October 2011 Volume 14 Number 4 - Educational Technology ...

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The flow of the aforementioned Interactive Teaching Method Retrieval Module is shown in Figure 7. The stage of Due Time Setting can be implemented with various waiting strategies. We have implemented the strategies for evaluation. The initial ontology is constructed based on the Mathematics Ontology, as shown in Figure 3, by the 100 participating teachers. This ontology includes 100 concepts, which are equally divided into four categories: Numbers, Geometry, Algebra and Probabilities. With the built ontology, Expert Profile can be represented with respect to each of the concepts. In this implementation, the expert’s profile includes Trust, AWT and Specialty. An example is shown in Figure 8. For the concept “C2”, this teacher has a high Trust value of 0.8. The average waiting time for “C2” teaching method is 7 minutes. Also, the teacher has a middle degree of specialty, 0.5. Experimental Results Figure 8. An example of Expert Profile of some teacher In this study, there are 100 pupils, 100 undergraduate tutors and 100 elementary-school teachers who volunteer to participate in this experiment and live in Taiwan. Each tutor is assigned a pupil. All teachers are involved in the three experiments. In the first experiment of Due time setting on static strategy, the participants are 4 pupils, 4 tutors and 100 teachers. In the experiment of waiting strategies evaluation, the participants are 10 pupils, 10 tutors and 100 teachers. In the last experiment of waiting behaviors of tutors, the participants are 100 pupils, 100 tutors and 100 teachers. Experimental Setup We have built a simulated Tutor Supporting Environment with the prototype system. A supporting community consisting of 100 elementary-school teachers was formed to help tutors via the Internet. To construct the profile of each supporting teacher, a training phase has been conducted for one month (from May 15 to June 15, 2009). On average, 25.5 supporting sessions have been collected for each teacher, and the distribution is shown in Figure 9. Number of Sessions 35 30 25 20 15 10 5 0 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 Id of teachers Figure 9. The distribution of supporting sessions 215
Two criteria have been adopted to evaluate the performance of waiting strategy. Reduced Precision (RP) extends the classical Precision measure by considering the waiting time. To calculate RP, it is necessary to determine the relevance of teaching methods to concepts. In this evaluation, the work of determining the relevance was executed by human experts. Particularly, five professors, experts in e-Learning, were invited to judge whether a teaching method was relevant to a Mathematical concept or not, the relevance being decided by vote. Impact of Due Time Setting on Static Strategy The purpose of this first experiment was to determine the importance of Due Time Setting for retrieving teaching methods in a tutor supporting environment. A Static waiting strategy was implemented as a baseline for evaluation. The Static strategy waits for a fixed amount of time. The participants are 4 pupils, 4 tutors and 100 supporting teachers. Each tutor is assigned a pupil. Without loss of generality, we let the 4 pupils study the four subjects: Numbers, Geometry, Algebra and Probabilities. Tutors use the tutor supporting environment to retrieve teaching methods. Each pupil asks three questions about learning concepts, and the average Precision and Recall values are derived. Figure 10 shows the results with respect to classical recall and precision. First, we observe that the Recall value increases as Due Time increases for the four subjects. This means that an adaptive waiting strategy is demanded and a naïve static waiting strategy will not be suitable for this scenario. Similarly, the Precision value increases as Due Time increases for the four subjects, though not so significantly. Furthermore, we find that Due Time Setting is suitable for the scenario of Recall-oriented teaching method retrieval, which tries to find as many results as possible. For Precision-oriented teaching method retrieval, longer waiting might not significantly increase the performance. Recall Reduced Recall 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.04 0.035 0.03 0.025 0.02 0.015 0.01 0.005 0 Num bers Geom etry Algebra Probabilities 5 10 15 20 Due Time (min) Precision 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Numbers Geom etry Algebra Probabilities 5 10 15 20 Due Time (min) Figure 10. (a) The relation of Recall and due time; (b) the relation of Precision and due time Numbers Geom etry Algebra Probabilities 5 10 15 20 Due Time (min) Reduced Precision 0.12 0.1 0.08 0.06 0.04 0.02 0 5 10 15 20 Due Time (min) Num bers Geom etry Algebra Probabilities Figure 11. (a) The relation of Reduced Recall and due time; (b) the relation of Reduced Precision and due time Next, since the classic Recall and Precision measures are related to the Due Time, a new metric is needed to evaluate the performance of various waiting strategies. We use the Reduced Recall and the Reduced Precision measures to present the results of Figure 10, as shown in Figure 11. We found that the two reduced measures take Due Time into 216
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October 2011 Volume 14 Number 4
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Supporting Organizations Centre for
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Time-Quality Tradeoff of Waiting St
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Ibáñez, M. B., García, J. J., Ga
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Situated learning. It states that k
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executed in the client, the server
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The participants were twelve non-na
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Huang, H. M., Rauch, U., & Liaw, S.
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Theoretical background Feedback, is
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There are already a number of tools
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output used were blogs, written by
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Question Table 2: Perceived Relevan
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Black, P., & Wiliam, D. (2009). Dev
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detection, and analysis of affectiv
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experience is a suitable method of
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Collins, 1998). The appraisal theor
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encompasses the finer shades of an
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and can also track faces with glass
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Acknowledgements This research was
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Merill, D. C., Reiser, B. J., Traft
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to the above-discussed issues of th
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The annotation part of the SDM onto
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that enables the office users to ac
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Figure 4. Semantic document browser
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to the group’s shared semantic do
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participants performed less mouse c
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Bastili, V.R., Gianluigi, C., & Rom
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esource contribution, enabling conv
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Semantic Search In contrast to the
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Participation: P i i i NiVadd Vde
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and comment on each page as well. T
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Table 1. Questionnaire results (N=2
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propose for him, as well as calenda
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Discussion and Conclusion Collabora
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Sampson, D. G., & Zervas, P. (2011)
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Obtaining: the first step of the li
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process (Palmer & Richardson, 2004)
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and ratings (the use of star rating
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(K1) is the number of non-reusable
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Case 3: The process of reusing a se
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Washizaki, H., Yamamoto, Y. & Fukaz
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to examine determinants for pre-ser
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Overview of the model The first res
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significance of relationships. All
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Discussion This study represents a
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Davis, F., Bagozzi, R., & Warshaw,
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Ko, C.-C., Chiang, C.-H., Lin, Y.-L
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Interestingly, the Universal Design
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Figure 1. Framework of the TriAcces
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Experiment This experiment aimed to
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Table 2. Descriptive statistics for
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Foulds, R., & Camacho, C. (2003). T
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answer. However, students can choos
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Hartley & Collins-Brown, 1999; Morl
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Subjects Figure 4. Partial misconce
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correct for a four-option MC item i
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from −3 to 3 for any four-option
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Kansup, W. (1973). A comparison of
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The theory of mental models explain
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computer-mediated counter-arguments
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Learning in this study refers to, w
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the experiment process was anonymou
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supported. However, significant dif
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Cannon-Bowers, J. A., Salas, E., &
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Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.
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Many researchers have conducted emp
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Trialability Some studies have empi
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Results Instrument validation Two c
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In this study, Amos 6.0 was employe
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Our results strongly supported the
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Fornell, C., & Larcker, D. F. (1981
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Kimmerle, J., Moskaliuk, J., & Cres
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What becomes clear from this short
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Figure 1. Distribution of informati
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Results In order to test the hypoth
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esults of the present study with th
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Kolbitsch, J., & Maurer, H. (2006).
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A conceptual framework of this stud
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eveal positive effects on students
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learning resources; and able to acc
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Discussion This study suggests that
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146). Also, role of constructivist
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eform strategy, namely, the MOE-Int
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McNaughton, S. (2002). Meeting of m
Page 169 and 170: Wu, T.-T., Sung, T.-W., Huang, Y.-M
Page 171 and 172: Ubiquitous English-Reading Learning
Page 173 and 174: include the number of times and ran
Page 175 and 176: initial value for the predetermined
Page 177 and 178: The following Eq. (18) expresses th
Page 179 and 180: Limitations This experiment adopted
Page 181 and 182: Table 3 shows the analysis of the p
Page 183 and 184: enhance learning outcomes, it impor
Page 185 and 186: Ogata, H., & Yano, Y. (2004a). Cont
Page 187 and 188: Communication seems to be more effe
Page 189 and 190: the audience only listened, one whe
Page 191 and 192: Step 5) Writing the story On the ba
Page 193 and 194: As indicated in Table 2, the differ
Page 195 and 196: References Armstrong, S. (2003). Th
Page 197 and 198: Goh, T.-T. (2011). Exploring Gender
Page 199 and 200: OPAC has been transformed into AirP
Page 201 and 202: Perceived Self-Efficacy Figure 3. H
Page 203 and 204: were asked to rate their confidence
Page 205 and 206: (2001). The correlation table begin
Page 207 and 208: Discussion and Implications Note. S
Page 209 and 210: will become important to identify i
Page 211 and 212: Parker, A. (2007). SMS - Its use in
Page 213 and 214: Find the perimeter of the rectangle
Page 215 and 216: Wait Time Management Issues related
Page 217 and 218: Precision Reduced_Precision ( T T0
Page 219: learning concept. If the Specialty
Page 223 and 224: Reduced Recall 0.07 0.06 0.05 0.04
Page 225 and 226: In this work, both recall-oriented
Page 227 and 228: Tsai, C.-C., Chuang, S.-C., Liang,
Page 229 and 230: confidence and self-belief in their
Page 231 and 232: Peng et al. (2006) To explore ISE &
Page 233 and 234: Torkzadeh, Chang, and Demirhan (200
Page 235 and 236: educational level, locus of control
Page 237 and 238: Internet-based learning self-effica
Page 239 and 240: Though Lee and Lee (2008) did not d
Page 241 and 242: Regarding the category of ISE resea
Page 243 and 244: Bandura, A. (1997). Self-efficacy:
Page 245 and 246: Pintrich, P. R., & de Groot, E. V.
Page 247 and 248: In formal learning, Safran, Helic,
Page 249 and 250: that culture is essential when user
Page 251 and 252: Table 3. Analysis of usage frequenc
Page 253 and 254: 7 1.16 137.73 0.25 0.29 0.25 Social
Page 255 and 256: discussions freely. So Korean stude
Page 257 and 258: Johnson, L., Levine, A., & Smith, R
Page 259 and 260: Figure 1. The UE Faculty Web Portal
Page 261 and 262: 2006), and consistency (Sindhuja &
Page 263 and 264: Independent Variables Dependent Var
Page 265 and 266: Meanwhile, as shown in Table 4, Ava
Page 267 and 268: design methods in the development o
Page 269 and 270: Pearson, J. M., & Pearson, A. (2008
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Clewley, N., Chen, S. Y., & Liu, X.
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As shown in this Table, Holists wil
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satisfaction or dissatisfaction wit
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8 I would prefer to learn from huma
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the examples need to be more practi
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Brotherton, J. A,. & Abowd, G. D. (
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Cobos, D. L. (2011). Book review: T
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October 2011 Volume 14 Number 4 - Educational Technology ...

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