility and marginalization of information communication technology (ICT) has cultivated a “move-and-do” culture (Disterer & Kleiner, 2012). Also, previous study trying hard to propose a feasible model of formal education (i.e. Chan, Liao, Cheng, Chang, & Chien, 2017). Currently, an increasing number of learners keep their mobile devices on hand for both learning and communication (Song, 2014). One way in which users can access data quickly is through the use of mobile devices with quick response (QR) codes installed. Using a QR code requires a mobile device such as a smartphone, iPod, netbook or similar devices with a camera, an installed QR code reader application and Internet access via wireless communication. This system offers a wide variety of advantages for “ubiquitous learning” (i.e. learning anywhere and at any time) due to positive user experiences with QR code applications’ “look and feel” feature (Hung, Yang, Fang, Hwang, & Chen, 2014). This study aims to provide an understanding of how creating or using QR codes as a learning tool can support students’ learning.
The emergence of QR code technology has been used to popularize and complement formal ubiquitous learning, which is recognized as having a high potential to motivate learners and to improve their learning performance (Chen, Kinshuk, Wei, & Liu, 2011; Deng & Yuen, 2012; Hung et al., 2014; Hwang & Chang, 2011; Quinton & Smallbone, 2010; Tan, Tan, & Wettasinghe, 2011). Knowledge regarding the effects of QR code applications is generally limited to using QR codes in dealing with distributed schemas, processes, platforms and services. Only a few studies have investigated learning outcomes resulting from creating QR codes. Moreover, there are previous study revealed that engaged those students posing questions or construct the knowledge by themselves might raise the familiarity in learning materials, problem-solving skills, metacognitive awareness (Akben, 2018). The further study revealed that the students who conduct self-metacognitive questioning strategy in physics learning would gain a better learning attitude (Dökme & Koyunlu Ünlü, 2019). The empirical study showed that students’ collaborative problem-solving performance in human-computer agent was significantly greater than that in human-human interactions when they were solving daily problems simulations (Chien, 2019). Chien’s study examine the differences of collaborative interactions, but not about the learning materials. Therefore, it is interesting that to compare two groups of constructing learning materials by themselves or directly acquire new information through QR codes. As such, this research focused on a vehicle maintenance course and compared the learning effects in the creating-QR code group
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and the using-QR code group.
The individuals who motivate by curiosity tend to devote themselves to situations, which may facilitate them to learn, acquire new skills, or improve their soft skills (Kashdan et al., 2018). Thus, the researchers aimed to examine students’ domain knowledge acquisition and to measure related factors, such as students’ epistemic curiosity (EC) and learning progress. According to Litman and Jimerson (2004), there are two types of EC. As the two types of EC are quite different mental processes, their relative degrees of activation often differ in strength, which explains why it is possible to enjoy or desire information seeking (Litman, 2010). Thus, when students create or use QR codes to learn, the two types of EC will be activated differently and this difference is explored in this research.
Theoretical Background Related to Creating and Using QR Codes
Research has found that students are driven by two different types of stimuli: goal-oriented stimuli and experience-oriented stimuli (Sansone, Fraughton, Zachary, Butner, & Heiner, 2011). Accordingly, we designed two types of QR code learning methods for a course pertaining to vehicle engine maintenance. The first learning method corresponded to experience-oriented stimuli and required students to produce QR codes with respect to the course topics. The second corresponded to goal-oriented stimuli and required students to use QR codes to acquire knowledge about vehicle maintenance. To shift from a didactic orientation method to an inquiry orientation one, this study proposed a teaching method that required students to produce learning materials embedded in QR codes.
Additionally, creating QR code contents may involve the use of various cognitive functions, such as memory and thinking with respect to what the QR codes are and how they operate (e.g., Fabricius & Schwanenflugel, 1994; Nelson, Kruglanski, & Jost, 1998). Creating a QR code also requires understanding the criteria for the validity of the knowledge acquired, which is known as epistemic cognition (Kitchener, 1983). Extending from epistemic cognition, Loewenstein (1994) interpreted curiosity as a drive, while Litman (2008) and Litman and Spielberger (2003) defined EC as the desire that motivates an individual to acquire new knowledge, to close information gaps, and to track intellectual problems. Thus, increasing students’ EC may increase their domain knowledge.
In the creating-QR code group, students were required to produce several YouTube videos that demonstrated their vehicle maintenance-related knowledge. To build the QR codes, students had to search for and organize related knowledge into several units and embed QR codes with a website link. In contrast, students in the using-QR code group learned the course materials by scanning QR
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codes to access detailed course contents. The quality of QR codes produced by the students may relate to their EC in terms of searching for or organizing the related knowledge into the QR codes. On the other hand, the EC of students in the using-QR code group may be less affected. This study therefore examined the level of EC in relation to learning outcomes associated with creating and using QR codes.
The Application of QR Codes in Learning
In 1994, a Japanese company called Denso-Wave invented the QR codes. QR is an abbreviation for “quick response,” four small square patterns are located in the four corners of a QR code. The QR code patterns help orient decoding software, and regardless of the angle used to scan, the information will be read correctly. The information contained in a QR code may be in different formats, such as a text, a URL or other data (Shin, Jung, & Chang, 2012). The application of a QR code in public affairs can improve the efficiency and accuracy of verification processes. With respect to education, QR codes can broaden learning environments and help learners instantly obtain information and thus further increase the ubiquity of mobile learning. If the production of QR codes can be integrated into the design of cross-platform learning, then, when combined with the use of mobile phones and tablets, the QR code is a tool that can allow students to effectively and immediately obtain information. Although QR codes are regarded as transitional technology, they can be used as a learning tool. QR codes can be used to provide the 21st century skills, including lifelong learning, in both under-graduate education and post-graduate education (O’Flaherty & Phillips, 2015). According to Shin et al. (2012), despite of concerns over users’ experiences and the interface, only a small number of industry reports have addressed usability issues associated with QR code viability. Additionally, previous research has not focused on the QR code design as a flipped learning approach. Thus, the present study hypothesized that through the use of new technology involving smartphones with cameras that can make use of the QR code reading software, students can exhibit greater learning interest or effectiveness.
EC
Litman, Robert, and Spielberger (2005) reported that curiosity reflected an intrinsic desire for acquiring new information to stimulate interest and/or to remove uncertainty and that curiosity was aroused by novel, complex, or ambiguous stimuli to motivate an exploratory behavior. Litman and Jimerson (2004) distinguished I-type EC as the interest type by adding new ideas and concepts to one’s repertoire with an expected pleasure of new discoveries; and therefore I-type is linked to acquiring new knowledge for the intrinsic enjoyment of the act itself (i.e., mastery-oriented learning).
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In contrast, D-type EC is identified with reducing uncertainty and eliminating undesirable states of ignorance and is conceptualized as the “need to know,” for which the correctness, accuracy, and relevance of the desired information about a specific unknown phenomenon are of utmost importance (i.e., performance-oriented learning) (Schneider, von Krogh, & Jäger, 2013). In coping with the information-seeking framework for creating or using QR codes for learning, the two types of EC were examined: I-type EC, where learners acquire new information by browsing, in absence of a specific need, and therefore directly related to the pleasure of new discoveries (Litman & Jimerson, 2004; Litman & Spielberger, 2003) and D-type EC, where learners acquire new information in pursuit of organizing the information into specific, objectively correct and relevant knowledge in order to resolve uncertainty, the elimination of which can be seen as rewarding, and therefore pleasurable (Litman et al., 2005). Based on this elaboration, D-type EC has a stronger link to learning activities that require the construction of an explicit description and explanation. For the purpose of this research, D-type EC would seem to be strongly related to creating QR codes for the vehicle maintenance course.
Baron and Kenny (1986) introduced personal traits and situational factors as moderating variables that affect learning outcomes. Several studies have explored characteristics of users in online communities, and distinguished them as cognitive and thinking styles (Kao, Lei, & Sun, 2008; Kaynar & Amichai-Hamburger, 2008; Matrai, Kosztyan, & Sik-Lanyi, 2008), which are linked to knowledge search behaviors. However, these studies are circumstantial and independent in nature (Koo & Choi, 2010) and lack an integrated understanding of user knowledge search behaviors pertaining to the construction of QR codes. To close the gaps in the literature, this study focused on the types of EC related to creating or using QR codes and the effects that QR codes have on student learning.
Learning Progress
Research has found that e-learning systems can significantly improve students’ learning outcomes and learning motivation. Learning progress is used as an indicator of learning outcomes, and it is also the most important factor in the assessment of learning effectiveness. In addition, Piccoli, Ahmad, and Ives (2001) defined learning progress as changes in learners’ cognitions, emotions, and skills. At present, achievement tests are the most frequently used criteria for evaluating students’ learning progress in using QR codes or creating QR codes. Thus, this study adopted pre- and post-tests to evaluate learning outcomes of the two groups.
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Research Hypotheses
Learning outcomes are influenced by various factors, including learners’ individual abilities, learning types, course contents, and the teaching and learning atmosphere. As previously introduced, Litman (2008) defined EC as an individual’s motivation for learning new ideas, eliminating message differences, and satisfying the desire for knowledge-related problems. Therefore, Schneider et al. (2013) elaborated that I-type EC and D-type EC theoretically correspond to different learning goals and the concept of I-type EC suggests that attaining knowledge is related to inner satisfaction (exploratory learning), while D-type EC is conceptualized as the “need to know.” Due to the flipped learning nature of creating QR codes, students are required to search for related knowledge to construct the course materials; in this sense, EC may affect students’ learning performance. Thus, the following research hypotheses were proposed:
H1: There is a significant difference between using and creating QR codes in relation to I-type EC.
H2: There is a significant difference between using and creating QR codes in relation to D-type EC.
In this study, learning progress relevant to cognitive achievement was evaluated by comparing appraisals of the pre- and post-tests. According to cognitive approach of understanding, performance in information-processing components can be influenced by individuals’ mental abilities and learning achievements (Carroll, 1993; Sternberg, 1982); creating and using QR codes for learning are activities whereby students have to process information components, and this study compared the students’ pre- and post-test abilities. Thus the following hypothesis was proposed:
H3: There is a significant difference between low- and high-ability groups in relation to learning progress.
Methods
Using new technology to acquire knowledge is a requirement for students. In this research, the teachers decided on the topics and the students devised the course units based on reference books and vehicle repair handbooks.
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Procedures
An experimental design was employed in this study. Vocational students in the second grade majored in auto-repair technology were involved. They were divided into two groups: one with creating-QR codes and the other with using-QR codes. In the creating-QR code group, they embedded information for three units of a vehicle maintenance course into QR codes. The learning contents of this study included: (1) the process for changing engine oil; (2) the process for changing brake fluid; and (3) the process for replacing ignition parts. All the participants in both groups went through the learning process for three hours per week for a total of six weeks.
The pre-tests were administered before the students began creating the learning content to ascertain the students’ abilities before beginning the experimental process. After the pre-test, the students in creating-QR codes group collected information and discussed how to organize the QR code contents on their own. The students were given three hours per week for six weeks to organize the contents. The teacher, as a facilitator, joined the participants’ discussion, provided official handbooks, and offered practical experiences. In brief, it is important for the teacher to make sure that the contents met the learning objectives, and the information is adequate as the control group need to know. In the end of the content organization stage, the teachers invited other teachers and experts to fill the table of specification to ensure the validation of the learning contents. They kept rolling this until results reached an agreement on the inter-rating of face validity. After completing the three units, this group completed a post-test regarding the learning materials when they created QR codes. However, the using-QR codes groups directly learned through the content that made by creating-QR codes group. They also had three hours per week for a total of six weeks to understand the learning materials. To compare the learning progress of the creating-QR code group and those of the using-QR code group, this study explored students’ EC for learning in a vehicle maintenance course.
Learning Content Segments
The experimental teaching focused on a vehicle maintenance course. The QR codes were linked to a web database where students clicked to read about the details of vehicle maintenance that were created by the students in the creating-QR code group. The students in the creating-QR code group also produced instructional video clips for the three units. An example of the course contents is shown in Figure 1, and an example of an instructional video clip made by students is shown in Figure 2.
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Figure 1. An Example of the QR Code Course Content
Figure 2. An Example of an Instructional Video Showing How to Gauge the Plug Made by Students in the Creating-QR Code Group
Participants
The totals of 112 vocational high school students in the second grade from Taipei were participating in the present study. However, there was one class that consists of 36 students who could not finish the experiment because of the swine flu spreads in schools, and four participants failed to complete the experiments due to personal issue (e.g. absent, personal leave, want to quit the program). Therefore, there were 72 valid samples in this study. The high performance group was defined with top 33% of all the participants that whose scores was above 75, and the low
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performance group was defined with bottom 33% of all the participants that whose scores was below 64. Due to the natural distribution of the classes, all the participants of this study are all male students.
Measurement
A set of questions asked the participants to rate their perceptions related to EC after creating-QR codes or using-QR codes to learn the course materials. This study adapted previous theories or research to the questionnaire items and used the forward-backward method to translate the original materials into Chinese, which ensured “face validity” for the verification on the accuracy and clarity of a translation. This research study utilized a five-point Likert scale to compute scores; on this scale, “1” meant “strongly disagree” and “5” meant “strongly agree.”
This study adopted Litman’s (2008) and Litman and Spielberger’s (2003) two-dimensional cognitive EC, namely, I-type EC and D-type EC for reference. Some diction was revised such that the students under evaluation could conform to the design of the research survey. All psychometric properties of the subscales identified in this instrument were measured by confirmatory- factor-analysis as reported in the Results section. The learning progress in the present study is measured by pre- and post-test. The test items were adapted or modified from the certification examination of vehicle repair and maintenance in ten years. To ensure the quality of all the items, the ideal difficulty level is 74 (Lord, 1952), and the ideal discrimination index should be exceed 0.4 (Ebel & Frisbie, 1986). However, there were 56 items remain, and were narrow down by focus group to 40 items. The items were reviewed by experts and teachers that all the items were all consisted to the learning materials of both groups.
Data Analyses
The data were analyzed in two steps. This study used confirmatory factor analysis to test the reliability and validity of the questionnaire. Results of pre- and post-tests were used for exploring the learning progress of the two groups.
Reliability and Validity Analyses
Originally the questionnaires consisted of seven items for I-type EC construct and seven items for D-type EC construct. After conducting the first-order confirmatory factor analysis introduced by Kline (2010), items with residual values greater than .5 were removed. The final questionnaire retained the remaining items, which contained six items related to I-type EC and four related to
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D-type EC. Next, all the reliability and validity of the questionnaires were analyzed as described below:
First, the reliability was evaluated based on internal consistency and composite reliability. To evaluate the internal consistency of the variables, the reliability of the questionnaire was assessed using Cronbach’s α. Cronbach’s α value greater than .6 indicates an acceptable level of reliability, according to Hancock and Mueller (2013). Table 1 showed that the Cronbach’s α values ranged from .71 to .85, indicating reliable variables, according to Hair, Black, Babin, and Anderson (2009). Moreover, the composite reliability (CR) of the constructs ranged from .83 to .89, which exceeded the threshold value of .7 as suggested by Hair et al.. Table 1 also showed that the standard deviations were small (i.e., they were all below 1.00), which indicated a low degree of dispersion, according to Hair et al..
Table 1
Reliability and Validity Analyses
Items M SD FL t-value
I-type EC: α = .85, CR = .89, AVE = .67
1. While using or creating QR codes, I learned something new and wanted to find out more
3.65 .80 .64 16.19 2. While using or creating QR codes, I enjoyed exploring new ideas 3.51 .81 .78 19.48 3. While using or creating QR codes, I found it fascinating to learn
new information
3.69 .84 .71 19.14
4. While using or creating QR codes, I enjoyed discussing the related abstract concepts
3.31 .80 .85 14.67 5. While using or creating QR codes, I enjoyed learning about
subjects which are not familiar to me
3.44 .79 .79 12.44
6. While using or creating QR codes, I searched for more information than required
3.34 .77 .74 18.14 D-type EC: α = .71, CR = .83, AVE = .65
1. While using or creating QR codes, if I found a concept hard to understand, I would seek for clarification before taking a break
3.11 .73 .86 12.78 2. While using or creating QR codes, I encountered complex
concepts and tended to be in deep thought until the concepts made sense to me
3.31 .72 .87 18.20
3. While using or creating QR codes, I used my brain more than when I used textbooks
3.15 .60 .65 13.48
4. While using or creating QR codes, I was interested to discover how things worked
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Second, the discriminative power of the scale in this study was determined in accordance with its ability to discriminate between the instrument items, and the scale was examined via independent-sample t-tests for explaining the discriminative power of each item. Critical ratios (t-values) greater than 3 were assumed to indicate significant discriminative power. As shown in Table 1, all of the t-values were significant (between 12.38 and 24.69, p < .001), which according to Green and Salkind (2004) indicated that all items were discriminative and were able to identify the degree of the responses in the different samples.
Third, convergent validity refers to the degree to which one construct is measured by multiple items. In this study, the convergent validity was first evaluated, according to Hair et al. (2009), by verifying whether the average variance-extracted (AVE) values were greater than .6. As shown in Table 1, the AVE values ranged from .61 to .67, which indicated that the conditions being met, the convergence was acceptable, according to Hair et al.. The convergent validity was also evaluated by verifying that the factor loadings (FLs) of all of the items were significant and greater than .6. All of these conditions were met, which indicated acceptable convergent validity, according to Hair et al.. As shown in Table 1, the FLs were statistically significant and ranged from .64 to .87. All of these conditions were met which indicated acceptable convergent validity. A summary of the discriminative and convergent validity analyses revealed that all of the required conditions were met, indicating that this kind of construct validity was acceptable according to Hair et al..
Learning Progress of High and Low Ability Groups
The learning outcome measurement included contents related to changing engine oil, gearbox oil and brake fluid oil, as well as contents related to the fuel and ignition systems. Each unit had 25 test items for a total of 75 questions as a pre-test, and the post-test used the same test items to measure the learning outcomes. In this section, we wanted to realize whether students’ learning progress differed in terms of pre- and post-test without comparing the using-QR code and the creating-QR code approaches. Then paired-sample t-test analysis was applied to investigate the differences between the pre- and post-tests. Table 2 showed that the mean values were 61.29 and 78.07, respectively, (t = -20.51, p < .001), which indicated that the students’ learning progress improved.
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Table 2
Learning Progresses of Two Groups (N = 72)
M SD SE t
Pre-test 61.29 6.85 0.80
Post-test 78.07 10.51 1.23 -20.51***
***p < .001.
Learning Progress of Creating-QR Code and
Using-QR Code
The independent-sample t-test was conducted to understand students’ learning outcomes in comparing the creating-QR code and using-QR code approaches. Table 3 showed that with respect to D-type EC, the effect of the different learning approaches on EC was significant (t = 2.40, p < .05), and the mean of creating-QR code group was more than that of using-QR code group. On the other hand, with respect to I-type EC, the effect of the different learning approaches on EC was not significant (t = .71, p > .05). Furthermore, this study applied paired-sample t-tests to investigate the differences between the two types of EC. The results showed there was a significant difference (t = 4.49, p < .001), and the mean of D-type EC was higher than that of I-type EC. In summary, the students in the creating-QR code group exhibited greater D-type EC than those in the using-QR code group. Overall, no matter whether in the using-QR code group or the creating-QR code group, all the students showed a greater level of D-type EC than I-type EC.
Table 3
Comparison of I-Type and D-Type EC Between Learning Approaches
Groups n M SD SE t Creating-QR Code 34 3.61 .43 .07 Using-QR Code 38 3.38 .36 .05 2.40*** Creating-QR Code 34 3.26 .41 .07 Using-QR Code 38 3.19 .37 .06 .71*** D-type EC 35 3.49 .41 .05 I-type EC 37 3.42 .39 .04 4.49*** *p < .05. ***p < .001.
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Learning Progress Between Ability Groups and
Learning Approaches
This section applied two-way ANOVA to investigate the relationship between the high- and low-ability groups and two learning approaches in relation to learning progress. The between-subjects’ effects are shown in Table 4. The interaction between the high- and low-ability groups and the different learning approaches were not statically significant (F(1,34) = .09, p > .05).
Table 4
Learning Progress Between Ability Groups and Learning Approaches
df Mean Square F
Corrected Model 3 252.60 7.12***
Intercept 1 12468.10 351.48***
Ability Groups 1 255.08 7.19***
Learning Approaches 1 501.78 14.15***
Ability Groups*Learning Approaches 1 3.29 .09***
Error 44 35.47
Total 48 *p < .05. **p < .01. ***p < .001.
Next, independent-sample t-tests were used to test the main effects on high- and low-ability groups and learning approaches. The collected data in the present study is 72, and therefore the amount of high and low group is 24. Table 5 showed a significant difference between high- and low-ability groups (t = -2.31, p < .05); and the learning progress of the low-ability group was superior to that of the high-ability group. Furthermore, there were significant differences in the learning progress of the creating-QR code group and the using-QR code group (t = 3.52, p < .01); and the learning progress of the creating-QR code group were shown to be superior compared to that of the using-QR code group. In summary, the learning progress of the low ability group was higher than that of the high ability group for both learning approaches, and the creating-QR code method was more effective than the using-QR code method.
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Table 5
The Independent-Sample t-Test for Ability Groups and Learning Approaches
Groups n M SD SE t High-ability 24 15.37 6.25 1.43 Ability Groups Low-ability 24 20.53 7.47 1.71 -2.31** Creating-QR Code 34 21.78 6.24 1.47 Learning
Approaches Using-QR Code 38 14.50 6.48 1.45 -3.52**
*p < .05. **p < .01.
Discussion
Sago (2011) states that mobile devices, such as smartphones can utilize QR codes to bridge the gaps between traditional methods of retrieving information and the digital realm. That is, using smartphones to access information embedded in QR codes can facilitate the “just in time” (JIT) learning paradigm (Jamu, Lowi-Jones, & Mitchell, 2016). For example, Brandenburg and Ellinger (2003) assert that JIT learning is now enhanced by advances in technology and can be used by providing ubiquitous learning opportunities which allow learners to access pertinent learning materials at the precise moment of need. Briefly, using QR codes as learning media can be beneficial to learners in integrating interactivities that motivate learners to learn domain specific knowledge (Shin, Jung, & Chang, 2012). A benefit of using QR code technology with mobile phones is JIT presentation of information for accomplishing a task as introduced by Kester, Kirschner, van Merriënboer, and Baumer (2001), and using QR codes has the potential to improve users’ learning outcomes (Liaw, Hatala, & Huang, 2010). However, only a few studies focused on the learning effects of creating-QR codes, and therefore an experimental study was performed to determine the ability of students in creating QR codes and using QR codes to learn; and we compared participants’ I-type EC, D-type EC and learning progress.
Hypothesis 1 examined the differences between using and creating QR codes in relation to I-type EC, and the results revealed this hypothesis was not supported. The need for cognition (NFC) is an individual difference that describes one’s intrinsic motivation to engage in and to enjoy effortful cognitive processes (Cacioppo & Petty, 1982). Individuals at the higher end of the NFC continuum view knowledge searching as fun; they are drawn to problems that require a great deal of thoughts and they tend to seriously consider issues (Gray, Chang, & Anderman, 2015). Based on the definition of I-type EC (Schneider et al., 2013), the way of creating QR codes is associated with the pleasure of discovering knowledge, and it emphasized the delight of discovery. By using QR codes
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to learn, students engaged in a new experience with the support of ICT that can further inspire students’ thirst for knowledge. On the other hand, students in the creating-QR code group experienced more pleasure while searching for information to create QR codes than students in the using-QR code group, who learned the course materials by using QR codes. But this assumption was not supported in this study.
Hypothesis 2 explored the differences between using and creating QR codes in relation to D-type EC, and the results indicated this hypothesis was supported. Behavioral performance is a mental effort; this means individuals may need a large amount of efforts in order to maintain a performance level. When the required behavior is considered to be too difficult, efforts will not be invested. On the other hand, the required behavior is easy to perform; efforts will be low or absent (Brouwer, Hogervorst, Holewijn, & van Erp, 2014). In some cases, additional efforts may be required in order to transform knowledge to control cognitive processes into more efficient processes (Liu & Wickens, 1994). Students with D-type EC, according to Litman and Mussel (2013) and Piotrowski, Litman, and Valkenburg (2014), are motivated to remove unpleasant feelings from being deprived of new knowledge and to put efforts in decreasing uncertainty and eliminating unknown conditions. This implies that D-type EC encompasses a desire to develop a deeper, more meaningful understanding of a subject (Richards, Litman, & Roberts, 2013). In line with this, the students making QR codes were assumed to have experienced decreasing uncertainty and eliminated unknown conditions in relation to the subject matter in order to produce high quality QR code contents. Thus, the students in the creating-QR code group had a higher perception of D-type EC than those in the using-QR code group. This result is supported by Mussel (2010), who highlighted that for obtaining and using new knowledge, D-type EC and learning outcomes showed strong convergence with one another.
Hypothesis 3 proposed to explore the differences in learning progress between both low and high-ability groups, and the results revealed that students in the low-ability group could be facilitated to learn more through both using-QR codes and creating-QR codes. When striving to resolve problem situations, learners experience changes in their mental representations, whereby the learning contents are recognized, defined, and organized (Seel & Dijkstra, 2004). In addition, students get objective information which leads to desired effects, and they will also see if learning approaches can result in an improvement of achievement. Förster and Souvignier (2014) find that feedback of learning progress is the key motivator for students to continue putting efforts to get learning goals. Supported by this, the results of independent-sample t-tests revealed that the learning progress of students in the creating-QR code group was better than that of students in the using-QR code group,
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regardless whether the students belonged to the low-ability or high-ability group.
Conclusions
Scaffolding theory itself assists learners to complete complex learning tasks in cognitive psychology. The QR code technology presents a novel scaffolding media to the learning concept in which it is in charge of organizing the what-to-learn module. In essence, the QR code technology aims to reduce learning complexity in the learning processes. In conclusion, this study, taking into consideration of the ever-changing nature of social media, discovered key motivations by comparing the learning effects of creating-QR codes and using-QR codes and therefore held implications for an effective application of QR codes in teaching settings. Students in the experimental group (i.e., creating-QR code group) exhibited a greater tendency toward D-type EC and experienced superior learning progress compared to those in the using-QR code group. Overall, this study showed that the creating-QR code is likely to generate more curiosity when learning the contents of a vehicle maintenance course. This implied that by implementing new ICT for students to create learning contents, creating-QR codes can be seen as a robust set of flipped learning for a technical undergraduate course (Kim, Kim, Khera, & Getman, 2014).
An identity-centered approach influences motivations, which in turn directly influences behaviors and learning (Falk, 2011). This study contributes a novel, vision-identity perspective for explaining EC in relation to the QR code creation or use. Specifically, this study argues that the strength of the relationship between QR code creation involves a higher level of D-type EC in order to remove the unpleasant feelings from being deprived of knowledge, which ultimately affects participants’ learning outcomes. In summary, college teachers should not limit students’ learning to traditional lecturing and multimedia contents. The results of this study suggest that teachers can diversify learning environments to cultivate students’ technological abilities by creating QR codes. Thus, learning by creating QR codes can also be integrated into other areas of learning, such as electrical engineering courses.
Implications
The results indicate some important implications on student-centered learning design and usage. First, the results from the present study suggested that let students organize learning materials and share to each other would significantly raise their D-type EC instead of I-type EC, which indicate that the learners would like to engage in acquire new information to decrease the feeling of
Chun-Hsin Chang & Chi-Ruei Tsai QR Codes in Vehicle Course 135
uncertainty. Second, the students who have lower learning performance have more learning progress when learning with QR code. The findings supported that this flipped learning method might be significantly efficient than conventional lecture. The further findings showed that engaged students in organizing learning content is better than directly access the learning materials by scanning the QR code.
Limitations and Future Studies
This study has a drawback of the user sample because the sample was drawn solely from a population of junior university students. This approach requires further studies involving samples with non-technological majors in order to validate the current results and to provide pivotal implications for future convergence of QR code learning. In addition, because it is still in its early stages of development in creating QR codes for ubiquitous learning, only limited data and experiences related to the effects of creating QR codes are available as is the awareness of course materials on learning attitudes. Moreover, according to the technology acceptance model promoted by Davis (1989), participants’ attitudes towards using and intention to use may be relevant for understanding more about creating or using QR codes for learning and may be subjected to future studies.
Pérez-Sanagustín, Muñoz-Merino, Alario-Hoyos, Soldani, and Kloos (2015) indicated that using QR codes is a good way to create situated-learning experiences, since they allow for creating digital layers of information that extend the physical space with a learning purpose. However, this study took a two-arm experimental study, with the students using QR codes as the control group. A future study may include another group that neither uses nor creates QR codes to learn the vehicle maintenance course and then compare the learning effects among all three groups.
In recent decades, various studies have been conducted regarding the use of QR codes in ubiquitous learning. Future studies can focus on other engineering or technological courses that are conducted over a certain period and include more course units. For example, by extending the experimental period to 12 weeks, the research results may be determined to be even more reliable.
Acknowledgements
This work was financially supported by the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
136 QR Codes in Vehicle Course Chun-Hsin Chang & Chi-Ruei Tsai
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Chun-Hsin Chang & Chi-Ruei Tsai QR Codes in Vehicle Course 141 教育科學研究期刊 第六十四卷第三期 2019年，64（3），119-141 doi:10.6209/JORIES.201909_64(3).0005
