April 2012 Volume 15 Number 2 - Educational Technology & Society

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change would lead to unreasonable item-administration sequence in which some easier items followed a correctly changed, difficult item. These unreasonable response patterns might lead to serious ability estimation bias and decrease testing precision. The rearrangement procedure was proposed to cope with this problem by rearranging the response patterns and re-estimating examinees’ ability after answers were changed. Rearrangement procedure Papanastasiou (2002) proposed the rearrangement procedure that rearranged and skipped certain items and could better estimate the examinees’ abilities. For example, the rearrangement procedure allowed examinees to change up to five of their answers after they finished 30 items in the allotted testing time. After examinees revised their answers, the examinees’ final scores were calculated. (a) (b) Figure 4. Example of Change C�I (Papanastasiou & Reckase, 2007) Compared with the traditional CAT, three types of answer changing (Change I�I, Change I�C, and Change C�I) caused the rearrangement procedure in ability estimation. Change I�I involved changing answers from incorrect to incorrect and it would make no difference in ability estimation between the traditional CAT and the rearrangement procedure. The second type (Change I�C) involved changing answers from incorrect to correct and it would result in item skipping in the rearrangement procedure. For instance, if Change I�C was made to item i, the ability estimation ˆ ��i would be changed to ˆ ��i �. This change in ability estimation would probably make the item i+1 not the most informative item for the ability ˆ ��i � since it would be easier and was originally targeted at ability ˆ ��i rather than ˆ ��i �. To solve this problem, the ability estimation would skip to item X (e.g., item i+k, with 1
would be more accurate if items j+1 through j+k-1 were ignored from the estimation procedure, and item j+k was used after the item whose answer was changed. As Figure 4a shows, a Change I�C (incorrect-to-correct) was made to item 2 of a reviewable CAT. As a result, item 3 became an inappropriate item after the change of this answer since it was selected from the item bank on the premise that the examinee answer item 2 was wrong. In this case, item 3 was skipped because it was targeted at a lower ability level than . The algorithm therefore jumped to item 4 since that was the first more difficult item answered incorrectly that came after item 3 (see Figure 4b). The underlying hypothesis of reviewable CAT led to the fact that high-ability students might on occasion miss items that they should have answered correctly. However, almost all previous reviewable studies were carried out based on traditional CAT that assumed that a high-ability student should answer an easy question with probability approaching one. As descried above, the underlying hypothesis of reviewable CAT was consistent with the principle of 4PL model. Besides, the 4PL model might propose examinee an opportunity to recover from the inappropriate responses which introduced by reviewing and changing answer in reviewable CAT. In the present study, therefore, the effect of 4PL model and the rearrangement review solution on reducing estimation bias was investigated. Methodology In this study, a simulated experiment was conducted to evaluate the effect of implementing the 4PL model on reducing the impact of inappropriate items on reviewable CAT. The participant demography, item bank characteristics, and procedure are described as follows. Participants A group of 13,000 examinees were simulated for this study (1,000 examinees for thirteen equally spaced θ levels). The θ-level groupings ranged from -3.0 to 3.0 at equally spaced intervals of 0.5. Item bank The simulated item bank with 250 items was generated according to the specifications proposed by Papanastasiou and Reckase (2007). To determine the item pool characteristic for their simulation study, Papanastasiou and Reckase reviewed psychometric literature to obtain information on the distributions of item parameters of real item pools. Table 1 describes the targeted distributional characteristics of the item pool created for this simulation. As for the upper asymptote, a test-wide δ with value of 1 for 3PL-based CAT and 0.98 for 4PL-based CAT was determined, respectively. By designing item bank of 3PL- and 4PL-based CAT in this way, the only difference between these two models would be the value of upper asymptote, and the results would be correspondent with the intention of this study. Table 1. Distributional Characteristics of the Item Parameters Parameters Type of distribution Mean SD Minimum Maximum b Uniform 0.00 2.00 -3.50 3.50 a Log normal 1.10 0.25 0.45 2.30 c Uniform 0.17 0.10 0.00 0.35 Simulation procedure Four versions of 30-item, fixed-length CAT were developed for this simulation experiment (as Figure 5 shows). All these four CATs were reviewable CAT with 6 blocks of 5 items, and the reviewing and changing answer were allowed only within block. The first two CATs (R3CAT and R4CAT) were conducted to compare the performance 236
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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
Page 189 and 190: Methods Before the experiment, a mo
Page 191 and 192: too simple, incomplete, or they eve
Page 193 and 194: Comparisons of UMLS Questionnaire i
Page 195 and 196: already described may play an impor
Page 197 and 198: Parker, D., Manstead, A. S. R., Str
Page 199 and 200: Avci, U., & Askar, P. (2012). The C
Page 201 and 202: Literature review The related varia
Page 203 and 204: collected through Personal Informat
Page 205 and 206: Self-efficacy 92 5 21 13.37 3.726 W
Page 207 and 208: In this problem, intention was take
Page 209 and 210: References Ajjan, H., & Hartshorne,
Page 211 and 212: Tan, T.-H., Lin, M.-S., Chu, Y.-L.,
Page 213 and 214: Students perused the course materia
Page 215 and 216: problem. All articles were then sen
Page 217 and 218: tests, electronic tests are more li
Page 219 and 220: Students 10 and 11: My classmate Ed
Page 221 and 222: collection was also approved by all
Page 223 and 224: Ubiquitous Revision Seamless Collab
Page 225 and 226: Tai, Y. (2012). Contextualizing a M
Page 227 and 228: tasks were developed from task type
Page 229 and 230: experiences in this phase. Moreover
Page 231 and 232: test is administered again right af
Page 233 and 234: The collected data were analyzed wi
Page 235 and 236: Gorp, K. V., & Bogaert, N. (2006).
Page 237 and 238: 4PL IRT model According to the numb
Page 239: examinee’s ability more accuratel
Page 243 and 244: C�I or Change I�I) would be 0.2
Page 245 and 246: improved by rearrangement procedure
Page 247 and 248: Table 6. Repeated Measures ANOVA of
Page 249 and 250: Leppisaari, I., & Lee, O. (2012). M
Page 251 and 252: Selection of the virtual tool The t
Page 253 and 254: Environmental themes emerged strong
Page 255 and 256: Visualization of text (visual langu
Page 257 and 258: environments where visual technolog
Page 259 and 260: - Logging in to learning environmen
Page 261 and 262: McNaught, C., Lam, P. & Lam, S.L. (
Page 263 and 264: on-site to finally achieve a meanin
Page 265 and 266: Table 1. Digital game designers and
Page 267 and 268: worldviews are socio-cultural-histo
Page 269 and 270: Phase Methods Visitor data collecti
Page 271 and 272: operate the game without the help o
Page 273 and 274: Visitors (end users) must be heard.
Page 275 and 276: Islas Sedano, C., Pawlowski, J., Su
Page 277 and 278: part of something larger than the s
Page 279 and 280: Previous studies have developed rel
Page 281 and 282: Technology Acceptance. The 10 items
Page 283 and 284: Table 4. Model Fit Indices Model χ
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Page 287 and 288: Discussion Differing from prior stu
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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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