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The data used for statistical analysis was collected from two sources: (1) the results of the post-test scores, and (2) the results of the three sub-categories in the post-test. The reliability analysis of the post-test showed internal consistency of the test scores. By using Cronbach’s coefficient alpha, we reported an overall reliability of .88 for this study. Specific reliability for 16 items in Part 1: multiple-choice was .86, and for the six items in Part 2: matching, it was .71 Statistical data analysis To test significant differences in learning achievement among the treatments (computer-assisted instruction versus computer and video game playing), a one-way multivariate analysis of variance (MANOVA) was performed. Multiple analysis of variance in Statistical Package for the Social Sciences (SPSS) was used to examine main and potential interaction effects of categorical variables on multiple interval/ratio dependent variables. That is, MANOVA tests for population group differences on several dependent variables simultaneously by creating vectors of the multiple dependent variables (Garson, 2005; Yoder, 2002). Thus, in conducting a one-way MANOVA for this study, we used various types of instructional treatments as independent variables (i.e., nominal scales); the outcome of students’ multiple-measure scores of the post-test were the dependent variables (i.e., interval/ratio scales). An alpha level of .05 was set to analyze the significant difference of hypotheses. Prior to using MANOVA, the researcher examined the test results and found the distribution to be fairly normal with no extreme outliers. From the results of the post-test (see Table 1), group means showed significant differences between the web CAI group (M = 70.05) and the video game group (M = 76.18). Table 1. Descriptive statistics results of post-test Treatment groups Mean Std. deviation N Part 1 (0–64 points) Web CAI 48.8421 8.27170 57 Video game 52.2353 6.90098 51 Total 50.4444 7.80766 108 Part 2 (0–18 points) Web CAI 10.7368 4.70762 57 Video game 11.7059 4.08066 51 Total 11.1944 4.42900 108 Part 3 (0–18 points) Web CAI 10.4737 3.85035 57 Video game 12.2353 3.83452 51 Total 11.3056 3.92561 108 Post-test total score (0–100 points) Web CAI 70.0526 12.96068 57 Video game 76.1765 10.76978 51 Total 72.9444 12.31049 108 Correlations between multiple dependent variables MANOVA assumes that multiple dependent variables relate statistically and conceptually (Yoder, 2002). According to this assumption, correlations between multiple dependent variables should be checked before MANOVA is conducted. As Table 2 indicates, most of the dependent variables have statistically significant correlations with one another (p < .01) in the range considered appropriate by Tabachnick and Fidell (2001). Table 2. Correlations between multiple dependent variables Part 1 Part 2 Part 3 Post-test total score Prior knowledge score Part 1 Pearson correlation 1 .349** .341** .869** .534** Sig. tailed)(two- .000 .000 .000 .000 N 108 108 108 108 108 5
Part 2 Pearson correlation .349** 1 .301** .677** .239* Sig. tailed)(two- .000 .002 .000 .013 Part 3 N Pearson correlation 108 .341** 108 .301** 108 1 108 .644** 108 .324** Sig. tailed)(two- .000 .002 .000 .001 N 108 108 108 108 108 Post-test total score Pearson correlation .869** .677** .644** 1 .528** Sig. tailed)(two- .000 .000 .000 .000 N 108 108 108 108 108 Prior knowledge score Pearson correlation Sig. (twotailed) .534** .000 .239* .013 .324** .001 .528** .000 1 N 108 108 108 108 115 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). By examining the Levene’s test of equality of error variances, the results showed that most of the dependent variables had equal variances (p > .05). Based on the above testing results, a one-way MANOVA could be conducted for data analysis. Results of multivariate analysis of variance (MANOVA) Table 3 reports MANOVA for the effect of the studied independent variable on the dependent groups. Intercept Treatment groups * Significant at .05 level Table 3. Results of multivariate tests Effect Wilks’ lambda F p .022 .927 1568.560 2.739 .000* .047* Table 4. Tests of between-subject effects Source Dependent variable Type III sum of squares df Mean square F Sig. Treatment groups Part 1 309.911 1 309.911 5.288 .023* Part 2 25.276 1 25.276 1.292 .258 Part 3 83.530 1 83.530 5.656 .019* Post-test total score 1009.413 1 1009.413 7.036 .009* Error Part 1 6212.755 106 58.611 Part 2 2073.641 106 19.563 Part 3 1565.387 106 14.768 Post-test total score 15206.254 106 143.455 Total Part 1 281344.000 108 Part 2 15633.000 108 Part 3 15453.000 108 Post-test total core 590872.000 108 6
Page 1 and 2: April 2009 Volume 12 Number 2
Page 3 and 4: Abstracting and Indexing Educationa
Page 5 and 6: Engaging students in multimedia-med
Page 7 and 8: this study discusses computer-based
Page 9: Sampling for preliminary study One
Page 13 and 14: Figure 1. Box and whisker plot of t
Page 15 and 16: characteristics and learning styles
Page 17 and 18: However, critical reflection and in
Page 19 and 20: novels, so they collected any relat
Page 21 and 22: 1. Theories of teaching: Comments m
Page 23 and 24: grammatical errors and basic writin
Page 25 and 26: Ho, B., & Richards, J. C. (1993). R
Page 27 and 28: Hwang, K.-A., & Yang, C.-H. (2009).
Page 29 and 30: e deduced from the Affective Domain
Page 31 and 32: Detection procedure Figure 1 shows
Page 33 and 34: avoided. Image processing is perfor
Page 35 and 36: falling asleep) as defined in this
Page 37 and 38: classmates or left their seats duri
Page 39 and 40: help teachers to recognize the lear
Page 41 and 42: Hsieh, P.-H., & Dwyer, F. (2009). T
Page 43 and 44: significant comprehension effects o
Page 45 and 46: Treatment 3 (keyword group): Studen
Page 47 and 48: Criteria of achievement measures Th
Page 49 and 50: esults. A correlational analysis de
Page 51 and 52: A 2 x 1 ANOVA analyzed the effect o
Page 53 and 54: References Aragon, S. R. (2004). In
Page 55 and 56: Raphael, T. (1982). Improving quest
Page 57 and 58: educing maintenance costs. The cont
Page 59 and 60: Synchronization functions According
Page 61 and 62:
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
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Table 6 shows the learners’ prefe
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Bell, P., & Winn, W. (2000). Distri
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Askar, P., & Altun, A. (2009). CogS
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elations, interactions and activiti
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Figure 3. A comparison of knowledge
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ontology will easily be extensible
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Figure 7. Visual Representation of
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Step 5: Use Boolean to combine the
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2) CogSkillNet provides classroom i
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Neo, M., & Neo, T.-K. (2009). Engag
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• Conversation and collaboration
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IMAGE 2 2. Problem identification I
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Methodology At the end of the proje
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presentation skills (m = 3.72), and
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6. Can’t be denying that, sometim
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Lambert, N. M., & McCombs, B. J. (1
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not detected in a previous study, t
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et al. (2001) studied the learning
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all the students. At the midpoint o
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The observed pairs were aware of be
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Table 2 shows the results from a mo
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Table 5: The distribution of time (
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As discussed in the section on prev
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McDowell, C., Werner, L., Bullock,
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Comprehension monitoring is the awa
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or roles in a context, such as “I
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Correlation coefficient was also co
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(a) Text 1 (b) Text 2 (c) Text 3 (d
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Table 3 shows that the average read
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Figure 10. Trace results of the les
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monitor their reading process and h
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Conn, S. R., Roberts, R. L., & Powe
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The technology-mediated groups, wit
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hybrid model of supervision was pos
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Our research indicates that the hyb
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Janoff, D. S., & Schoenholtz-Read,
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goal of using storyboards. But, thi
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In general, learning activities nee
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Furthermore, free subjects, such as
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Storyboarding Roots and related wor
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Referees, on the other hand, may wa
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Symbols Interpretation Defines a un
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Figure 4. Top-level storyboard for
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may take subjects such as practical
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network security information envir
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The concrete procedure and interact
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(e.g., for graduation) using such i
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Thus, academic education modeling b
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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
Page 343:
Chapter 6 exemplifies the activitie
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