January 2012 Volume 15 Number 1 - Educational Technology ...

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1.17 to 3.04 from topic 1 to 7 (F=30.74, p1 *** Topic 3 2.68 2.05 3>1 *** ,3>4 ** ,3>5 ** Topic 4 2.05 1.16 4>1 *** Topic 5 1.91 1.05 5>1 *** Topic 6 2.38 1.22 6>1 *** ,6>4 * ,6>5 *** Topic 7 3.04 1.38 7>1 *** ,7>2 ** ,7>4 *** ,7>5 *** ,7>6 *** * ** *** p
Table 3 shows that the mean frequency of claim, warrant, backing and rebuttal arguments generated by each student for each question from topic 1 to 7 ranged from 0.37-2.16, 0.10-0.97, 0.12-0.69, and 0.03-0.29. The increase of arguments was found to be statistically significant when comparing earlier topics with later topics through the use of repeated measure of ANOVA in all aspects, regardless of claim, warrant, backing and rebuttal (F(claim)=32.69, p 4 *** ,3>5 *** ,3>6 *** ,3>7 *** Topic 4 1.07 .85 .33 .48 1.41 .98 4>1 *** ,4>2 *** ,4>5 ** Topic 5 .86 .53 .23 .31 1.09 .58 5>1 *** ,5>2 *** Topic 6 1.14 .76 .26 .29 1.40 .82 6>1 *** ,6>2 *** ,6>5 ** Topic 7 1.23 .75 .22 .24 1.44 .76 7>1 *** ,7>2 *** ,7>5 *** WARRANT 29.95 *** Topic 1 .41 .30 .08 .14 .48 .31 1>3 *** ,1>4 *** Topic 2 .68 1.04 .30 .36 .97 1.13 2>1 *** ,2>3 *** ,2>4 *** ,2>5 ** ,2>6 * ,2>7 * Topic 3 .07 .21 .03 .11 .10 .27 Topic 4 .09 .27 .08 .22 .17 .32 Topic 5 .25 .33 .25 .33 .50 .46 5>3 *** ,5>4 *** Topic 6 .50 .40 .11 .18 .61 .43 6>1 * ,6>3 *** ,6>4 *** ,6>5 * Topic 7 .47 .35 .15 .20 .62 .42 7>1 ** ,7>3 *** ,7>4 *** ,7>5 * BACKING 11.63 *** Topic 1 .23 .33 .06 .11 .29 .34 1>5 *** ,1>6 ** Topic 2 .49 .70 .14 .29 .64 .80 2>1 *** ,2>3 *** ,2>4 ** ,2>5 *** ,2>6 *** Topic 3 .11 .33 .16 .36 .27 .45 3>5 ** ,3>6 ** Topic 4 .11 .24 .18 .36 .28 .45 4>5 ** ,4>6 ** Topic 5 .02 .08 .10 .19 .12 .23 Topic 6 .05 .11 .09 .16 .14 .19 Topic 7 .38 .39 .32 .41 .69 .70 7>1 *** ,7>3 *** ,7>4 *** ,7>5 *** ,7>6 *** REBUTTAL 6.06 *** Topic 1 .01 .06 .02 .06 .03 .08 Topic 2 .03 .15 .01 .06 .04 .18 Topic 3 .03 .13 .11 .44 .15 .52 Topic 4 .03 .11 .16 .32 .19 .36 4>1 *** ,4>2 *** Topic 5 .05 .11 .15 .26 .20 .32 5>1 *** ,5>2 ** Topic 6 .10 .22 .13 .22 .23 .39 6>1 *** ,6>2 *** Topic 7 .18 .29 .11 .19 .29 .42 7>1 *** ,7>2 *** ,7>3 *** ,7>5 * Note: * p
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
Page 159 and 160: such as grades and evaluation by ot
Page 161 and 162: students’ interest, expectancy fo
Page 163 and 164: constructs that measure students’
Page 165 and 166: Bandura, A. (1978). Reflections on
Page 167 and 168: Lin, J. M.-C., & Liu, S.-F. (2012).
Page 169 and 170: Table 1. The MSWLogo commands learn
Page 171 and 172: other words, we had prepared a set
Page 173 and 174: lab. When she returned and found th
Page 175 and 176: Attitudes toward Programming and Co
Page 177 and 178: Table 5 successfully, it took four
Page 179 and 180: Kuter, S., Altinay Gazi, Z., & Alti
Page 181 and 182: not only the means for trainees’
Page 183 and 184: Data Collection Techniques and Anal
Page 185 and 186: organizations was highlighted by on
Page 187 and 188: provides the means for professional
Page 189 and 190: Kohonen, V. (2001). Towards experie
Page 191 and 192: As Rogers (1995) postulates in his
Page 193 and 194: Instrument and data collection Afte
Page 195 and 196: “Raising our computer skills in C
Page 197 and 198: teachers deploy ICT tools in langua
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Page 201 and 202: APPENDIX A. Questionnaire for Dista
Page 203 and 204: consistency with other ideas, and a
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Page 209: specifically, instructional approac
Page 213 and 214: previous Web-based instructional le
Page 215 and 216: Millar, R., & Osborne, J.F. (1998).
Page 217 and 218: Several studies explore the roles t
Page 219 and 220: understand the content structures o
Page 221 and 222: teacher was not allowed to provide
Page 223 and 224: two coding schemes, as illustrated
Page 225 and 226: also very limited. Teachers and sof
Page 227 and 228: learning activities. British Journa
Page 229 and 230: 2009; Bernard & Cathryn, 2006) or p
Page 231 and 232: In this study, the focus of the ins
Page 233 and 234: The student’s degree of mastery i
Page 235 and 236: The questionnaire for the acceptanc
Page 237 and 238: the t-test result, it is found that
Page 239 and 240: Scale Questionnaire item Mean S.D.
Page 241 and 242: Hwang, G. J. (2003). A conceptual m
Page 243 and 244: sharing, problem solving, and achie
Page 245 and 246: Virtual learning environment In the
Page 247 and 248: Class Table 1. The survey questions
Page 249 and 250: “…the distance and the lack of
Page 251 and 252: collaboration within EVS, thus prev
Page 253 and 254: Huang, T.-H., Liu, Y.-C., & Chang,
Page 255 and 256: y personalised context examples in
Page 257 and 258: Students’ Problem-Solving Guidanc
Page 259 and 260: Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Mediating effect of learning flow A
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The instrument used to measure lear
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addition, the cross-loadings of the
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and ease of use had significant eff
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Connell, J. P., Spencer, M. B., & A
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Despotović-Zrakić, M., Marković,
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Learning is a cognitive activity th
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The survey consisted of 30 question
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Created data mining model needs to
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dealt with the matter taught at the
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Conclusion Conducted research showe
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Romero, C., Ventura S., García E.,
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prompts as scaffolding strategy to
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Reflection Types This study attempt
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All the three groups completed the
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Results Learning Outcomes Pre- and
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Map Analysis in Transfer Test Figur
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Generic Prompts and Specific Prompt
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Biswas, G., Schwartz, D., Bransford
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Chen, Y.-H., Looi, C.-K., Lin, C.-P
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of correct response, answer until c
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Figure 7 shows a screenshot of one
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Collaboration Questionnaire results
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following diagrams. The double-arro
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“Most students were encouraged to
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Bangert-Drowns, R.L., Kulick, C. C.
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primary research questions. First,
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Individual learning Figure 2. Scree
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Experimental Tools This study emplo
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Table 3 shows that almost all items
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I usually engaged myself in listeni
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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January 2012 Volume 15 Number 1 - Educational Technology ...

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