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

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Marshall, C., & Rossman, G. (1995). Designing qualitative research, Newbury Park, CA: Sage. Morrison, G. R., Ross, S. M., & Kemp, J. E. (2004). Designing effective instruction (4 th Ed.), New York: John Wiley. Nelson, W. (2003). Learning by design. In D. S. Knowlton & D. C. Sharp (Eds.), Problem-based learning in the information age, San Francisco: Jossey-Bass, 39-44. Nelson, W., & Knowlton, D. S. (2005). “Learning through Design” as a strategy for faculty development: Lessons learned in a teacher education setting. In M. Orey, J. McClendon, & R. M. Branch (Eds.), Educational media and technology yearbook (vol. 30), Westport: Libraries Unlimited, 29-36. O'Grady, P. J., Righy, P., & Van den Hengel, J. W. (1987). Hermeneutics and the method of social science. American Psychologist, 42 (2), 194. Patton, M. Q. (1990). Qualitative evaluations and research methods (Rev. Ed.), Newbury Park, CA: Sage. Reiser, R. A. (2001). A history of instructional design and technology: Part 1: A history of instructional media. Educational Technology Research and Development, 49 (1), 53-64. Scanlon, P. A., & Ford, M. P. (1998). Grading student performance in real-world settings. In R. S. Anderson & B. W. Speck (Eds.). Changing the way we grade student performance: Classroom assessment and the new learning paradigm, San Francisco: Jossey-Bass, 97-105. Stokes, T., & Richey, R. C. (2000). Rapid prototyping methodology in action: A developmental study. Educational Technology Research and Development, 48 (2), 63-80. Weber, A. (1996). Professional development schools and university laboratory schools: Is there a difference? The Professional Educator, 18 (2), 59-65. Weiss, R. E. (2000). Humanizing the online classroom. In R. E. Weiss, D. S. Knowlton, & B. W. Speck (Eds.), Principles of effective teaching in the online classroom, San Francisco: Jossey-Bass, 47-52. Wiggins, G., & McTighe, J. (2005). Understanding by design (2 nd Ed.). Alexandria: Association for Supervision and Curriculum Development. 223
Wang, H.-Y., & Chen, S. M. (2007). Artificial Intelligence Approach to Evaluate Students’ Answerscripts Based on the Similarity Measure between Vague Sets. Educational Technology & Society, 10 (4), 224-241. Artificial Intelligence Approach to Evaluate Students’ Answerscripts Based on the Similarity Measure between Vague Sets Hui-Yu Wang Department of Education, National Chengchi University, Taiwan // 94152514@@nccu.edu.tw Shyi-Ming Chen Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan // Tel: +886-2-27376417 // smchen@mail.ntust.edu.tw ABSTRACT In this paper, we present 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 ui in the universe of discourse U belonging to a vague set is represented by a vague value. The grade of membership of u i in the vague set Ã is bounded by a subinterval [tÃ(u i), 1 – f Ã (u i)] of [0, 1]. It indicates that the exact grade of membership μ Ã(u i) of u i belonging the vague set Ã is bounded by t Ã(u i) ≤ μ Ã(u i) ≤ 1 – f Ã(u i), where t Ã(u i) is a lower bound of the grade of membership of u i derived from the evidence for ui, f Ã(u i) is a lower bound of the negation of u i derived from the evidence against u i, t Ã(u i) + f Ã(u i) ≤ 1, and u i∈U. An index of optimism λ determined by the evaluator is used to indicate the degree of optimism of the evaluator, where λ ∈ [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. Especially, they are particularly useful when the assessment involves subjective evaluation. The proposed methods can evaluate students’ answerscripts more stable than Biswas’s methods (1995). Keywords Similarity functions, Students’ answerscripts, Vague grade sheets, Vague membership values, Vague sets, Index of optimism Introduction In recent years, some methods have been presented for students’ evaluation (Biswas, 1995; Chang & Sun, 1993; Chen & Lee, 1999; Cheng & Yang, 1998; Chiang and Lin, 1994; Frair, 1995; Echauz & Vachtsevanos, 1995; Hwang, Lin, & Lin, 2006; Kaburlasos, Marinagi, & Tsoukalas, 2004; Law, 1996; Ma & Zhou, 2000; Liu, 2005; McMartin, Mckenna, & Youssefi, 2000; Nykanen, 2006; Pears, Daniels, Berglund, & Erickson, 2001; Wang & Chen 2006a; Wang & Chen, 2006b; Wang & Chen, 2006c; Wang & Chen, 2006d; Weon & Kim, 2001; Wu, 2003). Chang and Sun (1993) presented a method for fuzzy assessment of learning performance of junior high school students. Chen and Lee (1999) presented two methods for evaluating students’ answerscripts using fuzzy sets. Cheng and Yang (1998) presented a method for using fuzzy sets in education grading systems. Chiang and Lin (1994) presented a method for applying the fuzzy set theory to teaching assessment. Frair (1995) presented a method for student peer evaluations using the analytic hierarchy process method. Echauz and Vachtsevanos (1995) presented a fuzzy grading system to translate a set of scores into letter grades. Hwang, Lin and Lin, (2006) presented an approach for test-sheet composition with large-scale item banks. Kaburlasos, Marinagi, and Tsoukalas (2004) presented a software tool, called PARES, for computer-based testing and evaluation used in the Greek higher education system. Law (1996) presented a method for applying fuzzy numbers in education grading systems. Liu (2005) presented a method for using mutual information for adaptive item comparison and student assessment. Ma and Zhou (2000) presented a fuzzy set approach for the assessment of student-centered learning. McMartin, Mckenna and Youssefi (2000) used scenario assignments as assessment tools for undergraduate engineering education. Nykanen (2006) presented inducing fuzzy models for student classification. Pears, Daniels, Berglund, and Erickson (2001) presented a method for student evaluation in an international collaborative project course. Wang and Chen (2006a) presented two methods for students’ answerscripts evaluations using fuzzy sets. Wang and Chen (2006b) presented two methods for evaluating students’ answerscripts using fuzzy numbers associated with degrees of confidence. Wang and Chen (2006c) presented two methods for students’ answerscripts evaluation using vague sets. Weon and Kim (2001) presented a leaning achievement evaluation strategy in student’s learning procedure using fuzzy membership ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 224
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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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Page 179 and 180: Bottino, R. M., & Robotti, E. (2007
Page 181 and 182: The Text Editor allows the editing
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Page 185 and 186: Consolidation exercises Solution co
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Page 195 and 196: Method Participants The participant
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Page 217 and 218: Initial Design Because the field-ba
Page 219 and 220: • reducing the number of required
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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
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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
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with their teacher but rather than
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Tukey’s HSD post hoc test reveale
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collaboratively during study time?
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Jegede, O.J., Alaiyemola, F.F., & O
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esources. The use of synchronous te
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The use of digital communication mo
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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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