April 2012 Volume 15 Number 2 - Educational Technology & Society

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of the questionnaire (using Cronbach’s alpha) was 0.78. Analysis of the herein-considered sample showed a reasonable level of reliability (alpha>0.70). On average, learners reported highly positive attitudes towards MTVRS as a mobile reflective learning environment (M=4.01, SD=0.68). Besides, Table 2 presents the descriptive statistics for each UMLS item. On the questionnaire survey, a mean of 4 or more was recorded for items 1, 3, 4, 6, 9, 12, 15, 17, 18, 21, 22 and 23. This result indicated most learners thought using the MTVRS could enhance their attitudes toward mobile technologies and be useful in better understanding traffic violations. For instance, item 1: “MLS is useful for creating traffic violation issues”, most learners agreed that MLS is useful for posing traffic violations issues (M=4.13, SD=0.49). Following the use of mobile technologies with MTVRS, learners highly agreed with the benefits regarding their understanding of traffic violations problems from their real life surroundings. The highest score was recorded on the questionnaire for item 15: “MLS is an effective method for providing personalized information with regard to traffic violation issues” (M=4.38, SD=0.71). This result indicated mobile devices can provide scaffolding to the learners with effective approaches to investigation. Further, in terms of item “Overall, satisfaction with MLS with regard to traffic violation issues is acceptable”, the result indicated learners generally accepted using MTVRS in an ML environment with regard to traffic violations was supported and satisfactory (M=4.16, SD=0.68). According to the result of item 23: “Using MLS gives users opportunity to join reflection about traffic violation issues without the limitation of time and space”, the questionnaire mean was 4.06 (SD=0.84). The result indicated learners had high agreement in using MTVRS to reflect traffic violation problems in the ML environment. In sum, the findings of the questionnaire were positive and satisfactory. Table 2. Descriptive statistics for UMLS items Items Mean SD 1. MLS is useful for creating traffic violations issues. 4.13 0.49 2. MLS is a good reflection tool for creating traffic violations issues. 3.88 0.61 3. MLS is easy to use for creating traffic violations issues. 4.19 0.69 4. MLS made it easier to understand traffic violations issues. 4.09 0.64 5. MLS makes a good forum for interaction. 3.88 0.66 6. MLS makes it easier to reflect traffic violations problems with other learners. 4.13 0.61 7. MLS makes it easier to reflect traffic violations problems with the instructor. 3.97 0.78 8. MLS is a convenient platform to access reflection contents about traffic violations problems. 3.94 0.67 9. Overall, satisfaction with MLS with regard to traffic violations issues is acceptable. 4.16 0.68 10. MLS has the potential to become a good learning tool with regard to traffic violations issues. 3.78 0.71 11. MLS adds value to e-learning traffic violations issues. 3.84 0.68 12. MLS allows instant access to traffic violations issues regardless of your location. 4.00 0.62 13. MLS is useful as a supplement to traffic violations issues. 3.94 0.72 14. MLS is an effective learning aid or assistant for learners with regard to traffic violations issues. 3.88 0.66 15. MLS is an effective method for providing personalized information with regard to traffic violations issues. 4.38 0.71 16. MLS allows the conversion of idle time into productive time with regard to traffic violations issues. 3.81 0.59 17. MLS allows convenient access to reflection contents related to traffic violations problems– anywhere and anytime. 4.34 0.87 18. Information sent by MLS is more effective. 4.03 0.65 19. MLS allows access to information from the website about traffic violations issues. 3.94 0.50 20. MLS can be used as a supplemental tool for any existing course. 3.75 0.72 21. Information regarding traffic violations issues sent by MLS is more effective. 4.00 0.84 22. Information regarding traffic violations issues shared via discussing with MLS is more 4.19 0.82 23. effective. Using MLS gives users opportunity to join reflection about traffic violations problems without the limitation of time and space. 4.06 0.84 Total feedback 4.01 0.68 187
Comparisons of UMLS Questionnaire in terms of gender and age To further examine the attitudes toward mobile technologies and traffic violations in ML for each gender and age level, the collected data were further divided into the same characteristic of learners by gender and age level. Table 3 and Table 4 showed a t-test of the independent sample, indicating a significant difference in UMLS scores between genders and ages by the end of the experiment. That is, the mean score of females on UMLS was 97.00 (SD=6.14) compared to 88.12 (SD=3.67) for males, a difference that was significant (t=-5.03, p=.000
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April 2012 Volume 15 Number 2
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Supporting Organizations Centre for
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Contextualizing a MALL: Practice De
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Kop, R. (2012). The Unexpected Conn
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Human mediation and information flo
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elevant information based on some k
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of learners. A search facilitated t
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information as human mediation mean
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Blau, I., & Barak, A. (2012). How D
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examined whether the readiness to p
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Table 5 presents the ANOVA for the
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However, in discussing a non-sensit
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deviations showed smaller variance,
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actual participation discussing sen
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Mesch, G., & Elgali, Z. (2009). Soc
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Although prior research on overall
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with a score of 39 and below as “
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Table 2 demonstrates mean scores an
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Discussion Although the recent Inte
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Furthermore, the most preferred pla
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MEB. (2009). Internete erisim proje
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Learning Management Systems (LMS) b
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practice and repetition with feedba
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Online peer assessment Peer assessm
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At an international conference on m
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� Ability to produce rich assessm
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Meishar-Tal, H., & Tal-Elhasid, E.
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Tsai, S. C. (2012). Integration of
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order to decrease text complexity a
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matter how the learners’ aptitude
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If compared by programs, WP student
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mean F2F 4.64 4.52 4.35 4.44 *: p
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een analyzed and were compared with
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Chen, G.-D., Lee, J.-H., Wang, C.-Y
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and diverse cognitional and emotion
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Encourage and persuade students wit
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Materials and procedure Each studen
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The lower part of Figure 4 shows st
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Mayer, R. E., Sobko, K., & Mautone,
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that an item is answered correctly
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Table 1. Relative concepts frequenc
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The Account Management Module provi
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Experiment 1 and results Figure 7.
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Utilization of Test Item 36 33 30 2
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Utilization of Test Item 36 33 30 2
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0.065 0.877 (17.56) 0.938 (22.55) 0
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Hwang, G.J., Hsiao, C.L., & Tseng,
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the diverse needs of modern industr
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Figure 1. The framework of interdis
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Research Design Figure 3. Recommend
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friendly. 8. I feel the layout and
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satisfaction, and system acceptance
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Ozok, A. A., Fan, Q., & Norcio, A.
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than students in a conventional cla
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shown in Figure 2 to include the st
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(1996) Pleasure-Arousal-Dominance (
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Based on this connection, we consid
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Final dominance measurements were p
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student mood and thereby allow comp
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Sessink, O., Beeftink, H., Tramper,
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The rationale behind the video form
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usefulness of the movies and their
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case of the first two questions and
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this paper in order to evaluate the
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The survey questions Appendix A In
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E-training in organizations E-train
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efficacy regarding online training,
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Method Sample and procedure Figure
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0.874 which exceeded the recommende
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Even though the result does not con
Page 141 and 142: Brown, M., & Cudeck, R. (1993). Alt
Page 143 and 144: Wen, Y., Looi, C.-K., & Chen, W. (2
Page 145 and 146: when they are designing and impleme
Page 147 and 148: paper focuses on the first three st
Page 149 and 150: their self-esteem to be partners in
Page 151 and 152: Democratizing knowledge and symmetr
Page 153 and 154: 1) Reading the article: At the star
Page 155 and 156: Effects of RCKI principle-based ped
Page 157 and 158: References Alexander, C. (1979). Th
Page 159 and 160: Valsamidis, S., Kontogiannis, S., K
Page 161 and 162: The Analog system (Yan et al., 1996
Page 163 and 164: First, the number of the sessions a
Page 165 and 166: BioLayout uses a modified version o
Page 167 and 168: As shown in figure 3, all metrics c
Page 169 and 170: Figure 7. The detailed results for
Page 171 and 172: Enright, A. J., van Dongen, S., Ouz
Page 173 and 174: Chu, S. K. W., Kwan, A. C. M., & Wa
Page 175 and 176: teachers, as well as collaboration
Page 177 and 178: from the students’ ratings appear
Page 179 and 180: Table 5 summarizes the students’
Page 181 and 182: eading blogs (i.e., reminders, easy
Page 183 and 184: Luehmann, A. L. (2008). Using blogg
Page 185 and 186: Uzunboylu et al. (2009) examined th
Page 187 and 188: System implementation Overview of M
Page 189 and 190: Methods Before the experiment, a mo
Page 191: too simple, incomplete, or they eve
Page 195 and 196: already described may play an impor
Page 197 and 198: Parker, D., Manstead, A. S. R., Str
Page 199 and 200: Avci, U., & Askar, P. (2012). The C
Page 201 and 202: Literature review The related varia
Page 203 and 204: collected through Personal Informat
Page 205 and 206: Self-efficacy 92 5 21 13.37 3.726 W
Page 207 and 208: In this problem, intention was take
Page 209 and 210: References Ajjan, H., & Hartshorne,
Page 211 and 212: Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
Page 213 and 214: Students perused the course materia
Page 215 and 216: problem. All articles were then sen
Page 217 and 218: tests, electronic tests are more li
Page 219 and 220: Students 10 and 11: My classmate Ed
Page 221 and 222: collection was also approved by all
Page 223 and 224: Ubiquitous Revision Seamless Collab
Page 225 and 226: Tai, Y. (2012). Contextualizing a M
Page 227 and 228: tasks were developed from task type
Page 229 and 230: experiences in this phase. Moreover
Page 231 and 232: test is administered again right af
Page 233 and 234: The collected data were analyzed wi
Page 235 and 236: Gorp, K. V., & Bogaert, N. (2006).
Page 237 and 238: 4PL IRT model According to the numb
Page 239 and 240: examinee’s ability more accuratel
Page 241 and 242: would be more accurate if items j+1
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C�I or Change I�I) would be 0.2
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improved by rearrangement procedure
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Table 6. Repeated Measures ANOVA of
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Leppisaari, I., & Lee, O. (2012). M
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Selection of the virtual tool The t
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Environmental themes emerged strong
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Visualization of text (visual langu
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environments where visual technolog
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- Logging in to learning environmen
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McNaught, C., Lam, P. & Lam, S.L. (
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on-site to finally achieve a meanin
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Table 1. Digital game designers and
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worldviews are socio-cultural-histo
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Phase Methods Visitor data collecti
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operate the game without the help o
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Visitors (end users) must be heard.
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Islas Sedano, C., Pawlowski, J., Su
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part of something larger than the s
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Previous studies have developed rel
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Technology Acceptance. The 10 items
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Table 4. Model Fit Indices Model χ
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In the final path model of set 1, t
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Discussion Differing from prior stu
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Haythornthwaite, C., Kazmer, M. M.,
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Ge, Z.-G. (2012). Cyber Asynchronou
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college of a university situated in
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Table 3 shows us the following info
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Appendix B shows Class 1’s respon
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increase one’s ability to process
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Appendix A Responses concerning syn
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Shih, S.-C., Kuo, B.-C., & Liu, Y.-
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Adaptive U-learning Math Path Syste
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Figure 4. Online tutorial courses o
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Participants and Experimental Proce
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the influence of Test1 scores, an e
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Hall, T., & Bannon, L. (2006). Desi
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(Hofer & Pintrich, 1997; Liu, Lin &
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� Through online peer assessment,
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elativism.” High Internet self-ef
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Peng, H., Tsai, C.-C., & Wu, Y.-T.
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Since game-based learning has been
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The second reason is about the sust
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Methods Figure 5. Quest NPCs provid
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These differences indicated that pa
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quests further involves students’
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Chang, I.-H. (2012). The Effect of
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(1) Vision, planning and management
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Based on an examination of the lite
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Table 2. Analysis of reliability an
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λ10 - 0.69 0.83 ε5 14.50 * - 0.32
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Bailey, G. D. (1997). What technolo
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Stegall, P. (1998). The principal:
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instruction has studied the student
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espond and the estimation of possib
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Research question For the purpose o
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READI reliability To verify the con
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Physical -1.43333(*) .44699 .029 -2
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The results of this study suggest t
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Hsu, Y.-C., Ho, H. N. J., Tsai, C.-
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domains. Five research questions we
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Research sample groups (4) Faculty
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13. Policies, Social Culture Impact
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Non-Specified Others Engineering &
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(AR = -2.1) (n = 7) 2. Non-specifie
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(n): Total number of articles Resea
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Compared to the publications in 200
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Pedagogical Content Knowledge (TPAC
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