April 2012 Volume 15 Number 2 - Educational Technology & Society

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Cunningham, S. J., & Frank, E. (1999). Market basket analysis of library circulation data. Proceedings of the Sixth International Conference on Neural Information Processing, Australia, 825-830. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user information technology. MIS Quarterly, 13(3), 319-339 Davis, F. D., Bagozzi, R. P., & Warsaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of two theoretical models. Management Science, 35(8), 982-1003. DeLone, W. & McLean, E. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. Fishben, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison- Wesley. Gefen, D., Karahanna, E., & Straub, D.W. (2003). Trust and TAM in online shopping: An Integrated Model. MIS Quarterly, 27(1), 51-90. Goldberg, D., Nichols, D., Oki, B., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of ACM, 35(12), 61-70. Hair, J. F., Black, B., Babin, B., Anderson, R. E. & Tatham, R. L. (2009). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall. Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information and Management Learning, 40(6), 541-549. Howe, A.E. & Dreilinger, D. (1997) Savvysearch: A meta-search engine that learns which search engines to query. AI Magazine, 18(2), 19-25. Hsu, C. L. &. Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information and Management Learning, 41(7), 853-868. Hu, R. & Pu, P. (2009) Acceptance Issues of Personality-based Recommender Systems. Proceedings of the Fourth ACM Conference on Recommender Systems, USA, 221-224. Johannes, S., Matthias, H., Christoph, R., & Ralf, S. (2008). Adapting the user context in realtime: Tailoring online machine learning algorithms to ambient computing. Mobile Networks and Applications, 13(6), 583-598. Johansson, F. (2006). The Medici Effect: What You Can Learn from Elephants and Epidemics. Boston, MA: Harvard Business School. Kaiser, H. F. (1958). The varimax criterion for analysis rotation in factor analysis. Psychometrika, 23(3), 187-200. Karypis, G. (2001). Evaluation of Item-Based Top-N Recommendation Algorithms. Proceedings of the Tenth International Conference on Information and Knowledge management, USA, 247-254. Krulwich, B., & Burkey, C. (1997). The InfoFinder agent: Learning user interests through heuristic phrase extraction. IEEE Intelligent Systems Journal, 12(5), 22-27 Kuan, Y. T. (2004). The study of resource recommendation mechanism based on learning style (Unpublished master’s thesis). Chung Yuan Christian University, Jungli, Taiwan. Lai, C.-M., Huang H.-M., Liaw S.-S., & Huang W.-W. (2009). A study of user’s acceptance on three-dimensional virtual reality applied in medical education. Bulletin of Educational Psychology, 40(3), 342-362. Lang, K. (1995, July). NewsWeeder: Learning to filter netnews. Paper presented at the Twelfth International Conference on Machine Learning, California, USA. Liaw, S.-S & Huang, H.-M. (2003). An investigation of user attitudes toward search engines as an information retrieval tool. Computer in Human Behavior, 19 (6), 751-765. Liaw, S.-S, Chen, G.-D., & Huang, H.-M. (2008). Users’ attitudes toward Web-based collaborative learning systems for knowledge management. Computer & Education, 50(3), 950-961. Liaw, S.-S, Huang, H.-M., & Chen, G.-D. (2007). Surveying instructor and learner attitudes toward e-learning. Computer & Education, 49(4), 1066-1080. Liaw, S.-S. (2007). Computers and Internet as a job assisted tool: Based on the three-tier use model approach. Computer in Human Behavior, 23(1), 399-414. Moon, J. W. & Kim, Y. g. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217- 230. Ndubisi, N. O. (2006). Factors of online learning adoption: A comparative juxtaposition of the theory of planned behavior and the technology acceptance Model. International Journal on E-Learning, 5(4), 571–591. Neter, J., Wasserman, W., & Kutner, M. H. (1990). Applied linear statistical models (3rd ed.). IL: Irwin. Nielsen, J. (1993). Usability Engineering. CA: Academic Press. 99
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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
Page 53 and 54: Meishar-Tal, H., & Tal-Elhasid, E.
Page 55 and 56: Tsai, S. C. (2012). Integration of
Page 57 and 58: order to decrease text complexity a
Page 59 and 60: matter how the learners’ aptitude
Page 61 and 62: If compared by programs, WP student
Page 63 and 64: mean F2F 4.64 4.52 4.35 4.44 *: p
Page 65 and 66: een analyzed and were compared with
Page 67 and 68: Chen, G.-D., Lee, J.-H., Wang, C.-Y
Page 69 and 70: and diverse cognitional and emotion
Page 71 and 72: Encourage and persuade students wit
Page 73 and 74: Materials and procedure Each studen
Page 75 and 76: The lower part of Figure 4 shows st
Page 77 and 78: Mayer, R. E., Sobko, K., & Mautone,
Page 79 and 80: that an item is answered correctly
Page 81 and 82: Table 1. Relative concepts frequenc
Page 83 and 84: The Account Management Module provi
Page 85 and 86: Experiment 1 and results Figure 7.
Page 87 and 88: Utilization of Test Item 36 33 30 2
Page 89 and 90: Utilization of Test Item 36 33 30 2
Page 91 and 92: 0.065 0.877 (17.56) 0.938 (22.55) 0
Page 93 and 94: Hwang, G.J., Hsiao, C.L., & Tseng,
Page 95 and 96: the diverse needs of modern industr
Page 97 and 98: Figure 1. The framework of interdis
Page 99 and 100: Research Design Figure 3. Recommend
Page 101 and 102: friendly. 8. I feel the layout and
Page 103: satisfaction, and system acceptance
Page 107 and 108: than students in a conventional cla
Page 109 and 110: shown in Figure 2 to include the st
Page 111 and 112: (1996) Pleasure-Arousal-Dominance (
Page 113 and 114: Based on this connection, we consid
Page 115 and 116: Final dominance measurements were p
Page 117 and 118: student mood and thereby allow comp
Page 119 and 120: Sessink, O., Beeftink, H., Tramper,
Page 121 and 122: The rationale behind the video form
Page 123 and 124: usefulness of the movies and their
Page 125 and 126: case of the first two questions and
Page 127 and 128: this paper in order to evaluate the
Page 129 and 130: The survey questions Appendix A In
Page 131 and 132: E-training in organizations E-train
Page 133 and 134: efficacy regarding online training,
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
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Effects of RCKI principle-based ped
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References Alexander, C. (1979). Th
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Valsamidis, S., Kontogiannis, S., K
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The Analog system (Yan et al., 1996
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First, the number of the sessions a
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BioLayout uses a modified version o
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As shown in figure 3, all metrics c
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Figure 7. The detailed results for
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Enright, A. J., van Dongen, S., Ouz
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Chu, S. K. W., Kwan, A. C. M., & Wa
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teachers, as well as collaboration
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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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