April 2012 Volume 15 Number 2 - Educational Technology & Society

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The domain experts’ knowledge structure Once a knowledge structure is constructed by practising teachers and domain experts, it is named as the domain experts’ knowledge structure. The procedures for constructing a domain experts’ knowledge structure are as follows. First, the domain experts define the important concepts of each unit by analyzing teaching materials and objectives. Second, after much discussion, the domain experts decide the sequence of the concepts development and relationships among these concepts to depict in a tree diagram the experts’ knowledge structure for each unit. Figure 2 is an example of part of the domain experts’ knowledge structure for a triangle unit of mathematics used in elementary schools of Taiwan. In the domain experts’ knowledge structure, the upper-level concepts such as “find the isosceles triangle” are advanced concepts, while low-level concepts such as “find the right angle” are basic-level concepts. Generally, an item is developed to assess knowledge on a single concept. Diagnostic tests are developed by the concepts defined in the domain experts’ knowledge structure. Find the isosceles right triangle. Find the right triangle. Find the right angle. Figure 2. Part of the domain experts’ knowledge structure for the triangle Knowledge structure from Diagnosys Find the isosceles triangle. Find the isosceles triangle. Understand the definition of isosceles triangle. Calculate the unknown angle with the properties of the isosceles triangle. The interior angles of a triangle add up to 180 degrees. With Diagnosys, a paper-based pre-test is developed based on domain experts’ structures and is then administered to collect responses from students. This data was applied to develop the inference mechanism as follows. The relative frequencies of two concepts, A and B, are defined in Table 1. As shown in Table 1, f AB represents the number of students with correct answers for both concept A and concept B. If f AB � f AB �� f AB � f AB , then concepts A and B are equivalent and the relation is denoted as A↔B. Therefore, if students understand concept A, they will understand concept B as well, and vice versa. Moreover, if f AB �� f AB , then concept A could be linked forwards to concept B. The relation denoted as A→B means that A is a prerequisite to B. The important characteristic of the link A→B is twofold: 1. If the student gets an item on concept B correct, we can infer that she or he also understands concept A. 2. If the student gets an item on concept A incorrect, we can infer that she or he also does not understand concept B. These two rules apply transitively across the structure according to the partial ordering given by the links. For example, for the network, A→B→C, if a student gets an item on concept C correct then we can infer that the student understands concept B due to direct inferences, but also A due to indirect transitive inferences. This algorithm allows the system to significantly reduce the number of items administered compared with a conventional test. 75
Table 1. Relative concepts frequency table A (correct) A (incorrect) B (correct) f AB f AB f B (incorrect) f AB AB Some inefficient problems are posed such as the definitions of ordering relation, equivalence and transition among concepts lack clarity while being inoperable. To improve these limitations, a threshold model is defined in this paper: * If� AB � ( f � f ) /( f AB AB AB � f ) � � then A↔B. AB dia * If �AB � f / f � � dia , then A→B. AB AB Ordering theory and item relational structure theory In this paper, two other item ordering theories, OT and IRS are used for estimating knowledge structures of examinees and to develop new adaptive test algorithms. They are described briefly below: Let X � ( X1, X 2, �, X n) denote a vector containing n binary item score variables. Each student taking an n-item test produces a vector X � ( x1, x2, �, xn ) containing 1 (correct) and 0 (incorrect). Then the joint and marginal probabilities of items on concepts A and are represented in Table 2. concept A * Table 2 The joint and marginal probabilities of concepts A and B concept B X �1 X � 0 Total B A �1, X B A � 0, X B X � 1 P ( X �1) P ( X 1, X � 0) P ( X � 1) A B A � B A � 0, X B X � 0 P ( X � 1) P ( X � 0) P ( X � 0) A Total P ( X � 1) P ( X � 0) 1 B For OT, let � AB � P( X A � 0, X B � 1) � �OT , usually 0. 02 � � OT � 0. 04 (Airasian & Bart, 1973; Bart & Krus, 1973), concept A can be linked forward to concept B. The relation is denoted as A � B this means that A is a prerequisite to B. If A � B and B � A , then the relation is denoted as A � B and it means concepts A and B are equivalent. For IRS, Takeya (1991) proposed another index, * r AB , which is used to define the ordering relation from concept A to concept B. The definition of * r AB is * rAB � 1� ( P( X A � 0, X B � 1) / P( X A � 0) P( X B � 1)) � � IRS * If rAB � � IRS , then concept A can be linked forward to concept B. Usually the rule of thumb is to set � IRS � 0. 5 . The performances of knowledge-structure-based adaptive testing (KSAT) algorithms As mentioned above, four methods, Diagnosys, OT, IRS, and the domain experts, can be used to define knowledge structures. By applying these knowledge structures, the corresponding inference mechanisms (adaptive testing algorithm) are established. In this paper, we refer to them as knowledge-structure-based adaptive testing (KSAT). In this section, the performances of adaptive testing algorithms based on the four knowledge structures with different thresholds are compared and evaluated by using adaptive test simulation processes with a paper-based test dataset to determine the best algorithm. In these simulation processes, a paper-based test is taken owing to a limitation of computer equipment. The reason for using simulation is that there are hundreds of combinations of knowledge structure-based adaptive testing (KSAT) algorithms and thresholds. Finding a real computerized dataset for each combination is not feasible. B A A 76
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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
Page 29 and 30: Mesch, G., & Elgali, Z. (2009). Soc
Page 31 and 32: Although prior research on overall
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Page 35 and 36: Table 2 demonstrates mean scores an
Page 37 and 38: Discussion Although the recent Inte
Page 39 and 40: Furthermore, the most preferred pla
Page 41 and 42: MEB. (2009). Internete erisim proje
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Page 45 and 46: practice and repetition with feedba
Page 47 and 48: Online peer assessment Peer assessm
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Page 51 and 52: � Ability to produce rich assessm
Page 53 and 54: Meishar-Tal, H., & Tal-Elhasid, E.
Page 55 and 56: Tsai, S. C. (2012). Integration of
Page 57 and 58: order to decrease text complexity a
Page 59 and 60: matter how the learners’ aptitude
Page 61 and 62: If compared by programs, WP student
Page 63 and 64: mean F2F 4.64 4.52 4.35 4.44 *: p
Page 65 and 66: een analyzed and were compared with
Page 67 and 68: Chen, G.-D., Lee, J.-H., Wang, C.-Y
Page 69 and 70: and diverse cognitional and emotion
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Page 73 and 74: Materials and procedure Each studen
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Page 121 and 122: The rationale behind the video form
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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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the influence of Test1 scores, an e
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Hall, T., & Bannon, L. (2006). Desi
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(Hofer & Pintrich, 1997; Liu, Lin &
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� Through online peer assessment,
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elativism.” High Internet self-ef
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Peng, H., Tsai, C.-C., & Wu, Y.-T.
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Since game-based learning has been
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The second reason is about the sust
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Methods Figure 5. Quest NPCs provid
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These differences indicated that pa
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quests further involves students’
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Chang, I.-H. (2012). The Effect of
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(1) Vision, planning and management
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Based on an examination of the lite
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Table 2. Analysis of reliability an
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λ10 - 0.69 0.83 ε5 14.50 * - 0.32
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Bailey, G. D. (1997). What technolo
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Stegall, P. (1998). The principal:
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instruction has studied the student
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espond and the estimation of possib
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Research question For the purpose o
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READI reliability To verify the con
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Physical -1.43333(*) .44699 .029 -2
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The results of this study suggest t
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Hsu, Y.-C., Ho, H. N. J., Tsai, C.-
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domains. Five research questions we
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Research sample groups (4) Faculty
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13. Policies, Social Culture Impact
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Non-Specified Others Engineering &
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(AR = -2.1) (n = 7) 2. Non-specifie
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(n): Total number of articles Resea
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Compared to the publications in 200
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Pedagogical Content Knowledge (TPAC
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