January 2012 Volume 15 Number 1 - Educational Technology ...

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transcription. Forth, this study recommends that educators prepare notes with the main points of the lecture. These notes can be helpful during online lectures in adjusting the educator’s speech to a less spontaneous mode; therefore, text can be generated from STR technology with a higher accuracy rate. Furthermore, this study recommends that educators design STR technology-based teaching and learning activities in a way that encourages students to find the strengths and limitations of STR technology. The previously mentioned recommendations were used in this study by the teacher, and as a result, STR-generated texts achieved a high accuracy rate and the texts became useful and meaningful for students’ learning both during and after lectures. Conclusion The study was conducted with appropriately designed learning activities by using STR technology in an online synchronous lecturing environment as well as in a situation wherein an individual student completed homework. The aim of this study was to investigate the students’ perceptions and behavioral intentions toward using STR and the effectiveness of applying STR in synchronous cyber classrooms. The study revealed that the students in the experimental group perceived the STR technology to be easy to use and useful for one-way lectures and individual learning. Most students mentioned that they were highly motivated to use STR to learn in the future. The results of effect size revealed moderate improvement in performance of the experimental group over the control group on homework accomplishments. The results of the t-test and effect size showed that the students in the experimental group performed significantly better on the post-test than the students in the control group. These results imply that the students in the experimental group used STR-generated texts during one-way lectures and for individual learning; thus, the students benefited from STR-generated texts to enhance their overall learning experiences. The interviews with the students revealed that STR-generated texts were used by the students as study tools to clarify lecture content and to take notes during one-way lectures. After the online lectures, the students benefited from STR-generated text by studying it to recall and to better understand previous one-way lectures, to take notes, and to reflect on what they learned during the lecture by doing homework. Previous studies show similar findings regarding using the STR-text during and after one-way lectures (Ryba et al., 2006; SRS, 2011; Wald & Bain, 2008; Way et al., 2008). Based on the obtained findings, this study suggests that educators use STR mechanisms to support teaching and learning activities in synchronous learning environments and to encourage students to use STR-generated texts both during and after lectures to facilitate learning. Several limitations need to be acknowledged and addressed regarding this study. The first limitation is the relatively small sample size, which limits the broad generalization of the results. The second limitation is the consequent adoption of STR by the teacher and students. It requires the teachers to take time to train the system and to edit STRgenerated text before or after classes; thus, a teacher's voluntarily adoption may be very problematic. The third limitation pertains to the accuracy rate of STR technology and the delay of text generation during online, one-way lecturing, which restricts full usage of STR mechanisms. The last limitation relates to large standard deviation values, which suggests that students’ scores were spread out over a large range of values. Future studies should be conducted to investigate related issues. One promising theory that needs to be investigated in the future is the pedagogical effectiveness of using STR technology to support other learning activities, like group discussions, in synchronous learning environments. For example, we believe that using STR technology for group discussions in synchronous learning environments is useful to improve students’ learning, particularly when students use STRgenerated text for deeper reflection and summarization. Furthermore, students’ perceptions and acceptance of using STR technology for group discussions needs to be investigated. Meanwhile, advanced applications using STR mechanisms to conduct different language communications or lectures in traditional classroom environments are also worth further investigation. Acknowledgements This work was supported in part by the National Science Council of Taiwan, R.O.C. under grant NSC 99-2631-S- 006-001, NSC-98-2631-S-008-001, NSC 98-2511-S-008-008-MY3, and NSC 98-2511-S-008-005-MY3 and the Research Center for Science & Technology for Learning of the University System of Taiwan. 377
References Bloom, B. S. (Ed.). (1956). Taxonomy of educational objectives: The classification of educational goals. New York: David McKay Company. Chen, N.S. & Wang, Y. (2008). Testing Principles of Language Learning in a Cyber Face-to-Face Environment. Educational Technology & Society, 11 (3), 97–113. Chen, N.S., Ko, H.C., Kinshuk & Lin, T. (2005). A model for synchronous learning using the Internet. Innovations in Education and Teaching International, 42(2), 181–194. Cooper, H. (2007). The battle over homework: Common ground for administrators, teachers, and parents (3rd ed.). Thousand Oaks, CA: Corwin Press. Creswell, J.W. (2008). Educational research: planning, conducting, and evaluating quantitative and qualitative research (3rd ed.). Upper Saddle Creek, NJ: Pearson Education. Davis, F.D., Jr. (1986). A Technology Acceptance Model for Empirically Testing New End-User Systems: Theory and Results. Doctoral Dissertation, Massachusetts Institute of Technology. Hastie, M., Hung, I-C., Chen, N.S. & Kinshuk (2010). A blended synchronous learning model for educational international collaboration. Innovations in Education and Teaching International, 47(1), 9–24. Kanevsky, D., Basson, S., Chen, S., Faisman, A., Zlatsin, A., Conrod, S. & McCormick, A. (2006). Speech Transcription Services. Proceedings of the Speech and Computer International Conference (pp. 37–43), St. Petersburg, Anatolya Publishers. Petta, T.D. & Woloshyn, V.E. (2001) Voice Recognition for On-line Literacy: Continuous Voice Recognition Technology in Adult Literacy Training. Education and Information Technologies, 6(4), 225–240. Punch, K.F. (2009). Introduction to research methods in education. London: SAGE. Ryba, K., McIvor, T., Shakir, M. & Paez, D. (2006). Liberated Learning: Analysis of University Students’ Perceptions and Experiences with Continuous Automated Speech Recognition. Journal of Instructional Science and Technology, 9(1), 1–19. Smith, P.L. & Ragan, T.J. (2004). Instructional design (3rd ed.). New Jersey: John Wiley & Sons, Inc. Speech Recognition in Schools (SRS). Retrieved January 24, 2011, from http://callcentre.education.ed.ac.uk/Research/Speech_Recog_PRA/speech_recog_pra.html Van Niekerk, J.F. & Thomson, K.L. (2010). Evaluating the Cisco Networking Academy Program's Instructional Model against Bloom's Taxonomy for the Purpose of Information Security Education for Organizational End-Users. In N. Reynolds and M. Turcsányi-Szabó (Eds.), KCKS 2010, IFIP AICT 324 (pp. 412–423). Berlin: Springer. Wald, M. (2010). Synote: Accessible and Assistive Technology Enhancing Learning for All Students. In K. Miesenberger et al. (Eds.), ICCHP 2010, LNCS 6180 (pp. 177–184). Berlin: Springer. Wald, M. & Bain, K. (2008). Universal access to communication and learning: the role of automatic speech recognition. International Journal Universal Access in the Information Society, 6(4), 435–447. Wang, Y., Chen, N. S. & Levy, M. (2010). The design and implementation of a holistic training model for language teacher education in a cyber face-to-face learning environment. Computers and Education, 55(2), 777–788. Way, T., Kheir, R. & Bevilacqua, L. (2008). Achieving Acceptable Accuracy in a Low-Cost, Assistive Note-Taking, Speech Transcription System. Proceedings of the IASTED International Conference on Telehealth and Assistive Technologies (pp. 72– 77), ACTA Press. 378
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Mediating effect of learning flow A
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The instrument used to measure lear
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addition, the cross-loadings of the
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and ease of use had significant eff
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Connell, J. P., Spencer, M. B., & A
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Page 333 and 334: Learning is a cognitive activity th
Page 335 and 336: The survey consisted of 30 question
Page 337 and 338: Created data mining model needs to
Page 339 and 340: dealt with the matter taught at the
Page 341 and 342: Conclusion Conducted research showe
Page 343 and 344: Romero, C., Ventura S., García E.,
Page 345 and 346: prompts as scaffolding strategy to
Page 347 and 348: Reflection Types This study attempt
Page 349 and 350: All the three groups completed the
Page 351 and 352: Results Learning Outcomes Pre- and
Page 353 and 354: Map Analysis in Transfer Test Figur
Page 355 and 356: Generic Prompts and Specific Prompt
Page 357 and 358: Biswas, G., Schwartz, D., Bransford
Page 359 and 360: Chen, Y.-H., Looi, C.-K., Lin, C.-P
Page 361 and 362: of correct response, answer until c
Page 363 and 364: Figure 7 shows a screenshot of one
Page 365 and 366: Collaboration Questionnaire results
Page 367 and 368: following diagrams. The double-arro
Page 369 and 370: “Most students were encouraged to
Page 371 and 372: Bangert-Drowns, R.L., Kulick, C. C.
Page 373 and 374: primary research questions. First,
Page 375 and 376: Individual learning Figure 2. Scree
Page 377 and 378: Experimental Tools This study emplo
Page 379 and 380: Table 3 shows that almost all items
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Page 385 and 386: APPENDIX 2. Taxonomy for Informatio
Page 387 and 388: understand how we can effectively u
Page 389 and 390: 5. Is there a relationship between
Page 391 and 392: correlation between teachers’ IWB
Page 393 and 394: Q7. IWB provides advantages to me t
Page 395 and 396: training regarding this topic. This
Page 397 and 398: Conclusion This study provides a so
Page 399: Smith, H. J., Higgins, S., Wall, K.
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