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The frequency distribution of all the participants’ score was used to divide the participants into three groups of readers. The highest frequency falls in the two intervals, 101-110 (8 students) and 151-160 (8 students). These two intervals serve as the benchmarks for dividing the participants into three groups of readers. Participants with reading scores above 151 were identified as the more-proficient readers and those with reading scores below 101 were the less-proficient readers. Participants with reading scores between 101 and 151 were identified as the average readers. Thus, 29 more-proficient readers, 32 average readers, and 31 less-proficient readers were identified in this study. The more-proficient readers showed a mean score of 175.52 with a standard deviation of 14.60, the average readers a mean score of 126.41 with a standard deviation of 15.09, and the less-proficient readers a mean score of 74.03 with a standard deviation of 17.53. Material The online referential resolution practice used four texts to examine the participants’ reading comprehension. The four texts were selected from College Reading Workshop (Malarcher, 2005) based on the following four criteria: a number of references for reading practice, similar length, similar readibility level, texts written for EFL college students. The four texts were Ideas about Beauty (number of words: 582; number of referring words: 25; Text 1), Fast Food and Teen Workers (number of words: 583; number of referring words: 25; Text 2), Adventure Tours for Charity (number of words: 599; number of referring words: 43; Text 3), and Traditional Markets vs. Modern Markets (number of words: 577; number of referring words: 31; Text 4). The full text of Ideas about Beauty (text 1) is shown in Appendix. Procedures of Data Collection Figure 2. Feedback tool requested by a student Three phases were involved in the procedure of data collection. In the first phase, 92 college students received reading instruction in referential identification and resolution. In this instruction, types of reference and usage of the referring strategy were provided. This allowed students to have opportunities to practice the strategy. Three types of references, personal, demonstrative, and locative references, were investigated in this study as they appeared more frequently in texts ((Fortanet, 2004). Personal reference refers to individuals or objects by specifying their functions 285
or roles in a context, such as “I,” “me,” and “you.” Demonstrative references substitute nouns when the nouns can be understood from the context. They also indicate whether they are replacing singular or plural words. Examples include “this,” “these,” “that,” “more,” “neither,” etc. Locative references are used to indicate locations. Examples include “here” and “there.” In the second phase, students were introduced the online system of referential resolution. They were demonstrated how to use the online system and offered chances to practice it. For example, when students encountered difficulties in constructing their initial maps, they could request feedback by clicking an icon in the tool bar. Figure 2 shows the feedback received by a student. The three candidate references were highlighted for the student to make a second attempt at the correct answer. Then, the student connected the chosen reference to construct the mental map (see Figure 3). In the third phase, participants were asked to complete the online task in class during the period between October 2 nd , 2006 and January 3 th , 2007. The online system of referential resolution required the student to follow the steps in reading an online text: (1) identify references, (2) draw relationships between the references, and (3) answer reading comprehension test items. There were ten items in each comprehension test and the full score points of each test were 10. Finally, the system recorded participants’ reading behavior and performance. Procedures of Data Analysis Figure 3. The mental map the student drew after feedback The collected data were categorized into reading product and reading process. In reading product, students’ score points in referential identification, referential resolution, the frequency of feedback tool request, and reading comprehension test were analyzed by Statistical Package for Social Science (SPSS) 14.0 version. Mean, standard deviation, and Pearson product-moment correlation coefficient were computed to examine students’ performance on the referential resolution task and to investigate the relationships among referential identification, referential resolution, and reading comprehension test. In reading process, participants’ mental maps of referential resolution, the trace results of their reading process, and the frequency of feedback tool request were examined. 286
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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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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 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
Page 279 and 280: The observed pairs were aware of be
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: 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
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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
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expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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