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Result Reading product In reading product, students’ scores in referential identification and resolution were shown in percentage since the number of referential words is different in each text. For each reading comprehension test, the full score points are 10. The means and standard deviations of referential identification, resolution, and reading comprehension test are shown in Table 1. Variable Referential identification Referential resolution Reading comprehension Text Table 1. Descriptive statistics of readers’ performance in the system Text 1 Text 2 Text 3 Text 4 Group N Mean SD Mean SD Mean SD Mean SD MR 29 22.93 1.25 23.72 1.49 40.10 2.76 49.72 1.33 AR 32 21.16 2.81 20.56 1.41 38.16 3.06 38.72 2.30 LR 31 19.93 1.75 18.26 1.48 24.00 4.55 24.71 4.21 MR 29 21.59 1.86 21.86 .99 42.97 6.63 49.69 3.39 AR 32 15.34 3.09 18.59 1.24 32.72 5.50 36.28 1.40 LR 31 13.13 2.09 14.19 2.27 14.61 3.62 20.19 5.06 MR 29 8.93 .70 8.07 .65 8.83 1.04 9.55 .51 AR 32 6.28 .47 7.78 .42 7.28 .89 8.19 .69 LR 31 5.35 .49 6.45 1.12 5.35 .75 6.32 1.51 Note: N refers to the number of participants; SD refers to standard deviation; MR refers to the more proficient readers; AR refers to average readers; LR refers to less proficient readers; the means of referential identification and resolution are shown in percentage. As shown in Table 1, all three groups of readers made progress as they read the four sequential online texts. The more-proficient readers outperformed the average and the less-proficient ones as their mean scores in referential identification task ranging from 22.93 to 49.72. In contrast, the mean of the referential identification task for the lessproficient readers only ranges from 21.16 to 38.72. Furthermore, the mean of reading comprehension test for the more-proficient readers ranges from 8.07 to 9.55 whereas the mean for the average readers is from 6.28 to 8.19. REFER_RE 60 55 50 45 40 35 30 25 20 15 10 5 15 20 REFER_ID 25 30 35 40 45 TEXT Figure 4. The relationship between referential identification and resolution Text 4 Text 3 Text 2 Text 1 287
Correlation coefficient was also conducted to examine the nature of the relationship between the variables in this study. The correlation coefficient for the referential identification and referential resolution for text 1, 2, 3, and 4 are .81, .84, .85, and .84 which show a positive relationship between the two variables (see Figure 4). That is, as readers’ scores in referential identification increased, their scores in referential resolution also raised. Furthermore, the correlation coefficient between referential resolution and reading comprehension test for four texts are .68, .71, .94, and .90 which also indicate a positive relationship. As readers’ scores in referential resolution increased, their scores in reading comprehension test were also raised. Reading process In the following, one student was randomly selected from each reading proficiency group, a total of 3 students, to represent the developmental process of more proficient, average, and less proficient readers in referential resolution and reading comprehension. Reading process of the more-proficient readers In this study, the more-proficient readers were found to be able to integrate textual information in four sequential online reading tasks. Figure 5 shows one of the examples. Figure 5. The more-proficient reader’s mental representation of personal references In addition to the more-proficient reader’s mental representation of referential resolution, he read and reread the sentences and requested the feedback tool as he was not sure what references referred to. Figure 6 presents one of the more-proficient reader’s reading process in which he requested the feedback tool for assistance. Figure 6 shows that the more-proficient reader constantly requested the feedback tool 21 times (e.g. line 150). He requested the feedback tool either for overcoming his reading difficulties or confirming his selection. For instance, after he read the sentences, he requested the feedback tool of this (12) (e.g. lines 21~23). Then, he read and reread the sentences and finally made a correct selection. The more-proficient reader also requested the feedback tool as he tried to confirm the answer he chose. For example, after he connected the men (23) to its subject (e.g. lines 150~153), he checked the connection again by requesting the feedback tool for 4 times. Then, he revised his connection by cutting the previous selection. Table 2 further presents the more-proficient reader’s frequency request of the feedback tool for resolving personal references. It was found that the more-proficient reader increased his request of feedback tool in resolving personal references in four texts. 288
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April 2009 Volume 12 Number 2
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Abstracting and Indexing Educationa
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Engaging students in multimedia-med
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this study discusses computer-based
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Sampling for preliminary study One
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Part 2 Pearson correlation .349** 1
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Figure 1. Box and whisker plot of t
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characteristics and learning styles
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However, critical reflection and in
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novels, so they collected any relat
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1. Theories of teaching: Comments m
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grammatical errors and basic writin
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Ho, B., & Richards, J. C. (1993). R
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Hwang, K.-A., & Yang, C.-H. (2009).
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e deduced from the Affective Domain
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Detection procedure Figure 1 shows
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avoided. Image processing is perfor
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falling asleep) as defined in this
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classmates or left their seats duri
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help teachers to recognize the lear
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Hsieh, P.-H., & Dwyer, F. (2009). T
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significant comprehension effects o
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Treatment 3 (keyword group): Studen
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Criteria of achievement measures Th
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esults. A correlational analysis de
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A 2 x 1 ANOVA analyzed the effect o
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References Aragon, S. R. (2004). In
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Raphael, T. (1982). Improving quest
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educing maintenance costs. The cont
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Synchronization functions According
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inconsistency) and navigate directl
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the suitability of the instructiona
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Design of manipulation process The
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Table 1: Comparative analysis of au
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the convenience of mobile-based ass
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According to evaluation result, the
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Tan, O. S., Parsons, R. D., Hinson,
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At a deeper level, though, there wa
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theoretical assumptions. Two theore
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The students were not going to have
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slightly different. The group who c
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every item, not giving any result.
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edited and presented to the class u
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Figure 10. Screen captures from the
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Some of the concerns that emerge fr
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Ajayi, L. (2009). An Exploration of
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As a result of the confluence of di
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peers (Schellens et al. 2005). Sche
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The integration of discussion board
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ecause the technology allowed the p
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The pre-service teachers perceived
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Furthermore, the study demonstrated
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Schellens, T., van Keer, H., & Valc
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McKean, 2003). Instructors will see
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program at The Ohio State Universit
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Knowledge and skills of developing
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their monthly reflections or assign
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Bull, K., Montgomery, D., Overton,
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frustrating the student). ITSs make
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• Link removal - advanced student
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Expert Module Expert Module’s mai
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Based on the questionnaire results
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students were told that their perfo
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thirds of the interviewed students
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Throughout the semester, students i
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human teacher or question developer
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To test the learning effectiveness
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APPENDIX A Computer Science and Sof
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Thus, the use of computers changes
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Specifically, two objectives were p
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Nonetheless, both teachers assign a
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48% 35% 53% similar similar 69% 42%
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Table 4: Students’ attitudes Stat
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activities. Specifically, the histo
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Huang, Y.-M., Huang, T.-C., Wang, K
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• Could recommended learning sequ
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Analysis of sequences prediction me
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Figure 4. The sorting of learning s
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The figures 5a and 5b illustrate th
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Learning sequence recommendation sy
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In this study, forty subjects were
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The potential for LSRS to promote l
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They pointed out that LSRS helped t
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Cover, T. M., & Thomas, J. A. (1991
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Related Efforts An Internet forum i
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with handheld devices and they were
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agent would direct learners to a le
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Mobile RSS Aggregator In order to s
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learning, such as posting question
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With novel technological support, m
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Shen, L., Wang, M., & Shen, R. (200
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as an ‘affective loop’, which r
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Figure 2 is an example of two-dimen
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preference was gathered through dat
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compute the heart rate (HR) as a fu
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sessions. Of all the 18 learning se
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Burleson, W., Picard, R. W., Perlin
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Wu, C.-C., & Lai, C.Y. (2009). Wire
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The wireless handheld learning envi
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the lengthiness of the scales, whic
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Nursing dictionary We constructed a
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them. The computers were networked
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“The major difference was that I
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questionnaire. We do not consider t
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Faruque, F., Hewlett, P. O., Wyatt,
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etween a user and the system. Addit
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Figure 2. A concept map representin
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C5.0 can help teachers to generate
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that a selected programming exercis
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differences between our system and
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1(students can complete without any
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The PADS sometimes assigned very di
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Corno, L. (2000). Looking at Homewo
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Hwang, W.-Y., Hsu, J.-L., Tretiakov
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listed these functions as allowing
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In the research presented in this a
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Research Tools Web-based learning e
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Table 1. Operational definitions of
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action, interaction, and outeractio
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for them was getting the answer as
Page 241 and 242: Table 6 shows the learners’ prefe
Page 243 and 244: Bell, P., & Winn, W. (2000). Distri
Page 245 and 246: Askar, P., & Altun, A. (2009). CogS
Page 247 and 248: elations, interactions and activiti
Page 249 and 250: Figure 3. A comparison of knowledge
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Page 253 and 254: Figure 7. Visual Representation of
Page 255 and 256: Step 5: Use Boolean to combine the
Page 257 and 258: 2) CogSkillNet provides classroom i
Page 259 and 260: Neo, M., & Neo, T.-K. (2009). Engag
Page 261 and 262: • Conversation and collaboration
Page 263 and 264: IMAGE 2 2. Problem identification I
Page 265 and 266: Methodology At the end of the proje
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Page 271 and 272: Lambert, N. M., & McCombs, B. J. (1
Page 273 and 274: not detected in a previous study, t
Page 275 and 276: et al. (2001) studied the learning
Page 277 and 278: all the students. At the midpoint o
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Page 281 and 282: Table 2 shows the results from a mo
Page 283 and 284: Table 5: The distribution of time (
Page 285 and 286: As discussed in the section on prev
Page 287 and 288: McDowell, C., Werner, L., Bullock,
Page 289 and 290: Comprehension monitoring is the awa
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Page 295 and 296: (a) Text 1 (b) Text 2 (c) Text 3 (d
Page 297 and 298: Table 3 shows that the average read
Page 299 and 300: Figure 10. Trace results of the les
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Page 303 and 304: Conn, S. R., Roberts, R. L., & Powe
Page 305 and 306: The technology-mediated groups, wit
Page 307 and 308: hybrid model of supervision was pos
Page 309 and 310: Our research indicates that the hyb
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Page 313 and 314: goal of using storyboards. But, thi
Page 315 and 316: In general, learning activities nee
Page 317 and 318: Furthermore, free subjects, such as
Page 319 and 320: Storyboarding Roots and related wor
Page 321 and 322: Referees, on the other hand, may wa
Page 323 and 324: Symbols Interpretation Defines a un
Page 325 and 326: Figure 4. Top-level storyboard for
Page 327 and 328: may take subjects such as practical
Page 329 and 330: network security information envir
Page 331 and 332: The concrete procedure and interact
Page 333 and 334: (e.g., for graduation) using such i
Page 335 and 336: Thus, academic education modeling b
Page 337 and 338: Feyer, T., Kao, O., Schewe, K.-D.,
Page 339 and 340: Richards, G. (2009). Book review: T
Page 341 and 342: expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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