Download - Educational Technology & Society

More documents

Recommendations

Info

approximately 2 technology-mediated classes for every face-to-face class. In total, there were 5 face-to-face meetings and 10 technology-mediated meetings. Data Analyses To test the three working hypotheses, scores were compared between the online and face-to-face supervision groups. ANOVAs were performed to analyze the relationship between the hybrid model of supervision and attitudes toward current and future practice using technology. In addition, ANOVAs were also performed to analyze the relationship between the hybrid model of supervision and attitudes toward the quality of supervision. Following the ANOVAs, power analyses were conducted. Results Table 1 contains the descriptive statistics for the present study. All instruments administered in the present study were found to have moderate-to-high reliability estimates, ranging from .66 to .93 for alpha coefficient estimates and ranging from .71 to .90 for split-half reliability estimates. Substantially higher correlations among the respective subscales of the Working Alliance measure and the Satisfaction Survey over the correlations with other measures, including the Supervision Questionnaire, suggested that the three constructs under investigation (i.e., satisfaction with the quality of supervision, working alliance, and satisfaction with the use of technology) were relatively independent of one another. Table 1. Descriptive Statistics (N = 76) Scale Mean sd α Split Half 1 Correlations 1 2 3 4 5 1. Supervision Quest. Working Alliance 27.2 2.32 .85 .88 1.00 2. Rapport 6.4 0.64 .93 .88 .26 1.00 3. Client Focus Satisfaction Survey 7.2 0.93 .89 .90 .39* .80** 1.00 4. Subscale 1 54.7 11.73 .90 .90 -.26 -.08 -.01 1.00 5.Subscale 2 10.9 3.60 .77 .82 -.20 -.21 -.22 .57** 1.00 6. Subscale 3 11.0 3.14 .66 .71 -.16 -.15 -.08 .74** .72** 1 = split half reliability estimates were corrected for length using the Spearman-Brown formula. *p < .05 (2-tailed); ** p < .01 (2-tailed). Analysis of Variance results are presented in Table 2 addressing each of the three research questions under investigation. Our first research question focused on the degree to which use of the hybrid model of supervision was positively associated with attitudes toward use of technology in counselor education. Subscale 3 of The Web-Based, Distance Group Satisfaction Survey (Roberts, et al., 2002) was used to determine perceptions of usefulness of technology in counselor education. A one-way ANOVA was computed comparing the means of the hybrid model of supervision group (M = 9.46, SD = 2.76) and the face-to-face supervision group (M = 12.71, SD = 2.90). The analysis indicated that there was a significant difference between the means of the two groups (F(1,74) = 25.06, p < .001, η = .50). Thus, our hypothesis was supported, indicating that use of the hybrid model of supervision was in fact positively related to attitudes toward use of technology in counselor education. Our second research question focused on the degree to which use of the hybrid model of supervision was positively associated with attitudes toward use of technology in future professional practice. Subscale 2 of The Web-Based, Distance Group Satisfaction Survey (Roberts, et al., 2002) was used to determine perceptions of usefulness of technology in future professional practice. The one-way ANOVA computed indicated a significant difference between the hybrid model of supervision group (M = 9.56, SD = 2.72) and face-to-face supervision group (M = 12.71, SD = 3.16) (F(1,74) = 21.87, p
hybrid model of supervision was positively associated with attitudes toward use of technology in future professional practice. Table 2. Analysis of variance results, including Cohen’s effect size (ES) f-values Source df F η (η 2 ) p ES(f) Web-Based, Distance Group Satisfaction Survey: Subscale 1 1/74 28.94 .53 (.28) .001 .61 Satisfaction Survey: Subscale 2 1/74 21.87 .48 (.23) .001 .55 Satisfaction Survey: Subscale 3 1/74 25.06 .50 (.25) .001 .58 Supervisory Working Alliance Subscale 1: Rapport 1/65 1.08 .14 (.02) .30 .15 Subscale 2: Client Focus 1/65 2.48 .20 (.04) .12 .20 Supervision Questionnaire 1/74 .08 .03 (
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 and 10:
Sampling for preliminary study One
Page 11 and 12:
Part 2 Pearson correlation .349** 1
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
Page 63 and 64:
the suitability of the instructiona
Page 65 and 66:
Design of manipulation process The
Page 67 and 68:
Table 1: Comparative analysis of au
Page 69 and 70:
the convenience of mobile-based ass
Page 71 and 72:
According to evaluation result, the
Page 73 and 74:
Tan, O. S., Parsons, R. D., Hinson,
Page 75 and 76:
At a deeper level, though, there wa
Page 77 and 78:
theoretical assumptions. Two theore
Page 79 and 80:
The students were not going to have
Page 81 and 82:
slightly different. The group who c
Page 83 and 84:
every item, not giving any result.
Page 85 and 86:
edited and presented to the class u
Page 87 and 88:
Figure 10. Screen captures from the
Page 89 and 90:
Some of the concerns that emerge fr
Page 91 and 92:
Ajayi, L. (2009). An Exploration of
Page 93 and 94:
As a result of the confluence of di
Page 95 and 96:
peers (Schellens et al. 2005). Sche
Page 97 and 98:
The integration of discussion board
Page 99 and 100:
ecause the technology allowed the p
Page 101 and 102:
The pre-service teachers perceived
Page 103 and 104:
Furthermore, the study demonstrated
Page 105 and 106:
Schellens, T., van Keer, H., & Valc
Page 107 and 108:
McKean, 2003). Instructors will see
Page 109 and 110:
program at The Ohio State Universit
Page 111 and 112:
Knowledge and skills of developing
Page 113 and 114:
their monthly reflections or assign
Page 115 and 116:
Bull, K., Montgomery, D., Overton,
Page 117 and 118:
frustrating the student). ITSs make
Page 119 and 120:
• Link removal - advanced student
Page 121 and 122:
Expert Module Expert Module’s mai
Page 123 and 124:
Based on the questionnaire results
Page 125 and 126:
students were told that their perfo
Page 127 and 128:
thirds of the interviewed students
Page 129 and 130:
Throughout the semester, students i
Page 131 and 132:
human teacher or question developer
Page 133 and 134:
To test the learning effectiveness
Page 135 and 136:
APPENDIX A Computer Science and Sof
Page 137 and 138:
Thus, the use of computers changes
Page 139 and 140:
Specifically, two objectives were p
Page 141 and 142:
Nonetheless, both teachers assign a
Page 143 and 144:
48% 35% 53% similar similar 69% 42%
Page 145 and 146:
Table 4: Students’ attitudes Stat
Page 147 and 148:
activities. Specifically, the histo
Page 149 and 150:
Huang, Y.-M., Huang, T.-C., Wang, K
Page 151 and 152:
• Could recommended learning sequ
Page 153 and 154:
Analysis of sequences prediction me
Page 155 and 156:
Figure 4. The sorting of learning s
Page 157 and 158:
The figures 5a and 5b illustrate th
Page 159 and 160:
Learning sequence recommendation sy
Page 161 and 162:
In this study, forty subjects were
Page 163 and 164:
The potential for LSRS to promote l
Page 165 and 166:
They pointed out that LSRS helped t
Page 167 and 168:
Cover, T. M., & Thomas, J. A. (1991
Page 169 and 170:
Related Efforts An Internet forum i
Page 171 and 172:
with handheld devices and they were
Page 173 and 174:
agent would direct learners to a le
Page 175 and 176:
Mobile RSS Aggregator In order to s
Page 177 and 178:
learning, such as posting question
Page 179 and 180:
With novel technological support, m
Page 181 and 182:
Shen, L., Wang, M., & Shen, R. (200
Page 183 and 184:
as an ‘affective loop’, which r
Page 185 and 186:
Figure 2 is an example of two-dimen
Page 187 and 188:
preference was gathered through dat
Page 189 and 190:
compute the heart rate (HR) as a fu
Page 191 and 192:
sessions. Of all the 18 learning se
Page 193 and 194:
Burleson, W., Picard, R. W., Perlin
Page 195 and 196:
Wu, C.-C., & Lai, C.Y. (2009). Wire
Page 197 and 198:
The wireless handheld learning envi
Page 199 and 200:
the lengthiness of the scales, whic
Page 201 and 202:
Nursing dictionary We constructed a
Page 203 and 204:
them. The computers were networked
Page 205 and 206:
“The major difference was that I
Page 207 and 208:
questionnaire. We do not consider t
Page 209 and 210:
Faruque, F., Hewlett, P. O., Wyatt,
Page 211 and 212:
etween a user and the system. Addit
Page 213 and 214:
Figure 2. A concept map representin
Page 215 and 216:
C5.0 can help teachers to generate
Page 217 and 218:
that a selected programming exercis
Page 219 and 220:
differences between our system and
Page 221 and 222:
1(students can complete without any
Page 223 and 224:
The PADS sometimes assigned very di
Page 225 and 226:
Corno, L. (2000). Looking at Homewo
Page 227 and 228:
Hwang, W.-Y., Hsu, J.-L., Tretiakov
Page 229 and 230:
listed these functions as allowing
Page 231 and 232:
In the research presented in this a
Page 233 and 234:
Research Tools Web-based learning e
Page 235 and 236:
Table 1. Operational definitions of
Page 237 and 238:
action, interaction, and outeractio
Page 239 and 240:
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
Page 251 and 252:
ontology will easily be extensible
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
Page 267 and 268: presentation skills (m = 3.72), and
Page 269 and 270: 6. Can’t be denying that, sometim
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
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 and 290: Comprehension monitoring is the awa
Page 291 and 292: or roles in a context, such as “I
Page 293 and 294: Correlation coefficient was also co
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
Page 301 and 302: monitor their reading process and h
Page 303 and 304: Conn, S. R., Roberts, R. L., & Powe
Page 305: The technology-mediated groups, wit
Page 309 and 310: Our research indicates that the hyb
Page 311 and 312: Janoff, D. S., & Schoenholtz-Read,
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
Page 343: Chapter 6 exemplifies the activitie
show all

Download - Educational Technology & Society

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

Delete template?

Save as template?