April 2012 Volume 15 Number 2 - Educational Technology & Society

More documents

Recommendations

Info

* � P( X � 0, X � 1) is the probability of violating the ordering relationship of B A � ; that is directly related to the AB � A B prediction error. From the definition and explanation of IRS in Takeya (1991), * r is designed to be a coefficient that AB * has the benefits of both � and the correlation coefficient of items A and B. However, this modification reduces the AB direct relationship with the prediction error and affects the performance of IRS. The definition of Diagnosys, * � AB � f / f shows that the error frequency AB AB f is divided by f , and this cause the relationship AB AB between * � AB and the prediction error is reduced. From these observations, it is reasonable that OT has the best performance. The performance of knowledge structure-based adaptive testing (KSAT) is affected by the correct guessing. Two methods, most difficult item method (MDI) and prerequisite item (PI) method, were proposed to deduce the possibilities of guessing. The experimental results show that both methods can improve the effect of correct guessing and have better performances than original methods. Since OT has the best performance, it was selected to implement the KSAT system. The performance of the KSAT system shows that under the 93 percent prediction accuracy, the use of test items is 11.42. That is, on average, students need only complete one third of 35 items in the original paper-based exam when the KSAT system is applied. This result is close to that of the simulation study in experiment 1 and shows that the simulation process adopted in this study is valid and suitable. From the above discussions, this study has three contributions. First, some evaluation methods for KSAT algorithms were applied to find the best adaptive testing algorithm among domain experts, OT, IRS, and Diagnosys, and OTbased KSAT algorithm has the best and stable performance. Second, two methods, the most difficult item method (MDI) and the prerequisite item (PI) method were proposed to improve the effect of correct guessing in KSAT algorithms. Finally, an OT-based adaptive testing system was developed and evaluated. Upon completion of the adaptive test, a diagnosis profile about the student’s state of learning or understanding was provided to do the subsequent actions, such as tailored instruction or remediation in applied educational settings. Another two directions are considered in the future study. First, OT, IRS, and Diagnosys were used to analyze the ordering relationship of dichotomous items, those methods for polytomous items will be considered in the next step. The second is to develop constructed response items and their automatic scoring mechanism to enhance the function of the KSAT system. Acknowledgement The authors would like to thank the National Science Council of the Republic of China and the National Taichung University of Education for financially supporting this research under contract numbers: NSC-92-2521-S-142-003, NSC 97-2511-S-142-004, NSC 98-2410-H-142-005-MY2, NSC-100-2410-H-656-007 and 98T202-3. References Airasian, P.W., & Bart, W.M. (1973). Ordering theory: A new and useful measurement model. Journal of Educational Technology, 5, 56−60. Appleby, J., Samuels, P., & Treasure-Jones, T. (1997). Diagnosys—A knowledge-based diagnostic test of basic mathematical skills. Computers Education, 28(2), 113−131. Bart, W.M., & Krus, D.J. (1973 ). An ordering theoretic method to determine hierarchies among items. Educational and Psychological Measurement, 33, 291−300. Chang, S.H., Lin P.C., & Lin Z.C. (2007). Measures of partial knowledge and unexpected responses in multiple-choice tests. Educational Technology & Society, 10(4), 95−109. Chang, K.E., Liu, S.H., & Chen, S.W. (1998). A testing system for diagnosing misconceptions in DC electric circuits. Computers and Education, 31, 195−210. Gagne, R.M. (1977). The Conditions of Learning. Holt, Rinehart & Winston, New York, 3 rd ed. Guzman, E., & Conejo, R. (2005). Self-assessment in a feasible, adaptive web-based testing system. IEEE Transactions on Education, 48(4), 688−695. 87
Hwang, G.J., Hsiao, C.L., & Tseng, C.R. (2003). A computer-assisted approach to diagnosing student learning problem in science course. Journal of Information Science and Engineering, 19(2), 229−248. Lewis,C., & Sheehan, K.(1990).Using Bayesian decision theory to design a computer mastery test. Applied Psychological Measurement, 14, 367−386. Liu, C.L. (2005).Using mutual information for adaptive item comparison and student assessment. Educational Technology & Society, 8(4), 100−119. Sands, W.A., Water, B.K., & McBride, J.R. (Eds.). (1997). Computerized adaptive testing: From inquiry to operation. Washington, DC:American Psychological Association. Sheehan, K., & Lewis, C. (1992). Computerized mastery testing with nonequivalent testlets. Applied Psychological Measurement, 16, 65−76. Takeya,M. (1991). New item structure theorem. Tokyo: Waseda University. Tao, Y.H., Wu, Y.L., & Chang, H.Y. (2008). A practical computer-adaptive testing model for small-scale scenarios. Educational Technology & Society, 11(3), 259−274. Tatsuoka, K. K., Corter, J. E. & Tatsuoka, C. (2004). Patterns of diagnosed mathematical content and process skills in TIMSS-R across a sample of 20 countries. American Educational Research Journal, 41(4), 901−926. Tsai, C.C., & Chou, C. (2002). Diagnosing students’ alternative conception in science. Journal of Computer Assisted Learning, 18, 157−165. Tselios, N., Stoica, A., Maragoudakis, M., Avouris, N., & Komis,V. (2006). Enhancing user support in open problem solving environments through Bayesian network inference techniques. Educational Technology & Society, 9(4),150−165. van der Linden, W.J. (2000). Constrained adaptive testing with shadow tests. In W.J. van der Linden & C.A.W. Glas (Eds.), Computer-adaptive testing: Theory and practice (pp. 27−52). Boston: Kluwer. Vomlel, J. (2004). Building adaptive tests using Bayesian networks. Kybernetika, 40(3), 333−348. Wainer, H. (2000). Computerized adaptive testing: a primer (2nd ed). NJ: Lawrence Erlbaum Associates. Yan, D., Almond, R. G., & Mislevy, R. J. (2004). Comparisons of cognitive diagnostic models (ETS Research Report RR-04-02). Princeton, NJ: Educational Testing Service. Yen, Y.C., Ho, R.G., Chen, L.J., Chou, K.Y., & Chen, Y.L. (2010). Development and evaluation of a confidence-weighting computerized adaptive testing. Educational Technology & Society, 13(3), 163–176. Yu, S.C., & Yu, M.N. (2006). The relationships among indices of diagnostic assessment of knowledge Structure and S-P chart analysis. Journal of Education & Psychology, 29(1), 183–208. 88
Page 1 and 2:
April 2012 Volume 15 Number 2
Page 3 and 4:
Supporting Organizations Centre for
Page 5 and 6:
Contextualizing a MALL: Practice De
Page 7 and 8:
Kop, R. (2012). The Unexpected Conn
Page 9 and 10:
Human mediation and information flo
Page 11 and 12:
elevant information based on some k
Page 13 and 14:
of learners. A search facilitated t
Page 15 and 16:
information as human mediation mean
Page 17 and 18:
Blau, I., & Barak, A. (2012). How D
Page 19 and 20:
examined whether the readiness to p
Page 21 and 22:
Table 5 presents the ANOVA for the
Page 23 and 24:
However, in discussing a non-sensit
Page 25 and 26:
deviations showed smaller variance,
Page 27 and 28:
actual participation discussing sen
Page 29 and 30:
Mesch, G., & Elgali, Z. (2009). Soc
Page 31 and 32:
Although prior research on overall
Page 33 and 34:
with a score of 39 and below as “
Page 35 and 36:
Table 2 demonstrates mean scores an
Page 37 and 38:
Discussion Although the recent Inte
Page 39 and 40:
Furthermore, the most preferred pla
Page 41 and 42: MEB. (2009). Internete erisim proje
Page 43 and 44: Learning Management Systems (LMS) b
Page 45 and 46: practice and repetition with feedba
Page 47 and 48: Online peer assessment Peer assessm
Page 49 and 50: 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
Page 71 and 72: Encourage and persuade students wit
Page 73 and 74: Materials and procedure Each studen
Page 75 and 76: The lower part of Figure 4 shows st
Page 77 and 78: Mayer, R. E., Sobko, K., & Mautone,
Page 79 and 80: that an item is answered correctly
Page 81 and 82: Table 1. Relative concepts frequenc
Page 83 and 84: The Account Management Module provi
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: 0.065 0.877 (17.56) 0.938 (22.55) 0
Page 95 and 96: the diverse needs of modern industr
Page 97 and 98: Figure 1. The framework of interdis
Page 99 and 100: Research Design Figure 3. Recommend
Page 101 and 102: friendly. 8. I feel the layout and
Page 103 and 104: satisfaction, and system acceptance
Page 105 and 106: Ozok, A. A., Fan, Q., & Norcio, A.
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
Page 119 and 120: Sessink, O., Beeftink, H., Tramper,
Page 121 and 122: The rationale behind the video form
Page 123 and 124: usefulness of the movies and their
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
Page 133 and 134: efficacy regarding online training,
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
Page 151 and 152:
Democratizing knowledge and symmetr
Page 153 and 154:
1) Reading the article: At the star
Page 155 and 156:
Effects of RCKI principle-based ped
Page 157 and 158:
References Alexander, C. (1979). Th
Page 159 and 160:
Valsamidis, S., Kontogiannis, S., K
Page 161 and 162:
The Analog system (Yan et al., 1996
Page 163 and 164:
First, the number of the sessions a
Page 165 and 166:
BioLayout uses a modified version o
Page 167 and 168:
As shown in figure 3, all metrics c
Page 169 and 170:
Figure 7. The detailed results for
Page 171 and 172:
Enright, A. J., van Dongen, S., Ouz
Page 173 and 174:
Chu, S. K. W., Kwan, A. C. M., & Wa
Page 175 and 176:
teachers, as well as collaboration
Page 177 and 178:
from the students’ ratings appear
Page 179 and 180:
Table 5 summarizes the students’
Page 181 and 182:
eading blogs (i.e., reminders, easy
Page 183 and 184:
Luehmann, A. L. (2008). Using blogg
Page 185 and 186:
Uzunboylu et al. (2009) examined th
Page 187 and 188:
System implementation Overview of M
Page 189 and 190:
Methods Before the experiment, a mo
Page 191 and 192:
too simple, incomplete, or they eve
Page 193 and 194:
Comparisons of UMLS Questionnaire i
Page 195 and 196:
already described may play an impor
Page 197 and 198:
Parker, D., Manstead, A. S. R., Str
Page 199 and 200:
Avci, U., & Askar, P. (2012). The C
Page 201 and 202:
Literature review The related varia
Page 203 and 204:
collected through Personal Informat
Page 205 and 206:
Self-efficacy 92 5 21 13.37 3.726 W
Page 207 and 208:
In this problem, intention was take
Page 209 and 210:
References Ajjan, H., & Hartshorne,
Page 211 and 212:
Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
Page 213 and 214:
Students perused the course materia
Page 215 and 216:
problem. All articles were then sen
Page 217 and 218:
tests, electronic tests are more li
Page 219 and 220:
Students 10 and 11: My classmate Ed
Page 221 and 222:
collection was also approved by all
Page 223 and 224:
Ubiquitous Revision Seamless Collab
Page 225 and 226:
Tai, Y. (2012). Contextualizing a M
Page 227 and 228:
tasks were developed from task type
Page 229 and 230:
experiences in this phase. Moreover
Page 231 and 232:
test is administered again right af
Page 233 and 234:
The collected data were analyzed wi
Page 235 and 236:
Gorp, K. V., & Bogaert, N. (2006).
Page 237 and 238:
4PL IRT model According to the numb
Page 239 and 240:
examinee’s ability more accuratel
Page 241 and 242:
would be more accurate if items j+1
Page 243 and 244:
C�I or Change I�I) would be 0.2
Page 245 and 246:
improved by rearrangement procedure
Page 247 and 248:
Table 6. Repeated Measures ANOVA of
Page 249 and 250:
Leppisaari, I., & Lee, O. (2012). M
Page 251 and 252:
Selection of the virtual tool The t
Page 253 and 254:
Environmental themes emerged strong
Page 255 and 256:
Visualization of text (visual langu
Page 257 and 258:
environments where visual technolog
Page 259 and 260:
- Logging in to learning environmen
Page 261 and 262:
McNaught, C., Lam, P. & Lam, S.L. (
Page 263 and 264:
on-site to finally achieve a meanin
Page 265 and 266:
Table 1. Digital game designers and
Page 267 and 268:
worldviews are socio-cultural-histo
Page 269 and 270:
Phase Methods Visitor data collecti
Page 271 and 272:
operate the game without the help o
Page 273 and 274:
Visitors (end users) must be heard.
Page 275 and 276:
Islas Sedano, C., Pawlowski, J., Su
Page 277 and 278:
part of something larger than the s
Page 279 and 280:
Previous studies have developed rel
Page 281 and 282:
Technology Acceptance. The 10 items
Page 283 and 284:
Table 4. Model Fit Indices Model χ
Page 285 and 286:
In the final path model of set 1, t
Page 287 and 288:
Discussion Differing from prior stu
Page 289 and 290:
Haythornthwaite, C., Kazmer, M. M.,
Page 291 and 292:
Ge, Z.-G. (2012). Cyber Asynchronou
Page 293 and 294:
college of a university situated in
Page 295 and 296:
Table 3 shows us the following info
Page 297 and 298:
Appendix B shows Class 1’s respon
Page 299 and 300:
increase one’s ability to process
Page 301 and 302:
Appendix A Responses concerning syn
Page 303 and 304:
Shih, S.-C., Kuo, B.-C., & Liu, Y.-
Page 305 and 306:
Adaptive U-learning Math Path Syste
Page 307 and 308:
Figure 4. Online tutorial courses o
Page 309 and 310:
Participants and Experimental Proce
Page 311 and 312:
the influence of Test1 scores, an e
Page 313 and 314:
Hall, T., & Bannon, L. (2006). Desi
Page 315 and 316:
(Hofer & Pintrich, 1997; Liu, Lin &
Page 317 and 318:
� Through online peer assessment,
Page 319 and 320:
elativism.” High Internet self-ef
Page 321 and 322:
Peng, H., Tsai, C.-C., & Wu, Y.-T.
Page 323 and 324:
Since game-based learning has been
Page 325 and 326:
The second reason is about the sust
Page 327 and 328:
Methods Figure 5. Quest NPCs provid
Page 329 and 330:
These differences indicated that pa
Page 331 and 332:
quests further involves students’
Page 333 and 334:
Chang, I.-H. (2012). The Effect of
Page 335 and 336:
(1) Vision, planning and management
Page 337 and 338:
Based on an examination of the lite
Page 339 and 340:
Table 2. Analysis of reliability an
Page 341 and 342:
λ10 - 0.69 0.83 ε5 14.50 * - 0.32
Page 343 and 344:
Bailey, G. D. (1997). What technolo
Page 345 and 346:
Stegall, P. (1998). The principal:
Page 347 and 348:
instruction has studied the student
Page 349 and 350:
espond and the estimation of possib
Page 351 and 352:
Research question For the purpose o
Page 353 and 354:
READI reliability To verify the con
Page 355 and 356:
Physical -1.43333(*) .44699 .029 -2
Page 357 and 358:
The results of this study suggest t
Page 359 and 360:
Hsu, Y.-C., Ho, H. N. J., Tsai, C.-
Page 361 and 362:
domains. Five research questions we
Page 363 and 364:
Research sample groups (4) Faculty
Page 365 and 366:
13. Policies, Social Culture Impact
Page 367 and 368:
Non-Specified Others Engineering &
Page 369 and 370:
(AR = -2.1) (n = 7) 2. Non-specifie
Page 371 and 372:
(n): Total number of articles Resea
Page 373 and 374:
Compared to the publications in 200
Page 375:
Pedagogical Content Knowledge (TPAC
show all

April 2012 Volume 15 Number 2 - Educational Technology & Society

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?