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

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From Table 5, we can see that the vague marks of the questions Q.1, Q.2, Q.3 and Q.4 represented by vague sets ~ ~ ~ ~ F , F , F , respectively, where are 1 ~ 2 F and 3 4 F = [0, 0]/0% + [0, 0]/20% + [0, 0]/40% + [0.4, 0.5]/60% + [1, 1]/80% + 1 [0.5, 0.6]/100%, ~ F = [0, 0]/0% + [0, 0]/20% + [0, 0]/40% + [0.4, 0.5]/60% + [0.8, 0.9]/80% + 2 [1, 1]/100%, ~ F = [0, 0]/0% + [0.4, 0.5]/20% + [1, 1]/40% + [0.6, 0.7]/60% + [0.4, 0.5]/80% + 3 [0, 0]/100%, ~ F 4 = [0.8, 0.9]/0% + [0.5, 0.6]/20% + [0.2, 0.3]/40% + [0, 0]/60% + [0, 0]/80% + [0, 0]/100%. [Step 1] According to the standard vague sets E ~ ,V ~ ,G ~ , S ~ ,U ~ ~ ~ and the vague marks F , 1 F , 2 vague values, as shown in Table 6. ~ F , 3 4 ~ F , we can get the Table 6. Vague values of Example 2 t V~ ( t) E V~ ( t) V V~ ( t) G V~ ( t) S V ~ ( t) U V ~ ( t) F1 V~ ( t) F2 V~ ( t) F3 V~ ( t) F4 0 % [0, 0] [0, 0] [0, 0] [0, 0] [1, 1] [0, 0] [0, 0] [0, 0] [0.8, 0.9] 20 % [0, 0] [0, 0] [0, 0] [0.4, 0.5] [1, 1] [0, 0] [0, 0] [0.4, 0.5] [0.5, 0.6] 40 % [0, 0] [0, 0] [0.4, 0.5] [1, 1] [0.4, 0.5] [0, 0] [0, 0] [1, 1] [0.2, 0.3] 60 % [0.4, 0.5] [0.4, 0.5] [1, 1] [0.8, 0.9] [0.2, 0.3] [0.4, 0.5] [0.4, 0.5] [0.6, 0.7] [0, 0] 80 % [0.8, 0.9] [1, 1] [0.8, 0.9] [0.4, 0.5] [0, 0] [1, 1] [0.8, 0.9] [0.4, 0.5] [0, 0] 100 % [1, 1] [0.7, 0.8] [0.4, 0.5] [0, 0] [0, 0] [0.5, 0.6] [1, 1] [0, 0] [0, 0] By applying Eq. (5), we can get scores of the vague values, as shown in Table 7. Table 7. Scores of the vague values of Example 2 t S( V~ ( t)) E S( V~ ( t)) V S( V ~ ( t)) G S( V~ ( t)) S S( V ~ ( t)) U S( V ~ ( t)) F 1 S( V ~ ( t)) F2 S( V ~ ( t)) F3 S( V ~ ( t)) F4 0 % -1 -1 -1 -1 1 -1 -1 -1 0.7 20 % -1 -1 -1 -0.1 1 -1 -1 -0.1 0.1 40 % -1 -1 -0.1 1 -0.1 -1 -1 1 -0.5 60 % -0.1 -0.1 1 0.7 -0.5 -0.1 -0.1 0.3 -1 80 % 0.7 1 0.7 -0.1 -1 1 0.7 -0.1 -1 100 % 1 0.5 -0.1 -1 -1 0.1 1 -1 -1 H(X, Y) Y X Table 8. The degrees of similarity between the vague sets ~ ~ ~ F 1 F F 2 3 F 4 E ~ 0.900 1.000 0.492 0.342 V ~ 0.967 0.942 0.508 0.358 G ~ 0.792 0.742 0.633 0.350 S ~ 0.508 0.458 0.967 0.425 U ~ 0.300 0.250 0.508 0.825 ~ 235
By applying Eq. (7), we can get the degree of similarity H(X, Y) between the vague values X and Y, where ~ ~ ~ ~ F }, as shown in Table 8. X∈{ E ~ ,V ~ ,G ~ , S ~ ,U ~ } and Y ∈{ F 1 , F 2 , F 3 , 3 Because H(V ~ ~ , F 1 ) is the maximum value among the values of H( E ~ ~ , F 1 ), H(V ~ ~ , F 1 ), H(G ~ ~ , F 1 ), H( S ~ ~ , F 1 ) and H(U ~ ~ , F 1 ), we award grade “B” to the question Q.1 due to the fact that the letter grade “B” corresponds to the standard vague set V ~ (very good). Because H( E ~ ~ , F 2 ) is the maximum value among the values of H( E ~ ~ , F 2 ), H(V ~ ~ , F 2 ), H(G ~ ~ , F 2 ), H( S ~ ~ , F 2 ) and H(U ~ ~ , F 2 ), we award grade “A” to the question Q.2 due to the fact that the letter grade “A” corresponds to the standard vague set E ~ (excellent). Because H( S ~ ~ , F 3 ) is the maximum value among the values of H( E ~ ~ , F 3 ), H(V ~ ~ , F 3 ), H(G ~ ~ , F 3 ), H( S ~ ~ , F 3 ) and H(U ~ ~ , F 3 ), we award grade “D” to the question Q.3 due to the fact that the letter grade “D” corresponds to the standard vague set S ~ (satisfactory). Because H(U ~ ~ , F 4 ) is the maximum value among the values of H( E ~ ~ , F 4 ), H(V ~ ~ , F 4 ), H(G ~ ~ , F 4 ), H( S ~ ~ , F 4 ) and H(U ~ ~ , F 4 ), we award grade “E” to the question Q.4 due to the fact that the letter grade “E” corresponds to the standard vague set U ~ (unsatisfactory). [Step 2] Because 90 ≤ A ≤ 100, 70 ≤ B < 90, 30 ≤ D < 50 and 0 ≤ E < 30, where “A”, “B”, “D” and “E” are letter grades, and the index of optimismλ determined by the evaluator is 0.60 (i.e.,λ = 0.60), based on Eq. (12), we can get the following results: K(A) = (1 – 0.60) × 90 + 0.60 × 100 = 96, K(B) = (1 – 0.60) × 70 + 0.60 × 90 = 82, K(D) = (1 – 0.60) × 30 + 0.60 × 50 = 42, K(E) = (1 – 0.60) × 0 + 0.60 × 30 = 18. Because the questions Q.1, Q.2, Q.3 and Q.4 carry 30 marks, 30 marks, 20 marks and 20 marks, respectively, and because H(V ~ ~ , F 1 ) = 0.967, H( E ~ ~ , F 2 ) = 1.000, H( S ~ ~ , F 3 ) = 0.967 and H(U ~ ~ , F 4 ) = 0.825, based on Eq. (11), the total mark of the student is evaluated as follows: 1 (30× 82× 0.967 + 30× 96× 1.000 + 20× 42× 0.967 + 20× 18× 0.825) 100 1 = (2378.82 + 2880 + 812.28 + 297) 100 = 63.681 = 64 (assuming that no half mark is given in the total mark). A Generalized Method for Evaluating Students’ Answerscripts Based on the Similarity Measure between Vague Sets In this section, we present a generalized vague evaluation method (GVEM) for students’ answerscripts evaluation based on the similarity measure between vague sets, where a generalized vague grade sheet shown in Table 9 is used to evaluate the students’ answerscripts. Table 9. A generalized vague grade sheet Question No. Sub-questions Vague mark Derived letter grade Mark Q.1 Q.11 ~ F 11 ~ F g 11 g 12 Q.12 12 m1 236
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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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Page 195 and 196: Method Participants The participant
Page 197 and 198: Table 5. Learning outcomes of diffe
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Page 201 and 202: Kayes, D.C. (2005). Internal validi
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Page 211 and 212: Downes, S. (2003). Design and Reusa
Page 213 and 214: Appendix A. Good-enough approaches
Page 215 and 216: Rapid Prototyping and Design Based
Page 217 and 218: Initial Design Because the field-ba
Page 219 and 220: • reducing the number of required
Page 221 and 222: that they felt this hindrance; I fe
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Page 227 and 228: Edelson, M. (1988). The hermeneutic
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
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Page 243 and 244: Q.2 carries 25 marks, Q.3 carries 2
Page 245 and 246: the proposed methods can evaluate s
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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
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Page 271 and 272: - and even appreciated - by student
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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contributed more information and en
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3. Use the second screen for the le
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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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