October 2007 Volume 10 Number 4 - Educational Technology ...

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Q.1 [0, 0] [0, 0] [0, 0] [0.7, 0.8] [0.8, 0.9] [0.9, 1.0] Q.2 [0, 0] [0, 0] [0.6, 0.7] [0.8, 0.9] [0.8, 0.9] [0, 0] Q.3 [0, 0] [0, 0] [0, 0] [0.7, 0.8] [0.8, 0.9] [0.8, 0.9] Q.4 [0, 0] [0.5, 0.6] [0.8, 0.9] [0.7, 0.8] [0, 0] [0, 0] Total mark = July 3, 2006 Question No. Vague marks Grade Q.1 [0, 0] [0, 0] [0, 0] [0.6, 0.7] [0.8, 0.9] [0.7, 0.8] Q.2 [0, 0] [0, 0] [0.6, 0.7] [0.8, 0.9] [0.7, 0.8] [0, 0] Q.3 [0, 0] [0, 0] [0, 0] [0.5, 0.6] [0.7, 0.8] [0.8, 0.9] Q.4 [0, 0] [0.5, 0.6] [0.8, 0.9] [0.6, 0.7] [0, 0] [0, 0] Total mark = July 4, 2006 Question No. Vague marks Grade Q.1 [0, 0] [0, 0] [0, 0] [0.6, 0.7] [0.8, 0.9] [0.8, 0.9] Q.2 [0, 0] [0, 0] [0.5, 0.6] [0.8, 0.9] [0.7, 0.8] [0, 0] Q.3 [0, 0] [0, 0] [0, 0] [0.7, 0.8] [0.8, 0.9] [0.8, 0.9] Q.4 [0, 0] [0.6, 0.7] [0.8, 0.9] [0.7, 0.8] [0, 0] [0, 0] Total mark = Figure 3. Evaluating the student’s answerscript at different days using the proposed method Table 10. A comparison of the evaluating results for different methods Methods Total mark Days Biswas’ method (1995) The proposed method July 1, 2006 69 68 July 2, 2006 72 68 July 3, 2006 55 68 July 4, 2006 55 68 The Merits of the Proposed Methods The proposed methods have the following advantages: (1) The proposed methods are more flexible and more intelligent than Biswas’ methods (1995) due to the fact that we use vague sets rather than fuzzy sets to represent the vague mark of each question, where the evaluator can use vague values to indicate the degree of the evaluator’s satisfaction for each question. Especially, the proposed methods are particularly useful when the assessment involves subjective evaluation. (2) The proposed methods are more stable to evaluate students’ answerscripts than Biswas’ methods (1995). They can evaluate students’ answerscripts in a more flexible and more intelligent manner. Conclusions In this paper, we have presented two new methods for evaluating students’ answerscripts based on the similarity measure between vague sets. The vague marks awarded to the answers in the students’ answerscripts are represented by vague sets, where each element belonging to a vague set is represented by a vague value. An index of optimismλ determined by the evaluator is used to indicate the degree of optimism of the evaluator, whereλ ∈[0, 1]. Because the proposed methods use vague sets to evaluate students’ answerscripts rather than fuzzy sets, they can evaluate students’ answerscripts in a more flexible and more intelligent manner. The experimental results show that 239
the proposed methods can evaluate students’ answerscripts more stable than Biswas’ methods (1995). Acknowledgements The authors would like to thank Professor Jason Chiyu Chan, Department of Education, National Chengchi University, Taipei, Taiwan, Republic of China, for providing very helpful comments and suggestions. This work was supported in part by the National Science Council, Republic of China, under Grant NSC 95-2221-E-011-117-MY2. References Biswas, R. (1995). An application of fuzzy sets in students’ evaluation. Fuzzy Sets and Systems, 74 (2), 187-194. Chang, D. F., & Sun, C. M. (1993). Fuzzy assessment of learning performance of junior high school students. Paper presented at the First National Symposium on Fuzzy Theory and Applications, June 25-26, 1993, Hsinchu, Taiwan. Chen, S. M. (1988). A new approach to handling fuzzy decisionmaking problems. IEEE Transactions on Systems, Man, and Cybernetics, 18 (6), 1012-1016. Chen, S. M. (1995a). Arithmetic operations between vague sets. Paper presented at the International Joint Conference of CFSA/IFIS/SOFT’95 on Fuzzy Theory and Applications, December 7-9, 1995, Taipei, Taiwan. Chen, S. M. (1995b). Measures of similarity between vague sets. Fuzzy Sets and Systems, 74 (2), 217-223. Chen, S. M. (1997). Similarity measures between vague sets and between elements. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 27 (1), 153-158. Chen, S. M. (1999). Evaluating the rate of aggregative risk in software development using fuzzy set theory. Cybernetics and Systems, 30 (1), 57-75. Chen, S. M., & Lee, C. H. (1999). New methods for students’ evaluating using fuzzy sets. Fuzzy Sets and Systems, 104 (2), 209-218. Chen, S. M., & Wang, J. Y. (1995). Document retrieval using knowledge-based fuzzy information retrieval techniques. IEEE Transactions on Systems, Man, and Cybernetics, 25 (5), 793-803. Cheng, C. H., & Yang, K. L. (1998). Using fuzzy sets in education grading system. Journal of Chinese Fuzzy Systems Association, 4 (2), 81-89. Chiang, T .T., & Lin C. M. (1994). Application of fuzzy theory to teaching assessment. Paper presented at the 1994 Second National Conference on Fuzzy Theory and Applications, September 15-17, 1994, Taipei, Taiwan. Frair, L. (1995). Student peer evaluations using the analytic hierarchy process method. Paper presented at the Frontiers in Education Conference, November 1-4, 1995, Atlanta, GA, USA. Gau, W. L., & Buehrer, D. J. (1993). Vague sets. IEEE Transactions on Systems, Man, and Cybernetics, 23 (2), 610- 614. Echauz, J. R., & Vachtsevanos, G. J. (1995). Fuzzy grading system. IEEE Transactions on Education, 38 (2), 158- 165. Hwang, G. J., Lin, B. M. T., & Lin, T. L. (2006). An effective approach for test-sheet composition with large-scale item banks, Computers & Education, 46 (2), 122-139. Kaburlasos, V. G., Marinagi, C. C., & Tsoukalas, V. T. (2004). PARES: A software tool for computer-based testing 240
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October 2007 Volume 10 Number 4
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Abstracting and Indexing Educationa
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The Relationship of Kolb Learning S
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a question about learning together
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affordances of networked learning s
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included a unit on collaborative le
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on at different times and work indi
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• the numerous evaluation studies
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wanted to work. The same inquiry pr
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usability studies have a place in t
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Milrad, M., & Jackskon, M. (2007).
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information- and communication tech
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Interactive Examination, the studen
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Table 1. Criteria for grading OD st
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content. Differences in the attitud
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Even though further research on the
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Olofsson, A. D. (2007). Participati
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Etzioni (1993) points out that an i
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programme. They are rather construc
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to be that each individual trainee
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Bernmark-Ottosson, A. (2005). Demok
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The Knowledge Foundation (2005). IT
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Figure 1. Design Theories in contex
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attention as we frequently discuss
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Blog Reflection This design concept
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Narrative Structure (Form) Content
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Löwgren, J., & Stolterman, E. (200
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critique, neither do the single ind
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suggested by Ljungberg (1999b) we c
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the del.icio.us site (see figure 3)
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Device cultures It is not only the
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uilt using a camera-equipped PDA ru
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Jones, C., Dirckinck-Holmfeld, L.,
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Milrad, M., & Spikol, D. (2007). An
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components to be able to deal with
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compulsory throughout the project.
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only with their existing day-today
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As our work continues, we will try
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cognition as well as self-regulated
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Mobile mind map tool for stimulatin
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the pictorial knowledge representat
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Ericsson, K. A., & Simon, H. A. (19
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Al-A'ali, M. (2007). Implementation
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complex cognitive skills involve em
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Table 1. CAT and a linear mathemati
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It is agreed that the difficulty le
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Figure 7. Our newly added factors i
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question factors according to IRT;
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Instructure 1 Login User-ID Passwor
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Shute, V., & Towle, B. (2003). Adap
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distinguish between partial knowled
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esponse is identified, then the sco
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are stored in the answer record and
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2 (7) MNSQ = ∑WniZ ni ∑ Wni =
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in the course. A randomized block d
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To test whether ET can effectively
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course, more comparison tests could
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Fleischmann, K. R. (2007). Standard
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educational standards in practice t
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laboratory activities, which are bu
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process is currently a largely top-
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Hsu, Y.-S., Wu, H.-K., & Hwang, F.-
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evolution with computing technology
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Resources 0.55 0.72 e17 In my schoo
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teachers’ instructional evolution
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Regression models indicating relati
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of beliefs about integrating comput
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Sinko, M., & Lehtinen, E. (1999). T
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concept of social support. Lastly,
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Procedure Content analysis procedur
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and mice are out of order. I have t
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feature of supportive online groups
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Anderson, J., & Lee, A. (1995). Lit
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Schwab, R. L., Jackson, S. E., & Sc
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Investigating the problem An extens
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meets the anywhere, anytime require
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minimise housekeeping tasks and fre
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3. Join Group Discussion 4. Attend
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Figure 4. Learning Shell showing He
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engineering the Learning Shell so i
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Olfos, R., & Zulantay, H. (2007). R
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portion of CourseInfo requires that
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Table 2. Assignment evaluation resp
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E-3c. Likert scale on usability: Th
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organized in four groups, namely: T
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The correlations were not high enou
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Data illustrate some reliability an
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nature, some specific core objectiv
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Frederiksen, J.R., & Collins, A. (1
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Bottino, R. M., & Robotti, E. (2007
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The Text Editor allows the editing
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Mathematical content and structure
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Consolidation exercises Solution co
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As to course components, the activi
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handle more formal representations
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Lagrange, J. B., Artigue, M., Labor
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Page 217 and 218: Initial Design Because the field-ba
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Page 229 and 230: Wang, H.-Y., & Chen, S. M. (2007).
Page 231 and 232: If the universe of discourse U is a
Page 233 and 234: we can see that S( X ) − S( Y ) S
Page 235 and 236: Example 1: Let Ã and B ~ be two va
Page 237 and 238: columns shown in Table 2, where 1
Page 239 and 240: (3) Find the maximum value among th
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Page 249 and 250: enabling learner (subject) to work
Page 251 and 252: • Self-, Peer- and Collaborative-
Page 253 and 254: (iii) Regulates the communication:
Page 255 and 256: Figure 3. A screen shot of the SCAL
Page 257 and 258: 2 nd study: Thirty-five students pa
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Page 261 and 262: Soller, A. (2001). Supporting Socia
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Page 265 and 266: Solution 2.2: Deliberately select h
Page 267 and 268: just near a deadline, when it may b
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Page 273 and 274: Panitz, T., & Panitz, P. (1998). En
Page 275 and 276: Concept Mapping Concept mapping by
Page 277 and 278: Research Questions In order to dete
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Page 281 and 282: Tukey’s HSD post hoc test reveale
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Page 285 and 286: Jegede, O.J., Alaiyemola, F.F., & O
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• Use drawing to develop writing
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student and teacher on the synchron
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Vogel, J. J., Vogel, D. S., Cannon-
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Cases The first set of ten case stu
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Kılıçkaya, F. (2007). Website re
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