April 2012 Volume 15 Number 2 - Educational Technology & Society

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Discussion Figure 4. The results of research model. *p
satisfaction, and system acceptance. Working from an academic perspective, we proposed and implemented a service able to use innovative strategies to offer interdisciplinary recommendations and to automatically detect areas in which the interests and/or needs of learners have changed. This system can recommend suitable learning materials in the service of individualized learning. This study is also the first to use factor analysis in its examination of recommendation services for learning resources and to explore the attitudes and system acceptance of learners. Thus, our results support the 3-TUM model and the research hypotheses and serve as a foundation for the development of systems that recommend learning options. Working from a practical perspective, this study proposed four important elements of the proposed system: system stability, interface satisfaction, recommendation accuracy, and content satisfaction. Learners were most concerned with interface design and the accuracy of the recommendations for instructional material. Our data emphasized the importance of designing recommendation systems that allow learners to simply and conveniently use the services and that provide learning materials that conform to the learners’ needs and interests. This study offers important contributions to the relevant literature by presenting innovative concepts regarding web-based interdisciplinary recommendations and by emphasizing the need for a system that is consistent with contemporary pedagogical conditions in its ability to enhance and effectively respond to diversity in learning. Acknowledgements This study is partially supported by the National Science Council of Taiwan under grants NSC97-2511-S-142-003 and NSC98-2511-142-005. References Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20 th International Conference on Very large Databases, Chile, 487-499. Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207-216. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. NY: Springer-Verlag Baker-Eveleth, L., Eveleth, D. M., O’Neill, M., & Stone, R. W. (2006). Enabling laptop exams using secure software: Applying the technology acceptance model. Journal of Information Systems Education, 17(4), 413–420. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall. Blumberg, B., Cooper, D. R., & Schindler, P. S. (2008) Business research method (2nd ed.). NY: McGraw Hill Higher Education. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1, 245-276. Chang, C.-K. (2010). Acceptability of an asynchronous learning forum on mobile devices. Behavior & Information Technology, 29(1), 23-33. Chen, H.-R. (2007). Evaluating Recommendation Service Mechanisms for Asynchronous Digital Learning Systems. WSEAS Transactions on Computers, 6(3), 612-617. Chen, H. R. & Yeh, F. Y. (2008) Instruction Resource Recommendation Services for Elementary Schools in Taiwan. International Journal of Human and Social Sciences, 3(2), 104-107. Chen, S. Y. & Liu, X. (2004). The contribution of data mining to information science. Journal of Information Science, 30(6), 550- 558. Chen, S. Y. (2005). Evaluating the learning effectiveness of using Web-based instruction: An individual differences approach. International Journal of Information & Communication Technology Education, 1(1), 69-82. Cheng-Chang, S. P., Gunter, G., Sivo, S., & Cornell, R. (2004–2005). End-user acceptance of a learning management system in two hybrid large-sized introductory undergraduate course: A case study. Journal of Educational Technology Systems, 33(4), 355- 365. Chien, Y. H. (2009). Learning and context of use for small-screen leading displays on visual performance in a Chinese sample. Perceptual and Motor Skills, 108(2), 505-512. Compeau, D. R. & Higgins C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. 98
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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
Page 51 and 52: � 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
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Page 73 and 74: Materials and procedure Each studen
Page 75 and 76: The lower part of Figure 4 shows st
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Page 79 and 80: that an item is answered correctly
Page 81 and 82: Table 1. Relative concepts frequenc
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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
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Page 99 and 100: Research Design Figure 3. Recommend
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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
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Page 121 and 122: The rationale behind the video form
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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
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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
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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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