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The experiment in stage A was performed without applying the proposed detection reinforcement mechanism. In response to statement 4 in stage A, “I was energetic in class,” twelve students considered themselves to be energetic, while 25 considered themselves to be unenergetic. For statement 5, “I felt fatigued,” 35 students considered themselves to be fatigued, while only four students considered themselves not feeling fatigued at all. In answer to statement 6, “I was dozing during class,” seven students admitted that they had been dozing. All ten students who participated in an in-depth interview on question B-1, “Under what circumstances did you feel sleepy in class?”, felt that interesting course content would make them less sleepy, while boring course content would make them sleepy. Eight students felt that staying long hours in class would affect their mental states. In particular, looking at the computer screen for a period of time would make the eyes fatigued. Seven students stated that they might not feel sleepy if the class could be given with high levels of interaction between the teacher and students or among the students, e.g., discussion or teamwork. In stage B, the experiment was undertaken with the proposed detection reinforcement mechanism. In response to the questionnaire statement 4 in stage B, “I was energetic in class,” 16 students considered themselves to be energetic, while 12 students considered themselves unenergetic. Regarding statement 5, “I felt fatigued,” 33 students considered themselves fatigued, while only four students said that they did not feel fatigued at all. In response to statement 6, “I was dozing during class,” five students admitted that they had dozed off in class. A comparison of data from stages A and B shows that the proposed detection reinforcement mechanism did not significantly decrease the mental fatigue of students during learning. Although this mechanism discouraged the students from being intentionally lazy, it made no significant difference to their feelings. Although two students could force themselves not to doze off in class as a result of the reinforcement mechanism, they still felt fatigued. Thus, the learning result for fatigued students was not good. All ten students in the in-depth interview on questions B-2, “Did the teacher’s warning feedback refresh you in class?”, and B-3, “Did the teacher’s encouraging feedback refresh you in class?”, admitted that receiving encouraging feedback from the teacher would refresh them during class. However, warning feedback resulting from fatigue had no influence on student behavior. Seven students responded that although they felt like refreshing themselves, they still felt fatigued. In particular, only some of the students who were already dozing could still refresh themselves after receiving the warning from the teacher. The remaining students could not refresh themselves and might have dozed off in class again. Thus, many factors affect the mental states of students for learning. As well as teaching materials, teaching methods must also be considered to improve the mental state of students, especially in distance education without face-to-face interaction. The external reinforcement mechanism did not improve the physiological responses of fatigue and the risk of dozing was not apparently minimized either. Additionally, the interview indicates that many factors influence the mental state of students. Some factors were associated with the lifestyles of the students, while others resulted from the content of teaching materials and the teaching methods of the teacher. The students still significantly affect their mental states even after receiving the warning feedback from the teacher. Therefore, simply warning a fatigued student does not work. Distance education enables learners to learn anytime and anyplace. The student should be asked to learn at another time in a better mental sate. Learning reluctantly is a waste of time. Consequently, because external effects cannot minimize mental fatigue caused by physiological exhaustion, students who feel fatigued should be allowed to learn at another time, rather than being requested to refresh themselves. Conclusion Aside from recognizing inattention and fatigue in learners by employing image recognition and detection, this study also focuses on improving the accuracy with the Bayesian network assessment. No technology that combines image recognition with reinforcement mechanisms is yet available. This study utilizes the simplest approach to achieve this objective, a webcam. Although the Bayesian network assessment could not achieve 100 percent accuracy, it did reduce detection misjudgment. Students might need to learn by distance education in different circumstances, for instance, when classes are suspended for infectious diseases, or when children are absent from school for illness. The proposed mechanism can 33
help teachers to recognize the learning conditions of students, and prevent students from learning inattentively after logging into the distance learning system. The teacher is able to observe the learning conditions of the students, making the interaction in distance education courses much more like that in at-school classes. Because the proposed mechanism cannot ensure 100 percent accuracy, in this research can only be utilized to help teachers and parents to supervise students, and is not appropriate for use in assessment. This mechanism was implemented and adopted to perform simulative tests and class experiments. Simulative test results reveal that the accuracy in detecting the feature behavior simulated by students is quite high. Thus, the mechanism can precisely detect learners who are inattentive or fatigued. Class experiment results show that the proposed mechanism can encourage inattentive learners to achieve the learning attention goal. However, mental fatigue caused by physiology can only be solved by requiring students to learn at another time in a better mental state to avoid wasting time. The proposed detection reinforcement mechanism and teaching procedure were designed from the perspective of Affective Domain Teaching Objectives, and can detect accurately the students’ inattentive learning Teachers of distance education courses thus explicitly controlling the learning condition of students avoiding the common difficulty in distance education of students’ exhibiting inattentive or fatigued behavior. The results of the simulative experiment class demonstrate that the mechanism of this research can detect inattentive behavior in students and encourage students to be attentive to learning. The proposed mechanism can accurately detect the behavior induced by inattention and fatigue. However, although this mechanism can urge students to be attentive to learning, it cannot refresh students with mental fatigue resulting from physiological exhaustion. Other factors must be studied to gain an in-depth understanding of the impact of teaching strategies and content and presentation of teaching materials on learning by students. Distance education allows learners to learn anytime and anyplace, enabling a teacher to take preventive measures to relieve the learning fatigue induced by exhaustion or allow students to learn at times when they are not tired. Although the proposed mechanism is not mature and its outcome may not exactly meet the teaching or learning needs, it is a helpful starting point for this research issue. Accordingly, future research will perform in-depth studies on factors such as teaching materials and teaching methods. A distance education mode that can achieve the Affective Domain Teaching Objectives with the proposed mechanism in this research could hopefully be developed. The simulative test was performed in a computer classroom to improve the detection accuracy given the limits of the image recognition and detection method, where the appropriate environmental conditions for image recognition could be easily met. Using the home as the test environment would require help from parents. For example, the home light and the angle of the webcam for the detection system would need to be adjusted according to various environmental conditions. This study aimed to detect inattentive learning behavior and did not classify such behavior specifically. Further detection and recognition of all aspects of student behavior would require more flexible and accurate image recognition and detection techniques or the integrated usage of other technologies. Acknowledgements The authors would like to thank the National Science Council of the Republic of China, Taiwan for financially supporting this research under Contract No. NSC97-2221-E-324-044. The editorial assistance of Ted Knoy is also appreciated. References Anderson, L. W. (1981). Assessing affective characteristics in the school, Boston: Allyn and Bacon. Eriksson, M., & Papanikotopoulos, N. P. (1997). Eye-tracking for detection of driver fatigue. Proceedings of the 1st IEEE International Conference on Intelligent Transportation System (pp. 314–319), Los Alamitos: IEEE Computer <strong>Society</strong>. Gagne, R. M., Briggs, L. J., & Wager, W. W. (1985). Principles of instructional design, Orlando, FL: Holt, Rinehart and Winston. Goge, N. L., & Broiler, D. C. (1992). <strong>Educational</strong> Psychology, Boston: Houghton Mifflin. 34
Page 1 and 2: April 2009 Volume 12 Number 2
Page 3 and 4: Abstracting and Indexing Educationa
Page 5 and 6: Engaging students in multimedia-med
Page 7 and 8: this study discusses computer-based
Page 9 and 10: Sampling for preliminary study One
Page 11 and 12: Part 2 Pearson correlation .349** 1
Page 13 and 14: Figure 1. Box and whisker plot of t
Page 15 and 16: characteristics and learning styles
Page 17 and 18: However, critical reflection and in
Page 19 and 20: novels, so they collected any relat
Page 21 and 22: 1. Theories of teaching: Comments m
Page 23 and 24: grammatical errors and basic writin
Page 25 and 26: Ho, B., & Richards, J. C. (1993). R
Page 27 and 28: Hwang, K.-A., & Yang, C.-H. (2009).
Page 29 and 30: e deduced from the Affective Domain
Page 31 and 32: Detection procedure Figure 1 shows
Page 33 and 34: avoided. Image processing is perfor
Page 35 and 36: falling asleep) as defined in this
Page 37: classmates or left their seats duri
Page 41 and 42: Hsieh, P.-H., & Dwyer, F. (2009). T
Page 43 and 44: significant comprehension effects o
Page 45 and 46: Treatment 3 (keyword group): Studen
Page 47 and 48: Criteria of achievement measures Th
Page 49 and 50: esults. A correlational analysis de
Page 51 and 52: A 2 x 1 ANOVA analyzed the effect o
Page 53 and 54: References Aragon, S. R. (2004). In
Page 55 and 56: Raphael, T. (1982). Improving quest
Page 57 and 58: educing maintenance costs. The cont
Page 59 and 60: Synchronization functions According
Page 61 and 62: inconsistency) and navigate directl
Page 63 and 64: the suitability of the instructiona
Page 65 and 66: Design of manipulation process The
Page 67 and 68: Table 1: Comparative analysis of au
Page 69 and 70: the convenience of mobile-based ass
Page 71 and 72: According to evaluation result, the
Page 73 and 74: Tan, O. S., Parsons, R. D., Hinson,
Page 75 and 76: At a deeper level, though, there wa
Page 77 and 78: theoretical assumptions. Two theore
Page 79 and 80: The students were not going to have
Page 81 and 82: slightly different. The group who c
Page 83 and 84: every item, not giving any result.
Page 85 and 86: edited and presented to the class u
Page 87 and 88: Figure 10. Screen captures from the
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Some of the concerns that emerge fr
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Ajayi, L. (2009). An Exploration of
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As a result of the confluence of di
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peers (Schellens et al. 2005). Sche
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The integration of discussion board
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ecause the technology allowed the p
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The pre-service teachers perceived
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Furthermore, the study demonstrated
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Schellens, T., van Keer, H., & Valc
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McKean, 2003). Instructors will see
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program at The Ohio State Universit
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Knowledge and skills of developing
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their monthly reflections or assign
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Bull, K., Montgomery, D., Overton,
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frustrating the student). ITSs make
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• Link removal - advanced student
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Expert Module Expert Module’s mai
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Based on the questionnaire results
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students were told that their perfo
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thirds of the interviewed students
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Throughout the semester, students i
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human teacher or question developer
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To test the learning effectiveness
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APPENDIX A Computer Science and Sof
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Thus, the use of computers changes
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Specifically, two objectives were p
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Nonetheless, both teachers assign a
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48% 35% 53% similar similar 69% 42%
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Table 4: Students’ attitudes Stat
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activities. Specifically, the histo
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Huang, Y.-M., Huang, T.-C., Wang, K
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• Could recommended learning sequ
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Analysis of sequences prediction me
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Figure 4. The sorting of learning s
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The figures 5a and 5b illustrate th
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Learning sequence recommendation sy
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In this study, forty subjects were
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The potential for LSRS to promote l
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They pointed out that LSRS helped t
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Cover, T. M., & Thomas, J. A. (1991
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Related Efforts An Internet forum i
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with handheld devices and they were
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agent would direct learners to a le
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Mobile RSS Aggregator In order to s
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learning, such as posting question
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With novel technological support, m
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Shen, L., Wang, M., & Shen, R. (200
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as an ‘affective loop’, which r
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Figure 2 is an example of two-dimen
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preference was gathered through dat
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compute the heart rate (HR) as a fu
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sessions. Of all the 18 learning se
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Burleson, W., Picard, R. W., Perlin
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Wu, C.-C., & Lai, C.Y. (2009). Wire
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The wireless handheld learning envi
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the lengthiness of the scales, whic
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Nursing dictionary We constructed a
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them. The computers were networked
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“The major difference was that I
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questionnaire. We do not consider t
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Faruque, F., Hewlett, P. O., Wyatt,
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etween a user and the system. Addit
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Figure 2. A concept map representin
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C5.0 can help teachers to generate
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that a selected programming exercis
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differences between our system and
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1(students can complete without any
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The PADS sometimes assigned very di
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Corno, L. (2000). Looking at Homewo
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Hwang, W.-Y., Hsu, J.-L., Tretiakov
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listed these functions as allowing
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In the research presented in this a
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Research Tools Web-based learning e
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Table 1. Operational definitions of
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action, interaction, and outeractio
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for them was getting the answer as
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Table 6 shows the learners’ prefe
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Bell, P., & Winn, W. (2000). Distri
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Askar, P., & Altun, A. (2009). CogS
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elations, interactions and activiti
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Figure 3. A comparison of knowledge
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ontology will easily be extensible
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Figure 7. Visual Representation of
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Step 5: Use Boolean to combine the
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2) CogSkillNet provides classroom i
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Neo, M., & Neo, T.-K. (2009). Engag
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• Conversation and collaboration
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IMAGE 2 2. Problem identification I
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Methodology At the end of the proje
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presentation skills (m = 3.72), and
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6. Can’t be denying that, sometim
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Lambert, N. M., & McCombs, B. J. (1
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not detected in a previous study, t
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et al. (2001) studied the learning
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all the students. At the midpoint o
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The observed pairs were aware of be
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Table 2 shows the results from a mo
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Table 5: The distribution of time (
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As discussed in the section on prev
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McDowell, C., Werner, L., Bullock,
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Comprehension monitoring is the awa
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or roles in a context, such as “I
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Correlation coefficient was also co
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(a) Text 1 (b) Text 2 (c) Text 3 (d
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Table 3 shows that the average read
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Figure 10. Trace results of the les
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monitor their reading process and h
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Conn, S. R., Roberts, R. L., & Powe
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The technology-mediated groups, wit
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hybrid model of supervision was pos
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Our research indicates that the hyb
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Janoff, D. S., & Schoenholtz-Read,
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goal of using storyboards. But, thi
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In general, learning activities nee
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Furthermore, free subjects, such as
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Storyboarding Roots and related wor
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Referees, on the other hand, may wa
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Symbols Interpretation Defines a un
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Figure 4. Top-level storyboard for
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may take subjects such as practical
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network security information envir
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The concrete procedure and interact
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(e.g., for graduation) using such i
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Thus, academic education modeling b
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Feyer, T., Kao, O., Schewe, K.-D.,
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Richards, G. (2009). Book review: T
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expands the child’s proximal envi
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Chapter 6 exemplifies the activitie
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