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Based on the synchronization types, the synchronization functions provided by MEAT can be classified into two categories: the Post Synchronization and the Real-time Synchronization. The two types of synchronization are similar and their main difference is the streaming sources. In Post Synchronization, streaming is sourced from prerecorded audio/video, and during the synchronization process the teacher can search the stream to find a specific scene for synchronizing. This capacity to undertake a direct search in audio or video stream speeds up the synchronizing progress because there is not a 1:1 ratio between streaming time and the time needed for synchronization. In our experience, to synchronize a one hour lecture stream takes only about 10 minutes. Figure 3 shows the snapshot of Post Synchronization. Differing from Post Synchronization, Real-time Synchronization records the teacher’s lecture as a video stream when synchronizing. Since Real-time Synchronization course is continuous, it takes 1:1 time to produce the lecture video, which is the limitation of Real-time Synchronization. Nevertheless, the Real-time Synchronization still advances in teaching in classroom where teacher can teach while doing the Real-time Synchronization. After the class, the learning content is produced and ready for students. This efficient process indirectly improves students’ learning efficiency, thereby fulfilling the purpose of “Rapid e- Learning” (Archibald, 2005). To precisely associate static materials with the correct time stamps of streaming is one of the major tasks in Synchronization phase. This study adopts the XML (Extensible Markup Language) (W3C, 2006) and XSLT (Extensible Stylesheet Language Transformations) (W3C, 1999) to implement an Extensible Time Stamp Indexing Model for instantly associating the static material with the correct streaming time stamp, in which users can add additional information with respect to static materials. Next paragraph defines the Extensible Time Stamp Indexing Model and explains how it works from an abstract conceptual perspective. Let M be an Extensible Time Stamp Indexing Model, which is characterized by a set of triplet T = {t1, t2, …, tn}, where n is associated with the number of registered time stamps and t is a set of meta-information of a specific time duration of given time flow F (|F| = length of streaming or time flow). For any triplet t = {id, s, e}, where the id is an unique ordinal identification in T, the time stamp s is a time index associated to a beginning of time duration, and the extension e is a profile object, which collects a set of user-defined information to describe the corresponding static learning material, e.g. title and file location. It denotes the time duration of ti as di, where d ⊆ F . If |T| = n, the time i duration di (i = {1, 2, …, n-1}) can be distinguished by (ti+1[s] - ti[s]) and the last time duration of dn can be obtained by (|F| - tn[s]). Given two time durations di and dj, where i ≠ j, then d ∩ d = φ . Moreover, | d | ≤ | F | , i j i because there is no restriction on (t1[s] = beginning of F). Figure 4 shows how to map the conceptual Extensible Time Stamp Indexing Model to virtual one. Considering Figure 4, the left hand side shows the conceptual Extensible Time Stamp Indexing Model, where each block presents a triplet t with respect to certain time duration of F. Subsequently, the right hand side of Figure 4 is a virtual xml file, which presents the mapping result of conceptual view. Notice that the real implementation of Extensible Time Stamp Indexing Model is similar to the Virtual view of Figure 4, but some differences exist between them, e.g., tag names are different and the model structure is not exactly equal. Figure 5: The Seek algorithm Generally speaking, when playing the learning streaming, the static materials would be displayed accordingly. However, in online asynchronous learning, the students usually seek the streaming for a specific purpose (seeking U i = 1 to n 55
inconsistency) and navigate directly to a certain material by skipping previous lectures (skipping inconsistency). These behaviors usually create inconsistency between the streaming and the static materials, and therefore a remedial operation is required to correct this. To correct the skipping inconsistency is relative easy, as it can be corrected by setting the navigated material as the present one. Furthermore, the seeking inconsistency can be corrected by Seek algorithm (see Figure 5). In Seek algorithm, the input p is the new seeking time point, T is the set of triplet of M, and the output is the correct triplet t, which satisfies ts [] ≤ p< ts [] + d, where d is the duration of t (t’s duration includes p). The only non-trivial step is steps 5-7, which consist of an iteration to locate correct triplet t. Packaging functions The Packaging function is composed of three sub-functions: The Mobile Content Translation Module, The Metadata Editing Tool, and the SCORM Packaging Module. Following paragraphs will introduce how these three subfunctions cooperate to perform packaging task. The Mobile Content Translation Module is responsible for generating mobile content according to the imported learning materials. In particular, the Mobile Content Translation Module analyzes the structure of learning materials and reconstructs a similar structure in mobile web pages. In order to deal with the image resizing work, the Mobile Content Translation Module also invokes Transformation Function to size down the images for rebuilding learning materials in a mobile manner. Following that, the Metadata Editing Tool provides a logical view of metadata for teachers to describe the (mobile) learning content, and the SCORM Packing Module then packaging the metadata file for producing a SCORM-compliant learning object. Hereinafter we summarize the selected parameters used to generate content for mobile devices. • Descriptive text: The title and main text are extracted from slides and they are then presented on mobile devices by clear text-based form. It enables learners to read them by their preferred devices without taking into account the resolution issue. • Layout structure: In addition to descriptive text, the original text layout structure (the nest structure, such as items and sub-items) is also reproduced into mobile-based content. Text items usually have the group relationships among themselves, and this parameter keeps the relationships in mobile content for illustrating the original meanings designed by creators. • Memorandum: Memoranda are often added into slides as the detailed annotations or supplements for reminding instructors of important concepts in slides. Since the descriptions of memorandum are relevant to slide, they are also extracted from slides to mobile content. • Sequence of slide: Based on the sequence of slide, the MEAT generates one index page (see the leftmost pictures of Figures 2b and 2c) to organize the mobile content. In addition, the page head and foot of each mobile content page has been added the hyperlink for navigating to next, index, and previous pages (see Figures 2b and 2c). Consequently, mobile learner can traverse among mobile contents via minimum clicks. • Snapshots of slide: All snapshots of slides are generated and downsized to mobile manner. Owning to the main texts of slides has been extracted, the purpose of these snapshots are to provide the figured concepts (such as trend of a curve and so on) instead of the descriptive concepts. The interoperability and reusability of learning object that conforms to the SCORM standard would be improved, and following the standard substantially reduces the cost of learning content reproduction. In order to conform to the standard, teachers have to fill in the fields of content metadata with the correct information. If the fields are not filled in correctly, the advantages of the standard are replaced with the need to go through a lengthy authoring step. In actuality, with regards to early versions of MEAT, which only provided the complete SCORM metadata filling form (see Figure 6a), there was a feedback from teachers indicating that the metadata filling work was too lengthy, and consequently there were few teachers who filled it in. It has been noted that people tend to perceive what they are motivated to perceive – that which has either intrinsic or extrinsic value (Bruner, 1966). If Bruner is correct, we can assume that teachers did not complete the filling task because of costs associated with filling the enormous metadata fields exceeding the benefits of that, and the scale of the filling work obviously has to be reduced. To this end, we summarized the key fields from complete SCORM metadata according the necessary fields defined in SCORM (see Figure 6b). Henceforth, if a teacher does not want to fill in the complete metadata, she can have the alternative of filling in the summary one, and the change result in teachers’ appreciations. 56
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April 2009 Volume 12 Number 2
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Abstracting and Indexing Educationa
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Engaging students in multimedia-med
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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 and 38: classmates or left their seats duri
Page 39 and 40: help teachers to recognize the lear
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: Synchronization functions According
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
Page 89 and 90: Some of the concerns that emerge fr
Page 91 and 92: Ajayi, L. (2009). An Exploration of
Page 93 and 94: As a result of the confluence of di
Page 95 and 96: peers (Schellens et al. 2005). Sche
Page 97 and 98: The integration of discussion board
Page 99 and 100: ecause the technology allowed the p
Page 101 and 102: The pre-service teachers perceived
Page 103 and 104: Furthermore, the study demonstrated
Page 105 and 106: Schellens, T., van Keer, H., & Valc
Page 107 and 108: McKean, 2003). Instructors will see
Page 109 and 110: 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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