Dong, W. M., & Wong, F. S. (1987). Fuzzy weighted averages and implementation of the extension pr<strong>in</strong>ciple. Fuzzy Sets and Systems, 21(2), 183–199. Doran, R. (1980). Basic measurement and evaluation of science <strong>in</strong>struction. Wash<strong>in</strong>gton D.C.: National Science Teachers Association. Gerber, M., Grund, S., & Grote, G. (2008). Distributed collaboration activities <strong>in</strong> a blended learn<strong>in</strong>g scenario and the effects on learn<strong>in</strong>g performance. Journal of Computer Assisted Learn<strong>in</strong>g, 24(3), 232–244. Gogoulou, A., Gouli, E., Grigoriadou, M., Samarakou, M., & Ch<strong>in</strong>ou, D. (2007). A web-based educational sett<strong>in</strong>g support<strong>in</strong>g <strong>in</strong>dividualized learn<strong>in</strong>g, collaborative learn<strong>in</strong>g and assessment. Journal of <strong>Educational</strong> <strong>Technology</strong> & <strong>Society</strong>, 10(4), 242–256. Hailikari, T., Katajavouri, N., & L<strong>in</strong>dblom-Ylanne, S. (2008). The relevance of prior knowledge <strong>in</strong> learn<strong>in</strong>g and <strong>in</strong>structional design. American Journal of Pharmaceutical Education, 72(5), 1–8. Huang, Y. M., Kuo, Y. H., L<strong>in</strong>, Y. T., & Cheng, S. C. (2008). Toward Interactive Mobile Synchronous Learn<strong>in</strong>g Environment with Context-awareness Service. Computers & Education, 51(3), 1205–1226. Hwang, G. J., Tseng, Judy. C. R., & Hwang, G. H. (2008). Diagnos<strong>in</strong>g student learn<strong>in</strong>g problems based on historical assessment records. Innovations <strong>in</strong> Education and Teach<strong>in</strong>g International, 45(1), 77–89. Lee, D. H., & Park, D. (1997). An efficient algorithm for fuzzy weighted average. Fuzzy Sets and Systems, 87(1), 39–45. L<strong>in</strong>, Y. C., L<strong>in</strong> Y. T., Huang, Y. M. (2011). Development of a diagnostic system us<strong>in</strong>g a test<strong>in</strong>g-based approach for strengthen<strong>in</strong>g student prior knowledge. Computers & Education, 57(2), 1557–1570. Moos, D. C., & Azevedo, R. (2008). Self-regulated learn<strong>in</strong>g with hypermedia: The role of prior doma<strong>in</strong> knowledge. Contemporary <strong>Educational</strong> Psychology, 33(2), 270–298. Ozuru, Y., Dempsey, K., & McNamara, D. S. (2009). Prior knowledge, read<strong>in</strong>g skill, and text cohesion <strong>in</strong> the comprehension of science texts. Learn<strong>in</strong>g and Instruction, 19(3), 228–242. Panjaburee, P., Hwang, G. J., Triampo, W., & Shih, B. Y. (2010). A Multi-Expert Approach for Develop<strong>in</strong>g Test<strong>in</strong>g and Diagnostic Systems Based on the Concept Effect Model. Computers & Education, 55(2), 527–540. Roschelle, J. (1995). Learn<strong>in</strong>g <strong>in</strong> Interactive Environments: Prior knowledge and new experience. Wash<strong>in</strong>gton, D.C.: American Association of Museums. Ross, T. J., Sorensen, H. C., Savage, S. J., & Carson, J. M. (1990). DAPS: Expert system for structural damage assessment. Journal of Computer and Civil Eng<strong>in</strong>eer<strong>in</strong>g, 4(4), 327–348. Saleh, I., & Kim, S. I. (2009). A fuzzy system for evaluat<strong>in</strong>g students' learn<strong>in</strong>g achievement. Expert Systems with Applications, 36(3), 6236–6243. Seel, N. M., & D<strong>in</strong>ter, F. R. (1995). Instruction and mental model progression: learner-dependent effects of teach<strong>in</strong>g strategies on knowledge acquisition and analogical transfer. <strong>Educational</strong> Research and Evaluation, (1), 4–35. Tao, Y. H., Wu, Y. L., & Chang, H. Y. (2008). A practical computer adaptive test<strong>in</strong>g model for small-scale scenarios. Journal of <strong>Educational</strong> <strong>Technology</strong> & <strong>Society</strong>, 11(3), 259–247. Tseng, C. R., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an adaptive learn<strong>in</strong>g system with two sources of personalization <strong>in</strong>formation. Computers and Education, 51(2), 776–786. Treagust, D. F. (1988). Development and use of diagnostic tests to evaluate students' misconceptions <strong>in</strong> science. International Journal of Science Education, 10(2), 159–169. Tuckman, B. W., & Monetti, D. M. (2010). <strong>Educational</strong> Psychology. Wadsworth, OH: Cengage Learn<strong>in</strong>g. 131
The EFWA algorithm Appendix Def<strong>in</strong>ition: the <strong>in</strong>put a, b, c, and d are the <strong>in</strong>tervals of fuzzy membership functions, and the outputs are the <strong>in</strong>tervals of the result fuzzy membership function. Additionally, the δ s and the ζ i s can be calculated by i Equations (7) and (8) respectively. ( a − a ) e + ( a − a ) e + ... + ( a a ) e δs = i e + e + ... e 1 i 1 2 i 2 n−i n 1 2 ( b − b) e + ( b − b) e + ... + ( b b) e ζ s = i e + e + ... e 1 i 1 2 i 2 n−i n 1 2 Description of the EFWA algorithm (Lee and Park, 1997) n n (1) Sort a’s <strong>in</strong> non-decreas<strong>in</strong>g order. Let (a1, a2, …, an) be the result<strong>in</strong>g sequence. Let first := 1 and last := n. (2) Sort a’s <strong>in</strong> non-decreas<strong>in</strong>g order. Let (a1, a2, …, an) be the result<strong>in</strong>g sequence. Let first := 1 and last := n. (3) Let δ-threshold := ⎢⎣( first + last ) /2⎥⎦ . For each i = 1, 2, …, δ-threshold, let ei := di and for each i =δthreshold + 1, …, n, let ei := ci. For an n-tuple S = (e1, e2, …, en), evaluate and δ − . + δsδ −threshold sδ threshold (4) If δsδ −threshold > 0 and δsδ − threshold + 1 ≤ 0 then L = fL(e1, e2, …, en) and go to Step 4; otherwise execute the follow<strong>in</strong>g step. (a) If > 0 , then first :=δ-threshold + 1; otherwise last := δ-threshold, and go to Step 2. δsδ −threshold (5) Sort b’s <strong>in</strong> non-decreas<strong>in</strong>g order. Let (b1, b2, …, bn) be the result<strong>in</strong>g sequence. Let first := 1 and last := n. (6) Letζ-threshold := ( first + last ) /2 ⎢⎣ ⎥⎦ . For each i = 1, 2, …, ζ-threshold, let ei := ci and for each i =ζthreshold + 1, …, n, let ei := di. For an n-tuple S = (e1, e2, …, en), evaluate and . ζ sζ −threshold 1 ζ s( ζ − threshold + 1) (7) If > 0 and ζ ≤ 0 then U = fU(e1, e2, …, en) and stop; otherwise execute the follow<strong>in</strong>g step: − + ζ sζ −threshold sζ threshold 1 (b) If > 0 , then first :=ζ-threshold + 1; otherwise last := ζ-threshold, and go to step 5. Illustrative example ζ sζ −threshold Assume that an <strong>in</strong>structor aims to assess five students (S1, S2, S3, S4, S5) to identify their level of understand<strong>in</strong>g with regard to five concepts (C1, C2, C3, C4, C5,). The <strong>in</strong>structor selects five test items (I1, I2, I3, I4, I5) from a test item bank to form a test-sheet, that are relevant to concepts one to five, and each test item has its difficulty degree, D1, D2, D3, D4, D5. In this test-sheet, each concept is possibly related to the others. The <strong>in</strong>structor then conducts the test to assess the five students to identify the level of understand<strong>in</strong>g of the <strong>in</strong>dividual students with regard to the five concepts. The degree of difficulty of each test item is shown <strong>in</strong> Table 7. In addition, the relationships among the test items and concepts are shown <strong>in</strong> Table 8, and the relationships among the concepts are shown <strong>in</strong> Table 9. After the (7) (8) 132
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http://www.ifets.info ISSN: 1436-45
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Supporting Organizations Centre for
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Timely Diagnostic Feedback for Data
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Lim et al. addressed two gaps, a us
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The new found benefits of technolog
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analysis of all articles submitted
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Since some articles could not meet
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Table 2. Distribution of research t
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Research method Table 8 shows diffe
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authors concluded that mixed-method
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trends. Library and Information Sci
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Taylor, E. W. (2001). Adult Educati
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Zealand 10 paper 15 19 Using mutual
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impact on a variety of sectors arou
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Grand challenges These grand challe
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eport for details of competencies c
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learning repositories to use in con
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Acknowledgements This paper is base
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eliminating duplicates, a total of
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Findings of this review In the foll
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Table 2. Summary of empirical resea
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students’ conception of learning
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their creative capacity in designin
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which make it impossible to see how
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The content knowledge Table 3 depic
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The first revision we have made to
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Angeli, C., & Valanides, N. (2005).
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Lee, H., & Hollebrands, K. (2008).
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Rushby, N. (2013). The Future of Le
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The top ten in the 2011 survey were
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My first vision of the future is on
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References Dienstag, J. (2006). Pes
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industries, new laws, and new areas
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or distractive effects of technolog
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This may be further complicated by
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espect, the literature about school
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Tondeur, J., Van Keer, H., van Braa
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Disruptive technology When mainfram
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with the physical environment, such
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mockups, and then embedded in compu
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the global marketplace. Students in
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games, and virtual worlds are domin
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- Page 141 and 142: drA = − + − + − = 2 2 2 ( , M
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We take two instances of character
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In designing Chinese-PP, we instead
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Boticki, I., Looi, C.-K., & Wong, L
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Hwang, G.-J., Sung, H.-Y., Hung, C.
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2000). For example, Filippidis and
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amount of information about the lea
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The factors that affected the stude
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Burguillo, J. C. (2010). Using game
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Wong, L.-H. (2013). Analysis of Stu
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Recognizing both the importance and
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Student responses were coded based
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English with the translations of th
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With her mother’s support, Jane w
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LGC that could occur in any physica
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The significance of the reported st
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Chen, Y.-L., Pan, P.-R., Sung, Y.-T
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them develop new concepts to facili
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Figure 1. Visualization of how mino
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experimental group all the other le
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Procedures All subjects in both gro
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misconceptions corrected by a learn
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exploration environment constructed
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Küçüközer, H., & Kocakülah, S.
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Lin, J.-W., Lai, Y.-C., & Chuang, Y
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The related works Feedback should b
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E 2 1 R 23 N E 5 R 15 E 1 R 13 E 3
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Once the frequent itemsets have bee
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The first phase generates a diagnos
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To assure pretest validity and reli
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Questionnaire and interviews To und
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Maughan, S., Peet, D., & Willmott,
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According to Angeli and Valanides (
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description, objectives, and entry
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TCK: For the fourth activity, the g
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A questionnaire focusing on Instruc
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eing aware of the importance of sel
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References Airasian, P. W., & Walsh
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Instructional Technology Instructio
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and He (2006) revealed that technol
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Model and hypothesis development Sa
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Table 1. Student profile Gender: Ma
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H3 Student satisfaction has positiv
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Conclusions Addressing four PRS iss
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Tao, Y.-H., & Yeh, R. C. (2009, Jul
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My overall experience of PRS is abs
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skills can move to the next learnin
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were divided into an experimental g
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illustrates the Monopoly gameplay m
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Comparison of monopoly and instruct
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Guskey, T. R., & Gates, S. L. (1986
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Yang, C.-C., Tseng, S.-S., Liao, A.
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text only because learners are able
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Figure 2. Structure of poetry multi
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time period. Bandwidth is measured
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Participants and experimental proce
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owses multimedia resources. Fewer p
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the other approach. Meanwhile, the
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Wang, Y.-H., Young, S. S.-C., & Jan
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mistakes in front of the teacher an
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Basic English learning materials we
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The instructor also showed positive
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H.L, M = 38.11) which occurred in b
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Discussions Tangible learning compa
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We can conclude from the current st
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Joo, Y. J., Joung, S., & Kim, E. K.
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on 506 students registered for cybe
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and management systems, learning se
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multivariate normal distribution as
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Structural model examination Table
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Direct effect Flow ← Satisfaction
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Second, the results of this study c
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Kim, H. S. (2008). The effects of t
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language accurately, or product-ori
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Thailand 5 Vietnam 25 Total 208 Res
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Procedures of data collection Figur
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discussions. From Brian’s log fil
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universities. There were 259 studen
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and he provided his arguments and r
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Within groups Total Test 4 Between
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Crooks, B. (2003). The combined use
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Appendix A 342
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teenager. Chapter four, “Identity