July 2007 Volume 10 Number 3 - Educational Technology & Society

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The questionnaires were returned by 77% of the intended teacher population. Unfortunately, 7 teachers answered abnormally whose questionnaires were left over 20% unfinished. We then further divided the responding subjects, of which 66% were junior high school teachers and 34% were senior high school teachers, into groups based on four major factors: gender, age, teaching seniority and hours of computer use per week. There were 43 male and 27 female in the valid sample. As for age, there were 34 teachers (49%, the mode) between the ages of 31 and 40; 17 (24%) between the ages of 21 and 30; 11 (16%) between the ages of 41 and 50; and 8 (11%) between the ages of 51 and 65. Thus 73% of all the teachers were under 40 years of age. Four groups were also created based on degree of teaching seniority: less than 4 years of service as a teacher, between 4 and 10 years, between 11 and 20 years, and more than 21 years. The corresponding number of teachers for each group were 12 (17%), 24 (34%), 25 (36%) and 9 (13%). Since secondary education in Taiwan has two levels, junior high school and senior high school, there are three years in each level. A secondary teacher was supposed to take three years to get acquainted with whole curriculum in each level. So those who had been teachers for less than 4 years were relatively new to the field and were considered “novices.” Those who had been teachers for 4-10 years were considered intermediate-level teachers; the others had taught for more than 10 years were deemed expert or experienced teachers. After reviewing domestic literatures and undergoing several discussions, we considered that as for hours of computer use per week there were five groups: less than 3 hours, 3 to 5 hours, 6 to 10 hours, 11 to 20 hours, and over 21 hours. The first two groups are the teachers who averagely used computers less than 1 hour per day, and the last group who used computers over 21 hours per week was averagely more than 3 hours in using computer per day. The corresponding number of teachers was found to be 6 (9%), 17 (24%), 13 (19%), 12 (17%), and 22 (31%). Thus over 30% of all the teachers averaged at least 3 hours of computer use per day; conversely, there were also more than 30% who averaged less than 1 hour per day. Instrument For this study we developed a questionnaire, “The Integration of CBT into Instruction Questionnaire.” It was divided into three sections: (1) demographic background and general nature of teachers’ computer use, (2) teachers’ attitude toward computers (3) the extent of integration of CBT into their instruction. The first section was demographic background included information about age, gender, teaching seniority and school level taught. The rest of this section included type of CBT use (purpose and place, e.g. only at home or also at school) and number of “computer hours” per week. And the last two sections consisted of 20 items: each item had a five-point Likert scale with choices ranging from “I fully agree” (5 points) to “I fully disagree” (1 point). Thus, 3 points would imply moderate extent. As for the items regarding “attitude toward computers,” we emphasized three factors that had been found to be crucial by earlier researchers: liking computers, having confidence in computers, and feeling anxiety about computers. However, Mortz and Nash (1997) found that “computer confidence” and “computer anxiety” had a high negative correlation, so that they might be considered two opposite poles of the same component. Thus we decided that “attitude toward computers” could be analyzed in terms of two factors: “computer liking” and “computer anxiety.” In the rest part of this subscale we also took personal acceptance of technological innovation to be a general and antecedent factor which helped to determine attitude toward computers (Braak, 2001). Thus there were 10 items on the “attitude toward computers” subscale, including 7 which described teachers’ feeling, reception and anxiety about computer use and 3 which described teachers’ acceptance of technology in their daily life. Section three also included a subscale for application of technology in instruction, designed to examine to what extent teachers used CBT in their instructional practice inside the classroom. We tried to see whether teachers used CBT primarily as a basic instructional tool for doing things like playing videos, demonstrating instructional contents with the word processor and applying instructional software to help students learn. The more advanced instructional uses we set out to investigate were, for instance, use of CBT to craft new curricula and teaching methods. Furthermore, we wanted to see if there might be some teachers who regarded CBT as a mind-tool for creating a student-centered learning environment that could enhance learning (Salomon, Perkins, and Globerson, 1991). Since there were not enough samples to perform factor analysis on the third section of the questionnaire, this study still used exploratory factor analysis, and principle component analysis with varimax rotation, to clarify the 235
underlying structure of the two primary constructs of these two subscales individually. In the first subscale, subjects’ attitudes toward computers were grouped into three factors: “computer affection,” “computer anxiety” and “acceptance of technological innovation.” They accounted for 67.35% of variance. The second subscale, subjects’ application of computer technology to their classroom instruction, was divided into two factors: basic instructional use and advanced instructional use. They accounted for 67.38% of variance. The eigen value of these factors from principle component analysis was larger than one. The overall reliability of this questionnaire was given a Cronbach’s alpha coefficient of 0.89; in addition, the two subscales were given values of 0.82 and 0.89. Table 1 shows the factor loadings and Cronbach’s alpha coefficient for each item in the questionnaire. Table 1: Questionnaire Items and Rotated Factor Loadings No Item and Factors Factor Loading Variance Explained Cronbach’s alpha coeff. Construct 1: Attitude Toward Computers Factor 1: Affection for Computers 0.67 0.82 1 I enjoy using computers.. 0.796 2 I feel bored and time-wasting when I use a computer. 0.864 3 I like to surf the internet with my computer. 0.656 4 I feel confident in using computer. Factor 2: Computer Anxiety 0.695 5 I feel nervous and uncomfortable when learning a computer software application. 0.858 6 I feel nervous and uncomfortable when leaning about computer hardware. 0.855 7 I feel anxious about using computers because I have never taken any computer-related course. Factor 3: Acceptance of New Information Technology 0.663 8 I consider myself less able to adjust to the newest high-tech (IT) devices than most other people. 0.639 9 I am curious about the most recent high-tech devices (new home appliances, audio-video entertainment equipment).. 0.719 10 I try to avoid operating high-tech devices that seem too complicated (like home appliances, audio-video entertainment equipment). 0.631 Construct 2: Application of Technology in Instruction Factor 1: Basic Instructional Use 0.67 0.89 11 I have played a film (VCD/DVD) using a computer CD-ROM and a projector in the classroom. 0.802 12 I have used computer software, such as Microsoft Word and PowerPoint, to demonstrate lesson contents. 0.830 13 I have adopted educational software, which was bought or downloaded from the internet, to enhance students' leaning. Factor 2: Advanced Instructional Use 0.763 14 I have collected and evaluated students' works through the internet. 0.721 15 I have developed and applied instructional software in the classroom. 0.677 16 I have developed on-line courses for students with computer-based technology. 0.823 17 I have applied computer-based technology to integrate different discipline lessons. 0.714 18 I have promoted students’ cooperative learning through computerbased technology and the internet. 0.851 19 I have enhanced students’ problem-solving ability by integrating computer-based technology into instruction. 0.803 20 I have assisted students in their individual learning through computer-based technology. 0.853 236
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July 2007 Volume 10 Number 3
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Advertisements Educational Technolo
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Distinguishing between games and si
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“sometimes” is less demanding t
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Figure 2. From generic skill to lea
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The large window on the left presen
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define competencies of individual a
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competency is composed of a single
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Generic Skills Classes 1 2 3 Receiv
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Figure 6. Extension of the competen
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Competency scale By combining the g
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activities, and resources present i
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Conclusion The importance given to
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Ainsworth, S. (2007). Using a Singl
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different instruction. For example,
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Author-focused studies Studies 1 an
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egression showed that students were
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REDEEM is now a senior citizen in t
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Chieu, V. M. (2007). An Operational
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When students discuss with peers, t
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COFALE as a learning environment an
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COFALE supports a set of predefined
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Tom is asked to prepare a list of g
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45-minute-long lecture and 2-hour-l
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as an easy-to-use technological mea
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Spiro, R. J., & Jehng, J. C. (1990)
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learning settings. However, such a
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introduced in the LOCO-Cite ontolog
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‘virtual subsection’) of the LO
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Accordingly, TANGRAM provides stude
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learning environment is to define t
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Acknowledgment This work is support
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Dron, J. (2007). Designing the Unde
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wikis and blogs (or at least, colle
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The darker side of social software
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for the beetles, whose guts act as
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The principle of sociability The pr
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Bonabeau, E., Dorigo, M., & Theraul
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Klamma, R., Chatti, M. A., Duval, E
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We perceive adaptivity and personal
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Mixed-method evaluation approach tr
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Garden of Knowledge - with a commun
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equirements for collaborative adapt
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Law, E. L.-C., & Hvannberg, E. T. (
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Wang, T. I., Tsai, K. H., Lee, M. C
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2.3 Recommendation Model Survey In
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Figure 1. The portion of the concep
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STEP4: Associated with concepts in
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A modified TF-IDF approach is used
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P(CK j x LOM ) = k NW max ∀ T∈
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4.2.2 Correlation-based algorithm I
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content feedback, it can be intuiti
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66% improvement in average, and, wh
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(a) (b) (c) Figure 10. Comparisons
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index.aspx. ADL-2 (2001). Advanced
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Wolpers, M., Najjar, J., Verbert, K
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activity is taking place. For examp
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Figure 2: The CAM schema elements (
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• Context: the context element ca
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4.1 Generation of Attention Metadat
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addition, such keywords can be extr
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Action related data, data about sea
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Braun, S., & Schmidt, A. (2006). Do
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El-Bishouty, M. M., Ogata, H., & Ya
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collaboration (Ogata, et al., 1999)
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In case of LOI value is equal or cl
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Also, the learner can click on an e
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System Evaluation An experiment was
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In the fifth phase, 89% of the stud
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Fischer, G., & Konomi, S. (2005). I
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application for specific needs, whi
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(4) Multimedia: The truth is that e
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Program Company Price OS 6 eZediaMX
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Program Text editor Text import for
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Program Sound Formats Image Formats
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Program Image Painting Export Scrip
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Program Effects Needs player 5 Easy
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Program WYSIWYG Design Interactivit
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the grades awarded to the programs
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Grade Image formats Sound formats V
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Figure 3. Adequacy of image formats
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Animating objects, pictures, etc.,
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Educational overview Having taken m
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In summary, the final outcome is th
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Developing the Interactivity Survey
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technique. be treated as a desired
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55. A lecture in which student lear
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specific conclusions that researche
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Discussion and conclusion Although
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Juwah, C. (2003). Using peer assess
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Publish results Set a few goals for
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familiarity with computer terms, ex
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provide a framework for the integra
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tool themselves, and then gauge its
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Page 191 and 192: portrayed as even more time-consumi
Page 193 and 194: Clarke, B. (2001). Corporate curric
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Page 199 and 200: entertaining (Neuhauser 1993). The
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Page 207 and 208: Figure 6. NARRATE phase screen afte
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Page 213 and 214: Rumelhart, D. E. (1975). Notes on a
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Page 229 and 230: Work outside the U.S. has also prom
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Page 247 and 248: of males. Secondly, as far as age w
Page 249 and 250: Conclusion This study examined the
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Page 255 and 256: Predetermined goal of a game The pr
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Page 259 and 260: Bain, C., & Newton, C. (2003). Art
Page 261 and 262: Prensky, M. (2005). Adopt and Adapt
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Page 267 and 268: another token. Other tokens further
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Page 279 and 280: Soller, A., & Lesgold, A. (2000). K
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‘HasExample’, and ‘HasFurther
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DCMI (2003). DCMI Metadata Terms, R
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Literature review Astronomy educati
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Systems of type I provide different
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night. To prevent students from get
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Figures 2-5 display screenshots of
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Results of the study reveal the imp
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vertical axis leads to the disappea
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Dede, C., Salzman, M., Loftin, R. B
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Trumper, R. (2000). University stud
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ased on pre-defined adaptation rule
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Therefore, the higher-level items o
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common prefix with the existing pa
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later should be omitted (presented
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5.1 The Average Length of each Sequ
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Recall 6. Discussion 0.8 0.7 0.6 0.
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easily take lessons in a good learn
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Laborda, J. G., & Royo, T. M. (2007
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Chapter 8, Online reference tools,
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Kenyon, D. M., & Malabonga, V. (200
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and concise manner (e.g. Weeding =
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stream are chapters 7 and 8 which f
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