April 2012 Volume 15 Number 2 - Educational Technology & Society

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(3) Emerging and Best Practice 3. Pedagogical Design and Theories (1) Rethinking Pedagogy in Technology-Enhanced Learning (2) User Prior Knowledge and Background Analysis (3) Innovative Pedagogical Models (4) Next Generation Learning Strategies 4. Adaptive and Personalized Technology- Enhanced Learning: Knowledge and Competencies Management (5) The Potential of Brain Science and its Impact on Learning (1) Learner Centered Strategies (2) Critical Thinking and Problem Solving Development (3) E-Library and Learning Resources (4) Learners Diversity, Inclusiveness and Inequality (5) Supporting Students Experience 5. Artificial Intelligence in Education (1) Intelligent Educational Systems (2) Ambient Intelligence and Smart Environments for Learning (3) Semantic Web and Ontology for Learning Systems 6. Computer Supported Collaborative Learning (4) Data Mining and Web Mining in Education (1) Web 2.0 and Social Computing for Learning and Knowledge Sharing (2) Collaborative and Group Learning (3) On-line Dictionaries and Language development (4) Collaborative and Social Technologies 7. Mobile and Ubiquitous Learning (1) Wireless, Mobile and Ubiquitous Technologies for Learning (2) Pervasive Computing for Learning (3) U-Computing in Learning 8. Digital Game and Intelligent Toy Enhanced Learning 9. E-Assessment and New Assessment Theories and Methodologies (4) Context-Aware U-Learning (1) Gaming, Simulation and Virtual Worlds (2) Identity in Gaming to Learn: Roles and Role-Playing - Collaboration, Competition and Community (3) Non-Visual Senses (Smelling, Touching, Hearing) (4) Game-Based Learning/Training (5) Multiplayer and social game design (6) Virtual Characters, Virtual Storytelling and Game Narrative (1) E-Assessment Strategies (2) Automatic e-Assessment (3) Collaborative e-Assessment (4) E-portfolio Implementation and Impact 10. Special Needs Education (1) Technology enhanced Special Needs Education (2) Learning Disabilities (3) Technology Enhanced Learning for Students with Special Needs 11. Motivation, Perceptions and Attitudes (1) Technology Acceptance of Technologies in Education (2) Readiness of Technologies in Education (3) Sociological and Psychological Dimensions of Technology-Enhanced Learning 12. Learning Behaviors, Usage Patterns and Discourse Analysis 13. Policies, Social Culture Impacts and Trends for Technology-Enhanced Learning (4) Self-regulation and Metacognition (1) Social Networking and Interactive Participation (2) Learning Culture (3) User Attitude and Technology Acceptance (4) Analysis of Asynchronous or Synchronous Interaction (5) Reflective Discourse (1) Policy Issues in Technology-Enhanced Learning (2) Organizational Policies and Strategies for Technology- Enhanced Learning Implementation (3) Global Trends in Technology-Enhanced Learning 357
Research sample groups (4) Faculty Development and Support (5) Academia-Industry Collaboration (6) International Challenges After the four experts’ discussion and consensus, seven sub-categories for the research samples were identified. The finalized sub-categories were: 1. Elementary school, 2. Junior and Senior high school, 3. Higher education, 4. Teachers, 5. Adults, 6. Others, and 7. Non-specified. Research learning domains The same experts discussed the usage of the sub-categories for the learning domains. The learning domain for each paper included in this review was categorized into one of the following sub-categories: 1. Science (e.g. Physics, Chemistry, and Biology, Medical and Sport Science), 2. Mathematics, 3. Arts & Language, 4. Social Studies, 5. Engineering (including Computers), 6. Others, and 7. Non-specified. Data coding and analysis This study used all of the empirical research articles published in BJET, C&E, ETR&D, ET&S, and JCAL from 2000 to 2009 (ten years) to examine the research trends in the TBL field. Content analysis was utilized to classify the research topics, sample groups, and learning domains of the articles. It is noted that many research articles addressed more than one research topic or sample group; therefore, the research topic and sample group category was coded into two levels. The primary matched sub-category was coded as “the first research topic” or “the first sample group” and the preceding matched sub-category was considered as “the second research-topic” or “the second sample group.” The matched sub-categories after the “second researchtopic” or “the second sample group” were excluded. If the article had only one research topic or one sample group, the field of the second research topic or second sample group was coded “none.” For example, if an article aimed to evaluate whether a new learning system enhanced the learning motivation of fourth grade students, the article’s first research-topic would be coded “2” (i.e., Evaluation of Learning Systems, Platforms and Architectures) and the second research-topic would be coded “11” (i.e., Motivation, Perceptions and Attitudes). Finally, a one-level coding process was involved in categorizing the learning domains, as based on our pilot coding for about 200 papers, most of the papers did not involve more than one learning domain. Articles with only one major learning domain were coded to the designated learning domain, and articles with more than one major learning domain or other than the aforementioned learning domain sub-categories were accounted as “Others.” However, only around 5% of the papers were coded as “Others” for the learning domains (presented later). The coding process was undertaken manually by the researchers. Seven doctoral students majoring in educational technology helped to categorize the articles based on the aforementioned categories (research topics, research sample groups, and learning domains). Two post-doctoral researchers then randomly selected 401 articles and followed the same coding process to compare the coding results by research topic. The results reached an agreement of 0.88. Since the research sample group and learning domain information was less ambiguous in most of the articles, the researchers only randomly located 100 articles to compare the coding results. The results of both categories reached the same agreement of 0.90. After the classification, the researchers proceeded to assign weighted scores to each article. First, a score of 1 was given to “Regular articles” (n = 2,806) and a score of 0.5 was applied to “Short articles” (that is, the “Colloquium” articles in BJET, n = 170). The total weighted score was 2,891. Then, a score of 0.6 was assigned to the first research-topic and a score of 0.4 was allocated to the second research-topic when an article covered two matched research topic sub-categories. If the article solely had one matched research topic, this topic received a total score of 1 (for the “short articles”, the score of the first research topic was 0.3 and the score of the second research topic was 358
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April 2012 Volume 15 Number 2
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Supporting Organizations Centre for
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Contextualizing a MALL: Practice De
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Kop, R. (2012). The Unexpected Conn
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Human mediation and information flo
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elevant information based on some k
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of learners. A search facilitated t
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information as human mediation mean
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Blau, I., & Barak, A. (2012). How D
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examined whether the readiness to p
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Table 5 presents the ANOVA for the
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However, in discussing a non-sensit
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deviations showed smaller variance,
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actual participation discussing sen
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Mesch, G., & Elgali, Z. (2009). Soc
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Although prior research on overall
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with a score of 39 and below as “
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Table 2 demonstrates mean scores an
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Discussion Although the recent Inte
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Furthermore, the most preferred pla
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MEB. (2009). Internete erisim proje
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Learning Management Systems (LMS) b
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practice and repetition with feedba
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Online peer assessment Peer assessm
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At an international conference on m
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� Ability to produce rich assessm
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Meishar-Tal, H., & Tal-Elhasid, E.
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Tsai, S. C. (2012). Integration of
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order to decrease text complexity a
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matter how the learners’ aptitude
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If compared by programs, WP student
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mean F2F 4.64 4.52 4.35 4.44 *: p
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een analyzed and were compared with
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Chen, G.-D., Lee, J.-H., Wang, C.-Y
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and diverse cognitional and emotion
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Encourage and persuade students wit
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Materials and procedure Each studen
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The lower part of Figure 4 shows st
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Mayer, R. E., Sobko, K., & Mautone,
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that an item is answered correctly
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Table 1. Relative concepts frequenc
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The Account Management Module provi
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Experiment 1 and results Figure 7.
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Utilization of Test Item 36 33 30 2
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Utilization of Test Item 36 33 30 2
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0.065 0.877 (17.56) 0.938 (22.55) 0
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Hwang, G.J., Hsiao, C.L., & Tseng,
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the diverse needs of modern industr
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Figure 1. The framework of interdis
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Research Design Figure 3. Recommend
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friendly. 8. I feel the layout and
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satisfaction, and system acceptance
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Ozok, A. A., Fan, Q., & Norcio, A.
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than students in a conventional cla
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shown in Figure 2 to include the st
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(1996) Pleasure-Arousal-Dominance (
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Based on this connection, we consid
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Final dominance measurements were p
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student mood and thereby allow comp
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Sessink, O., Beeftink, H., Tramper,
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The rationale behind the video form
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usefulness of the movies and their
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case of the first two questions and
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this paper in order to evaluate the
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The survey questions Appendix A In
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E-training in organizations E-train
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efficacy regarding online training,
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Method Sample and procedure Figure
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0.874 which exceeded the recommende
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Even though the result does not con
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Brown, M., & Cudeck, R. (1993). Alt
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Wen, Y., Looi, C.-K., & Chen, W. (2
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when they are designing and impleme
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paper focuses on the first three st
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their self-esteem to be partners in
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Democratizing knowledge and symmetr
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1) Reading the article: At the star
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Effects of RCKI principle-based ped
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References Alexander, C. (1979). Th
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Valsamidis, S., Kontogiannis, S., K
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The Analog system (Yan et al., 1996
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First, the number of the sessions a
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BioLayout uses a modified version o
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As shown in figure 3, all metrics c
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Figure 7. The detailed results for
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Enright, A. J., van Dongen, S., Ouz
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Chu, S. K. W., Kwan, A. C. M., & Wa
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teachers, as well as collaboration
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from the students’ ratings appear
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Table 5 summarizes the students’
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eading blogs (i.e., reminders, easy
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Luehmann, A. L. (2008). Using blogg
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Uzunboylu et al. (2009) examined th
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System implementation Overview of M
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Methods Before the experiment, a mo
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too simple, incomplete, or they eve
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Comparisons of UMLS Questionnaire i
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already described may play an impor
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Parker, D., Manstead, A. S. R., Str
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Avci, U., & Askar, P. (2012). The C
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Literature review The related varia
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collected through Personal Informat
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Self-efficacy 92 5 21 13.37 3.726 W
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In this problem, intention was take
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References Ajjan, H., & Hartshorne,
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Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
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Students perused the course materia
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problem. All articles were then sen
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tests, electronic tests are more li
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Students 10 and 11: My classmate Ed
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collection was also approved by all
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Ubiquitous Revision Seamless Collab
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Tai, Y. (2012). Contextualizing a M
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tasks were developed from task type
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experiences in this phase. Moreover
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test is administered again right af
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The collected data were analyzed wi
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Gorp, K. V., & Bogaert, N. (2006).
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4PL IRT model According to the numb
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examinee’s ability more accuratel
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would be more accurate if items j+1
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C�I or Change I�I) would be 0.2
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improved by rearrangement procedure
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Table 6. Repeated Measures ANOVA of
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Leppisaari, I., & Lee, O. (2012). M
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Selection of the virtual tool The t
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Environmental themes emerged strong
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Visualization of text (visual langu
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environments where visual technolog
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- Logging in to learning environmen
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McNaught, C., Lam, P. & Lam, S.L. (
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on-site to finally achieve a meanin
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Table 1. Digital game designers and
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worldviews are socio-cultural-histo
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Phase Methods Visitor data collecti
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operate the game without the help o
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Visitors (end users) must be heard.
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Islas Sedano, C., Pawlowski, J., Su
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part of something larger than the s
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Previous studies have developed rel
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Technology Acceptance. The 10 items
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Table 4. Model Fit Indices Model χ
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In the final path model of set 1, t
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Discussion Differing from prior stu
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Haythornthwaite, C., Kazmer, M. M.,
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Ge, Z.-G. (2012). Cyber Asynchronou
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college of a university situated in
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Table 3 shows us the following info
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Appendix B shows Class 1’s respon
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increase one’s ability to process
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Appendix A Responses concerning syn
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Shih, S.-C., Kuo, B.-C., & Liu, Y.-
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Adaptive U-learning Math Path Syste
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Figure 4. Online tutorial courses o
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Participants and Experimental Proce
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Page 361: domains. Five research questions we
Page 365 and 366: 13. Policies, Social Culture Impact
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Page 369 and 370: (AR = -2.1) (n = 7) 2. Non-specifie
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