April 2012 Volume 15 Number 2 - Educational Technology & Society

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visit courses with related content, based on student course paths in the LMS. In the future we will perform aggregate rankings on these groups, which are based on the proposed metrics (Enrichment / Interest / UPCS). Specifically our methodology has the following advantages: 1. It is independent of the LMS platform since it is based on the Apache log files and not the LMS platform itself. Thus, it can be easily implemented for every LMS. 2. It uses new metrics in order to facilitate the evaluation of each course in the LMS and allows the instructors to make proper adjustments to their course e-learning material. 3. It uses clustering techniques in order to identify different groups of courses and different groups of students. 4. The MCL graph clustering algorithm is fast and scalable, and is applied for the first time in the field of elearning. MCL can stack successfully courses into the same cluster based on the students’ course visits and can distinguish isolated courses. 5. The BioLayout visualization tool helps depict the clustering results and allows the instructor to further explore the results in an interactive 3D environment. We received feedback about the methodology by the educators. The educators were informed about the indexing results and most of them increased the quality and the quantity of their educational material. They improved the quality by reorganizing the educational material in a uniform, hierarchical and structured way. They also increased the quantity by embedding additional educational material. An important outcome through the process of informing the educators about our results was that the ranking of the courses constitutes an important motivation for the educators to try to improve their educational material. Because of their mutual competition, they each want their courses to be highly ranked. However, a few educators complained that the organization of their courses does not assist them in having high final scores in the ranking list. They argued that, for example, the metric interest is heavily influenced by the number of web pages used to organize the educational material. Thus, courses that have all their educational material organised in a few pages have a low interest score. They were asked again to re-organize the material for each course in the LMS according to the order they are taught, in order to facilitate the use by the students. In the future we plan to evaluate our methodology by using a pre- and post-test design. A statistical comparison of the metrics of the courses before and after an instructor's exposure to and utilization of the metrics would offer valuable insights and help systematically evaluate our methodology. We also plan to further automate the whole procedure, that is, we are developing a plug-in tool to automate the data pre-processing and clustering steps. This tool will run periodically (each month) and will e-mail to the instructors course ranking and suggestions. A similar policy was also applied by Feng et al. (2005), where in the long term, instructors were informed automatically by email about the quality of the content of their courses. We intend the final tool to offer insights at 2 levels: (i) On-line, with total statistical information such as number of visits per course (pages and sessions), student trends and activities at their visits, as well as detailed information per student (student duration per course and activity, student preferences and activities for all courses), and (ii) Off-line, with the use of data mining techniques such as pre-process, visualization, clustering, classification, regression and association, and discovering hidden data patterns. References Avouris, N., Komis, V., Fiotakis, G., Margaritis, M., & Voyiatzaki, G. (2005). Logging of fingertip actions is not enough for analysis of learning activities. Proceedings of Workshop Usage Analysis in learning systems (AIED’05), (pp. 1-8), The Netherlands: IOS Press. Baraglia, R., & Palmerini, P. (2002). SUGGEST: A web usage mining system, Proceedings of IEEE International Conference on Information Technology: Coding and Computing (pp. 282–287). Las Vegas, NV: IEEE Computer Society Press. Binali, H., Chen, Wu, & Potdar, V. (2009). Emotion detection in E-learning using opinion mining techniques. Proceedings of the 3rd IEEE International Conference on Digital Ecosystems and Technologies (pp. 259-264). Perth, WA, Australia: IEEE. Bing L. (2007). Web data mining: Exploring hyperlinks, contents and usage data. New York, NY: Springer. Claroline. (2009). Claroline.NET open source eLearning and eWorking platform. Retrieved April 10, 2009 from http://www.claroline.net Dongen, S. (2000). Graph clustering by flow simulation (Unpublished doctoral thesis). Utrecht University, Netherlands. 165
Enright, A. J., van Dongen, S., Ouzounis, C. A. (2002). An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research, 30(7), 1575-1584. Feng, M., & Heffernan, N. T. (2005). Informing teachers live about student learning: Reporting in the assistment system. Proceedings of the 12th Annual Conference on Artificial Intelligence in Education Workshop on Usage Analysis in Learning Systems 2005. The Netherlands: IOS Press. Galeas, P. (2009). Web Mining. Retrieved November 21, 2009 from http://www.galeas.de/webmining.html Goldovsky, L., Cases, I., Enright A. J., & Ouzounis, C. A. (2005). BioLayoutJava. Versatile Network Visualisation of Structural and Functional Relationships. Applied Bioinformatics, 4(1), 71-74. GUNet. (2009). Open eClass – Course management system. Retrieved July 10, 2009 from http://eclass.gunet.gr/ Hamalainen, W., & Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In M. Ikeda et al. (Eds.), Procceedings of Int. Conf. in Intelligent Tutoring Systems (pp. 525–534). New York, NY: Springer. Kantardzic, M. (2003). Data Mining: Concepts, models, methods, and algorithms. New York, NY: John Wiley & Sons. Kazanidis, I., Valsamidis, S., Theodosiou, T. & Kontogiannis, S. (2009). Proposed framework for data mining in e-learning: The case of Open e-Class. In H. Weghorn & P. Isaias (Eds). Proceedings of Applied Computing (pp. 254-258). Rome, Italy: IADIS Press. Khribi, M. K., Jemni, M., & Nasraoui, O. (2009). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology & Society, 12(4), 30–42. Kosala, R., & Blockeel, H., (2000). Web mining research: A Survey. SIGKDD Explorations, 2(1), 1-15. Lee, J., Hoch, R., Podlaseck, M., Schonberg, E. & Gomory, S. (1999). Analysis and visualization of metrics for online merchandising. Lecture Notes In Computer Science, Revised Papers from the International Workshop on web Usage Analysis and User Profiling, 1836, 126-141. Mazza, R., & Dimitrova, V. (2007). CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. I. Journal of Human-Computer Studies, 65(2), 125–139. Mazza R., & Milani, C. (2004). GISMO: A graphical interactive student monitoring tool for course management systems. Proceedings of International Conference on Technology Enhanced Learning ’04 (T.E.L.’04) (pp. 18-19). New York, NY: Springer. Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., & Heiner, C. (2005). An educational data mining tool to browse tutorstudent interactions: Time will tell. Proceedings of workshop on educational data mining (pp. 15–22). Retrieved April 22, 2009 from http://www.aaai.org/Papers/Workshops/2005/WS-05-02/WS05-02-003.pdf Nagi, K., Suesawaluk, P. (2008, May). Research analysis of Moodle reports to gauge the level of interactivity in elearning courses at Assumption University, Thailand. Paper presented at the International Conference on Computer and Communication Engineering 2008, Kuala Lumpur, Malaysia. Perkowitz, M., & Etzioni, O. (1999). Adaptive web sites: Conceptual cluster mining. Proceedings of International Joint Conference on Artificial Intelligence (pp. 264–269). San Francisco, CA: Morgan Kaufman Publishers Inc. Radhakrishna, C. R., & Bhaskara, M. R. (1998). Matrix algebra and its applications to statistics and econometrics. Singapore: World Scientific Publishing. Romero, C., Ventura, S., & García, E. (2008a). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1) 368-384. Romero, C., Gutierez, S., Freire, M., & Ventura, S. (2008b). Mining and visualizing visited trails in web-based educational systems. Proceedings of the 1st International Conference on Educational Data Mining (pp. 182-186). Retrieved April 22, 2009 from http://www.educationaldatamining.org/EDM2008/uploads/proc/19_Romero_19.pdf Romero, C., Ventura, S. (2007). Educational data mining: A Survey from 1995 to 2005. Elsevier Journal of Expert Systems with Applications, 33(1), 135-146. Gu, R., Zhu M., Zhao, L., & Zhang, N. (2008). Interest mining in virtual learning environments. Emerald Journal of Online Information Review, 32(2), 133 – 146. Srinivasa, R. (2005). Data mining in e-commerce: A survey. Sadhana, 30(2), 275–289. Srivastava J., Cooley, R., Deshpande, M., & Tan, P.-N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1(2), 12-23. 166
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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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Page 121 and 122: The rationale behind the video form
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Page 129 and 130: The survey questions Appendix A In
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Page 135 and 136: Method Sample and procedure Figure
Page 137 and 138: 0.874 which exceeded the recommende
Page 139 and 140: 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: Figure 7. The detailed results for
Page 173 and 174: Chu, S. K. W., Kwan, A. C. M., & Wa
Page 175 and 176: teachers, as well as collaboration
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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 and 192: too simple, incomplete, or they eve
Page 193 and 194: Comparisons of UMLS Questionnaire i
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
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collection was also approved by all
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Ubiquitous Revision Seamless Collab
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Tai, Y. (2012). Contextualizing a M
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tasks were developed from task type
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experiences in this phase. Moreover
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test is administered again right af
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The collected data were analyzed wi
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Gorp, K. V., & Bogaert, N. (2006).
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4PL IRT model According to the numb
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examinee’s ability more accuratel
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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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