April 2012 Volume 15 Number 2 - Educational Technology & Society

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studied by setting up an empirical experiment with 16 advanced students of computer science. In this experiment the independent variables were the kind of used device and the presence of an audio comment. Conclusions In this paper we have described the results obtained while integrating AV movies within a first-year undergraduate course on algorithms and data structures. These results, which are promising both from a student performance point of view and from a student appreciation point of view, can be summarized as follows: � AV movies seem to be a proficous way to get the students to invest their time while learning an algorithm. Indeed, after one month of front lectures, students who viewed and reviewed at least two movies performed in a statistically significant way (with respect to the Mann-Whitney-Wilcoxon test) better than the other students, while taking an intermediate exam fully devoted to the visualized sorting algorithms. Clearly, the same effect could have happened if one group of students would have read a text book, while the other group did nothing. However, we believe that AV movies have some motivating factor that we cannot forget: our results support the hypothesis that AV movies may be an effective way of helping students use time well (which is one of the seven principles of good practice in higher education indicated in (Chickering & Gamson, 1987)). � The above performance difference is mainly concentrated on simulation exercises. Students who viewed the movies performed better on these exercises while answering on more theoretical questions did not result in any statistical significant difference. Observe that this result somehow supports the hypothesis that making AV movies available is the main justification for the better performances, since they get the students to invest more time in understanding the behavior of an algorithm. � Students preferred to see the movies on their desktop/laptop display rather than on iPod or another PDA. Indeed, only a negligible percentage of students viewed the movies on these latter devices. It is worth noting, however, that we did not ask the students to specify whether they owned such a device: hence, this result could be simply due to the fact that these devices were not very popular among the analyzed cohort of students. � Basically all students considered the AV movies a useful tool for reviewing and studying the material covered by the lecture notes. As a consequence of this result, the integration of AVs into a learning management system such as Moodle seems to be a very interesting line of future research. Indeed, this possibility has already been explored in (Röβling & Vellaramkalayil, 2009), where the authors present a Moodle activity that is able to incorporate animations created in the ANIMAL system (Rössling & Freisleben, 2002). � Many students stated that the integration of an audio comment into the movies would have been useful. This result seems to be consistent with the experiment performed in (Hürst et al., 2007)) and suggests an interesting research direction. The main problem, in this case, is twofold. From a technical point of view, it is not easy to automatically add an audio comment to a movie, if we want to maintain the internationalization of our software. From a visualization efficacy point of view, instead, an automatically produced audio comment could turn out to be boring and, hence, not very effective. For this reason, we think that satisfying this students' request might turn out to be quite challenging. As a consequence of the results described in this paper, the new version of AlViE is now capable of automatically recording visualizations and of saving them in different video formats: we hope that this feature will turn out to be a good tool for study strategy (as also suggested in (Yi-Chuan & Cifuentes, 2006)). Besides the experimental results we have described in this paper, there are at least two other main reasons that justify this extension. First of all, movies can help algorithm visualization producers while advertising and distributing new algorithm visualizations. By using movies, there is no need to download a specific software: the final user can see the visualization (even on a browser) and subsequently decide whether it is worth using it and changing it (for instance, by executing the algorithm with different inputs). In this latter case, of course, the necessary software has to be downloaded. Secondly, producing movies can turn out to be a motivating way for students to present their visualizations. On the ground of the first author's experience, it seems that students like to construct and to present their own algorithm visualizations: we think that if they can also distribute them by means, for example, of a video pod-cast directory, then they might even be more motivated in realizing an effective visualization (which should turn out in a better understanding of the algorithm). From a more pedagogical point of view, finally, an interesting line of research would be to explore the effects of offering the possibility of interacting with the AVs (for instance, by producing new visualizations with different inputs). In particular, during the next academic year we plan to perform experiments similar to the ones described in 121
this paper in order to evaluate the efficacy of fully integrating this possibility. Once again, our hypothesis will be that whenever the algorithm animations are strictly tightened to the other course material then the possibility of creating new visualizations with different inputs will further improve student performances. Acknowledgments The first author is grateful to Cliff Shaffer for several useful e-mail discussions. We thank the anonymous referees for many valuable comments, which improved the presentation of our results. References Baecker, M. (1998). Sorting out sorting: A case study of software visualization for teaching computer science. In T. Stasko, J. B. Domingue, M. Brown, & B. Price (Eds.), Software Visualization. Cambridge, MA: The MIT Press. Brittain, S., Glowacki, P., Van Ittersum, J., & Johnson, L. (2006). Podcasting lectures. EDUCAUSE Quarterly, 29(3), 24-31. Campbell, G. (2005). There's something in the air: Podcasting in education. EDUCAUSE Review, 40(6), 32-47. Chickering, A. W., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7), 3-7. Cole, J., & Foster, H. (2007). Using Moodle: Teaching with the Popular Open Source Course Management System. Sebastopol, CA: O'Reilly Media, Inc. Crescenzi, P., Gambosi, G., & Grossi, R. (2006). Strutture di Dati e Algoritmi – Progettazione, Analisi e Visualizzazione. Turin, Italy: Pearson Education. Crescenzi, P., Gambosi, G., Grossi, R., Nocentini, C., & Verdese, W. (2007). AlViE [Computer software]. Available from http://www.algoritmica.org/software/ Crescenzi, P., & Nocentini, C. (2007). Fully integrating algorithm visualization into a CS2 course. In Proceedings of the Twelfth Annual Conference on Innovation and Technology in Computer Science Education (pp. 296-300). New York, NY: ACM Dix, A., Finlay, J., Abowd, G., & Beale, R. (2003). Human-Computer Interaction (3rd ed.). Essex, England: Prentice Hall. Hundhausen, C. D., Douglas, S. A., & Stasko, J. T. (2002). A meta-study of algorithm visualization effectiveness. Journal of Visual Languages and Computing, 13(3), 259-290. Hürst, W., Lauer, T., & Nold, E. (2007). A study of algorithm animations on mobile devices. In I., Ressell (Ed.), Proceedings of the Thirty-eighth SIGCSE Technical Symposium on Computer Science Education (pp. 160-164). NY, USA: ACM Laakso, M.-J., Myller, N., & Korhonen, A. (2009). Comparing learning performance of students using algorithm visualizations collaboratively on different engagement levels. Journal of Educational Technology & Society, 12(2), 267-282. Lazzari, M. (2009). Creative use of podcasting in higher education and its effect on competitive agency. Computers & Education, 52(1), 27-34. Naps, L. (2005). JHAVÉ: Supporting algorithm visualization. IEEE Computer Graphics and Applications, 25(5), 49-55. Naps, L., Eagan, J. , & Norton, L. (2000). JHAVÉ – An Environment to actively engage students in Web-based algorithm visualizations. In S., Haller (Ed.), Proceedings of the Thirty-first SIGCSE Technical Symposium on Computer Science Education (pp. 109-113). NY, USA: ACM Naps, L., Rössling, G., Almstrum, V., Dann, W., Fleischer, R., Hundhausen, C.,…Velázquez-Iturbide, J. Á.. (2003). Exploring the role of visualization and engagement in computer science education. ACM SIGCSE Bulletin, 35(2), 131-152. Price, B., Baecker, R., & Small, I. (1993). A principled taxonomy of software visualization. Journal of Visual Languages and Computing, 4(3), 211-266. Rössling, G., & Freisleben, B. (2002). Animal: A system for supporting multiple roles in algorithm animation. Journal of Visual Languages and Computing, 13(3), 341-354. Röβling, G., & Vellaramkalayil, T. (2009). A visualization-based Computer science hypertextbook prototype. ACM Transactions on Computing Education, 9(2), 1-13. Rosson, M., & Carroll, J. (2002). Scenario-based design. In A. Sears & J. Jacko (Eds.), The human-computer interaction handbook: Fundamentals, evolving technologies and emerging applications (pp. 1032-1050). CA, USA: Taylor & Francis Group. 122
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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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Page 121 and 122: The rationale behind the video form
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Page 135 and 136: Method Sample and procedure Figure
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Page 145 and 146: when they are designing and impleme
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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
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Page 161 and 162: The Analog system (Yan et al., 1996
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Page 169 and 170: Figure 7. The detailed results for
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from the students’ ratings appear
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Table 5 summarizes the students’
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eading blogs (i.e., reminders, easy
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Luehmann, A. L. (2008). Using blogg
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Uzunboylu et al. (2009) examined th
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System implementation Overview of M
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Methods Before the experiment, a mo
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too simple, incomplete, or they eve
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Comparisons of UMLS Questionnaire i
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already described may play an impor
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Parker, D., Manstead, A. S. R., Str
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Avci, U., & Askar, P. (2012). The C
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Literature review The related varia
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collected through Personal Informat
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Self-efficacy 92 5 21 13.37 3.726 W
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In this problem, intention was take
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References Ajjan, H., & Hartshorne,
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Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
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Students perused the course materia
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problem. All articles were then sen
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tests, electronic tests are more li
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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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