October 2007 Volume 10 Number 4 - Educational Technology ...

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In the third section, factor analytical techniques were used to determine the underlying structure of teachers’ responses to items. Principal axis factor analysis with varimax rotations was employed for the factor analysis. The results for both the Kaiser-Meyer-Olkin Measure of Sampling Adequacy (0.85) and the Bartlett Test of Sphericity (χ2 =6803.8, N = 613, p < 0.0001) were significant, indicating that factor analysis was suitable for this sample. By using Cattell’s screen test and examining the factor loadings of the items, we removed 13 items from the questionnaire: five factors emerged. According to the pattern of correlation between and among items and factors, we assigned a descriptive name to each of the factors. The factors arrived at were: (1) belief in the use of computing technology in classrooms (e.g., “I believe that technology-based instruction can improve learning achievement”); (2) high degree of interactive use of technology (e.g., “I have had students learn collaboratively through the Internet”); (3) technical and personnel resources available in a given teacher’s school (e.g., “In my school, there are enough technicians to maintain computers”); (4) low degree of interactive use of technology (e.g., “I have used computers to play videos in classrooms”); and (5) attitude toward technology-based instruction (e.g., “Learning software will not make me nervous and uncomfortable”). The validity values of the factors (eigen values) were 4.82, 2.13, 1.73, 1.24, and 1.01. The factor loadings of the 24 items ranged from 0.53 to 0.70, and 58% of the variations were included within the five factors (see details in Table 2). As shown in Table 2, the composite reliability coefficients were ranged from 0.69 to 0.89 and the overall instrument reliability reached 0.87. Therefore, the reliability of the instrument was established. Data Analysis We used frequency analysis to show the distribution of teachers in different stages of instructional evolution and MANOVA techniques to examine correlations and interactions between two factors (teaching seniority and the stage of computer-based instructional evolution) on five measures (beliefs, high-level interactive practices, technical and personnel resources, low-level interactive practices, and attitudes). The stepwise method of multiple linear regression was applied to indicate the relationships between and among these variables. The calculations to determine the coefficients of regression models and MANOVA were performed through the application of the SPSS 12.0 package. No. Items and Factors Table 2. Questionnaire Items and Factor Loading Factor Loading Average variance extracted Composite reliability Subscale score Mean S.D. Belief 0.53 0.89 e19 I think technology is helpful for my teaching. 0.73 3.60 0.73 e26 I believe that technology-based instruction can improve learning achievement. 0.71 3.51 0.78 e9 I think technology-based instruction is one of the future trends in education. 0.69 3.93 0.76 e27 I believe that technology-based instruction can make my teaching more lively and energetic. 0.68 3.89 0.67 e21 I should create different teaching strategies for technologybased instruction. 0.68 3.74 0.67 e25 I believe that technology-based teaching can increase students’ motivation. 0.68 3.81 0.66 e20 Using technology can help me share my teaching experiences with others. 0.68 3.59 0.68 e<strong>10</strong> I am willing to follow school policy on implementing technology-based instruction. 0.66 3.80 0.71 e22 I should develop different assessment strategies for technologybased instruction. 0.63 3.72 0.68 e1 I believe that conventional teaching methods are more efficient than technology-based instruction. 0.54 3.35 0.85 High-interaction practices (behaviors) 0.70 0.84 e35 I have designed activities that allow students to learn through 0.84 2.97 1.04 the Internet. e36 I have had students learn collaboratively through the Internet. 0.84 2.86 1.02 e37 I have used the Internet to support individual learning. 0.80 2.66 1.01 e33 I have used computers and the Internet to collect and grade students’ assignments. 0.72 3.24 1.09 121
Resources 0.55 0.72 e17 In my school, there are enough technicians to maintain computers. 0.83 3.06 1.01 e16 In my school, administrators can provide hardware and software for supporting technology-based instruction. 0.77 3.40 0.90 e13 In my school, teachers often discuss computer-related topics and exchange ideas about computer hardware and software. 0.65 3.03 0.87 e14 In my school, teachers often surf teaching websites. 0.56 3.55 0.77 Low-interaction practices (behaviors) 0.56 0.72 e32 I have used educational software to promote learning. 0.74 3.54 0.95 e30 I have used computers to play videos in classrooms 0.70 3.55 0.96 e34 I have used computer applications to create pictures, videos and animations and used them in classrooms. 0.61 3.55 0.92 e31 I have used document management software, such as Word and Powerpoint, to display my syllabus and my lectures. 0.58 4.02 0.79 Attitude toward computer technology 0.62 0.69 e3 I will not feel anxious when I take any computer-related courses. 0.87 3.61 0.97 e2 Learning software will not make me feel nervous and uncomfortable. 0.82 3.67 0.84 e5 Currently, information on the Internet is useful to my teaching. Instrument reliability: 0.87 0.51 3.71 0.80 Results and Discussion The results are presented in three sections. The first section shows the descriptive statistics associated with two factors (stage of computer-based instructional evolution and teaching seniority) and five dependent measures (beliefs, high-interaction practices, low-interaction practices, resources and attitudes). In the second section, results of MANOVA are presented and interactive effects between/among the stage of instructional evolution and teaching seniority in relation to the five dependent measures are shown. The third section outlines the relationships among beliefs, high- and low-interaction practices, resources and attitudes. Descriptive analyses According to the participants’ responses in the second section of the questionnaire (current stage of instructional evolution with computing technology), one-third of the teachers (about 37%) were in the third or “adaptation” stage; the teachers in that stage can use appropriate software and information technology to improve their teaching and students’ learning in science and mathematics. Roughly one-fifth of the teachers were in either the “entry” stage (15%), “adoption” stage (23%) or “appropriation” stage (18%). Very few teachers (about 7%) were in the final “invention” stage; the teachers in this stage need to be able to use computer-based instruction in a creative way, guiding students to use software as a learning tool in their knowledge construction, modeling, and communication. After surveying junior high school science and mathematics teachers who had integrated computing technology into their instruction in Taiwan, most of them were in adaptation and appropriation stages; that meant they usually applied computer software for instructional purposes, developed multiple teaching strategies to promote students’ cognitive ability, and felt confident in integrating technology into teaching. Teachers’ degree of seniority could indicate the degree of their teaching experiences, pedagogical content knowledge (PCK), subject knowledge, and computer skills. Teachers with a seniority of less than <strong>10</strong> years tended to have more computer skills because they more likely were able to take computer-based instruction courses in their teachers’ training programs. In contrast, the teachers who had already taught for more that 11 years tended to lack training in computer skills and to feel anxious about computers, while also having a higher degree of PCK due to their greater teaching experience. As Figure 1 shows, most teachers with low seniority are in the third or “adaptation” stage: 43% of the teachers who have taught for less than 5 years are in this stage. This means that most of them often integrate technology into their instruction but cannot use multiple methods to promote students conceptual understanding and cognitive ability because they lack the teaching experience, even though their computer skills are probably excellent. About 76% of this low-seniority group are in the first three stages (entry, adoption, and adaptation). On the other 122
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October 2007 Volume 10 Number 4
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Abstracting and Indexing Educationa
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The Relationship of Kolb Learning S
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a question about learning together
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affordances of networked learning s
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included a unit on collaborative le
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on at different times and work indi
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• the numerous evaluation studies
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wanted to work. The same inquiry pr
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usability studies have a place in t
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Milrad, M., & Jackskon, M. (2007).
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information- and communication tech
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Interactive Examination, the studen
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Table 1. Criteria for grading OD st
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content. Differences in the attitud
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Even though further research on the
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Olofsson, A. D. (2007). Participati
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Etzioni (1993) points out that an i
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programme. They are rather construc
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to be that each individual trainee
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Bernmark-Ottosson, A. (2005). Demok
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The Knowledge Foundation (2005). IT
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Figure 1. Design Theories in contex
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attention as we frequently discuss
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Blog Reflection This design concept
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Narrative Structure (Form) Content
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Löwgren, J., & Stolterman, E. (200
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critique, neither do the single ind
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suggested by Ljungberg (1999b) we c
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the del.icio.us site (see figure 3)
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Device cultures It is not only the
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uilt using a camera-equipped PDA ru
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Jones, C., Dirckinck-Holmfeld, L.,
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Milrad, M., & Spikol, D. (2007). An
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components to be able to deal with
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compulsory throughout the project.
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only with their existing day-today
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Page 79 and 80: Mobile mind map tool for stimulatin
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Page 83 and 84: Ericsson, K. A., & Simon, H. A. (19
Page 85 and 86: Al-A'ali, M. (2007). Implementation
Page 87 and 88: complex cognitive skills involve em
Page 89 and 90: Table 1. CAT and a linear mathemati
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Page 93 and 94: Figure 7. Our newly added factors i
Page 95 and 96: question factors according to IRT;
Page 97 and 98: Instructure 1 Login User-ID Passwor
Page 99 and 100: Shute, V., & Towle, B. (2003). Adap
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Page 109 and 110: in the course. A randomized block d
Page 111 and 112: To test whether ET can effectively
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Page 115 and 116: Fleischmann, K. R. (2007). Standard
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Page 123 and 124: Hsu, Y.-S., Wu, H.-K., & Hwang, F.-
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Page 131 and 132: Regression models indicating relati
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Page 137 and 138: concept of social support. Lastly,
Page 139 and 140: Procedure Content analysis procedur
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Page 145 and 146: Anderson, J., & Lee, A. (1995). Lit
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Page 149 and 150: Investigating the problem An extens
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Page 161 and 162: Olfos, R., & Zulantay, H. (2007). R
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Page 165 and 166: Table 2. Assignment evaluation resp
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Frederiksen, J.R., & Collins, A. (1
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Bottino, R. M., & Robotti, E. (2007
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The Text Editor allows the editing
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Mathematical content and structure
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Consolidation exercises Solution co
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As to course components, the activi
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handle more formal representations
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Lagrange, J. B., Artigue, M., Labor
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processed information was classifie
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Method Participants The participant
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Table 5. Learning outcomes of diffe
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peers and benefited most from discu
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Kayes, D.C. (2005). Internal validi
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This paper considers the potential
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The first assumption ensures that c
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allowed to rise when external fundi
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The second option, lowering the cos
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Downes, S. (2003). Design and Reusa
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Appendix A. Good-enough approaches
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Rapid Prototyping and Design Based
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Initial Design Because the field-ba
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• reducing the number of required
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that they felt this hindrance; I fe
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goals” (Edelson, 2002, p. 114) wa
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more and more from the periphery of
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Edelson, M. (1988). The hermeneutic
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Wang, H.-Y., & Chen, S. M. (2007).
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If the universe of discourse U is a
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we can see that S( X ) − S( Y ) S
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Example 1: Let Ã and B ~ be two va
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columns shown in Table 2, where 1
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(3) Find the maximum value among th
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By applying Eq. (7), we can get the
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Q.2 carries 25 marks, Q.3 carries 2
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the proposed methods can evaluate s
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Gogoulou, A., Gouli, E., Grigoriado
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enabling learner (subject) to work
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• Self-, Peer- and Collaborative-
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(iii) Regulates the communication:
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Figure 3. A screen shot of the SCAL
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2 nd study: Thirty-five students pa
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them characterized it as time and e
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Soller, A. (2001). Supporting Socia
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making reference to the others wher
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Solution 2.2: Deliberately select h
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just near a deadline, when it may b
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assessed on an individual basis. Wi
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- and even appreciated - by student
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Panitz, T., & Panitz, P. (1998). En
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Concept Mapping Concept mapping by
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Research Questions In order to dete
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with their teacher but rather than
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Tukey’s HSD post hoc test reveale
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collaboratively during study time?
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Jegede, O.J., Alaiyemola, F.F., & O
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esources. The use of synchronous te
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The use of digital communication mo
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contributed more information and en
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3. Use the second screen for the le
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• Use drawing to develop writing
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student and teacher on the synchron
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Vogel, J. J., Vogel, D. S., Cannon-
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Cases The first set of ten case stu
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Kılıçkaya, F. (2007). Website re
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