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To provide timely feedback, the new technology AJAX is presented for creating more efficient and interactive web applications that handle users’ requests instantly. AJAX applications do not require installing a browser plug-in, such as Java Applet and Microsoft ActiveX, but work directly with most popular browsers, allowing immediate updating of partial content on a web page when a user performs an action. When drawing an ERD, the process flows of timely feedback are as follows: Whenever a user executes a drawing step on a browser, this action triggers the local AJAX engine for submitting the request to the web server. The web application then processes this request and returns the results. After receiving the results, the browser’s partial page is updated according to the returned results. This processing flow is employed iteratively if the browser operates continuously. This AJAX feature that enables the result to return right after a student has completed a step can be used for timely feedback. The software model of the timely diagnosis feedback is shown in the right part of Fig. 6. The Diagnostic module is designed according to the descriptions of the proposed approach in the previous section. HTTP request User Interface Client Browser ASP.NET Web Application Verification Module (Correct or Incorrect) Traditional Web Application Model (Delayed Mode) Operation procedure and demonstrations HTML + CSS data Web Server JavaScript call HTTP request User Interface AJAX Engine Client Browser HTML+ CSS data XML data ASP.NET Web Application Diagnostic Module (Frequent Itemsets and Association Rules) Web Server AJAX Web Application Model (Timely Mode) Figure 6. Different software models for timely and delayed mode The operation procedure of WTDS is divided into two phases, shown in Fig. 7. First Phase Presetting correct ERD Inputting Min Support and Min Confidence Pre-testing by testees Generating frequent itemsets Generating association rules Diagnostic Database Second Phase Starting learning Performing a drawing action on ERD Determining if correct Finish ERD Figure 7. Operation stage of the IAFA system Yes Yes No No Returning a adaptive feedback immediately 235
The first phase generates a diagnostic database, which consists of the correct ERD answer, frequent itemsets, and association rules. An instructor first draws correct ERDs using the management interface of the system. The values of minimum support and minimum confidence must be settled for generating frequent itemsets and association rules, after which several testees are involved in simultaneous pretesting. Following the pretest, the system automatically generates frequent itemsets and association rules and then imports them into the database. An instructor can input hints for each frequent itemset in the management interface, as shown in Fig. 8. These hints do not contain information about correct answers; that is, they only provide clues crucial to rectifying a learner’s misconceptions about entities and relationships. These hints scaffold students to actively reflect and fix faulty concepts whenever they make mistakes during a problem-solving process. Figure 8. Inputting hints In the second phase, students begin their learning process. When a student performs a drawing on ERD, the system determines if this step is correct. If incorrect, the WTDS returns diagnostic feedback immediately to inform the student. The procedure repeats iteratively until the working ERD is finished correctly. Figure 9 illustrates the user interface for students to practice their ERDs. The functions include selecting and laying out entities, building relationships and cardinality ratio, adding attributes, setting strokes, and setting font and line colors. After laying out entities in their proper place, a student is able to build relationships between entities and their cardinality ratio. If the built relationship is incorrect, the diagnostic feedback appears immediately below. For example, as shown in Figure 9, once the student builds an incorrect relationship between Employee and Product, the feedback displays the following information: 1) Frequent Itemsets: There are R13, R12, and R14. 2) Major and likely errors and related hints: Because a major mistake is made on R14, there is a greater likelihood to make mistakes on R13 and R12. Meanwhile, the hints for R13, R12, and R14 are also provided. 3) The confidence (e.g., probability) of making errors on R12 and R13 is also shown for reference. Figure 9. User interface 236
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http://www.ifets.info ISSN: 1436-45
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Supporting Organizations Centre for
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Timely Diagnostic Feedback for Data
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Lim et al. addressed two gaps, a us
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The new found benefits of technolog
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analysis of all articles submitted
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Since some articles could not meet
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Table 2. Distribution of research t
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Research method Table 8 shows diffe
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authors concluded that mixed-method
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trends. Library and Information Sci
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Taylor, E. W. (2001). Adult Educati
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Zealand 10 paper 15 19 Using mutual
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impact on a variety of sectors arou
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Grand challenges These grand challe
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eport for details of competencies c
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learning repositories to use in con
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Acknowledgements This paper is base
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eliminating duplicates, a total of
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Findings of this review In the foll
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Table 2. Summary of empirical resea
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students’ conception of learning
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their creative capacity in designin
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which make it impossible to see how
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The content knowledge Table 3 depic
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The first revision we have made to
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Angeli, C., & Valanides, N. (2005).
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Lee, H., & Hollebrands, K. (2008).
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Rushby, N. (2013). The Future of Le
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The top ten in the 2011 survey were
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My first vision of the future is on
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References Dienstag, J. (2006). Pes
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industries, new laws, and new areas
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or distractive effects of technolog
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This may be further complicated by
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espect, the literature about school
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Tondeur, J., Van Keer, H., van Braa
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Disruptive technology When mainfram
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with the physical environment, such
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mockups, and then embedded in compu
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the global marketplace. Students in
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games, and virtual worlds are domin
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Malone, T. W. & Lepper, M. R. (1987
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education reform. In the following
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error elimination and/or refutation
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In essence, as depicted in figure 1
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of our students, who are the future
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Simon, H. (1996). The Sciences of t
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2010). Similar patterns can be obse
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To complement the literature search
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Figure 1. Domains of sustainable e-
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Discussion and conclusions This sco
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Baty, P. (2010, July 1). Global rep
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Loewenberger, P., & Bull, J. (2003)
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applied query expansion techniques
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di, 1 ≤ i ≤ n , 0 < d i ≤ 1,
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Procedure Figure 2 schematically de
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Phase 3. Wiki-based revision Throug
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Table 4. Comparison of the performa
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materials manually by working alone
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# Question 1 2 3 4 5 Table 7. Users
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Kuo, Y. H., & Huang, Y. M. (2009).
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Nevertheless, one of the major prob
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Rlj indicates the answer of the l t
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Relative weight Very low 5 ww , ∈
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Experiment and evaluation Figure 7.
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and CG (59.06 and 13.52). The resul
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Expert 1 Expert 2 Expert 3 Correctn
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The EFWA algorithm Appendix Defini
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y the fourth student correctly is:
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drA = − + − + − = 2 2 2 ( , M
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students have achieved what they wa
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environments. This gap can be bridg
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can understand, as well as a vehicl
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5. “Then move on to Loop 2.” Ea
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fact that the students could not ge
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conceptualization, and testing in n
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Soloway, E., Norris, C., Blumenfeld
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all image search queries result in
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instance, not knowing how to naviga
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displays in real time. The authors
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Significance was not found for main
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manifestation of visual literacy; a
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Mayer, R. E. (2001). Multimedia lea
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McMillen (1996) considered virtual
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Learning of area concepts in elemen
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shrink. changed. Hints and Show but
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Based on teacher observations and c
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Appendix 1. Sample items of BACT *I
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Wong, L.-H., Hsu, C.-K., Sun, J., &
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Johnson and Johnson (1994) identifi
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Design of game-based learning activ
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Findings The students exhibited hig
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We take two instances of character
Page 189 and 190: In designing Chinese-PP, we instead
Page 191 and 192: Boticki, I., Looi, C.-K., & Wong, L
Page 193 and 194: Hwang, G.-J., Sung, H.-Y., Hung, C.
Page 195 and 196: 2000). For example, Filippidis and
Page 197 and 198: amount of information about the lea
Page 199 and 200: The factors that affected the stude
Page 201 and 202: Burguillo, J. C. (2010). Using game
Page 203 and 204: Wong, L.-H. (2013). Analysis of Stu
Page 205 and 206: Recognizing both the importance and
Page 207 and 208: Student responses were coded based
Page 209 and 210: English with the translations of th
Page 211 and 212: With her mother’s support, Jane w
Page 213 and 214: LGC that could occur in any physica
Page 215 and 216: The significance of the reported st
Page 217 and 218: Chen, Y.-L., Pan, P.-R., Sung, Y.-T
Page 219 and 220: them develop new concepts to facili
Page 221 and 222: Figure 1. Visualization of how mino
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Page 225 and 226: Procedures All subjects in both gro
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Page 231 and 232: Küçüközer, H., & Kocakülah, S.
Page 233 and 234: Lin, J.-W., Lai, Y.-C., & Chuang, Y
Page 235 and 236: The related works Feedback should b
Page 237 and 238: E 2 1 R 23 N E 5 R 15 E 1 R 13 E 3
Page 239: Once the frequent itemsets have bee
Page 243 and 244: To assure pretest validity and reli
Page 245 and 246: Questionnaire and interviews To und
Page 247 and 248: Maughan, S., Peet, D., & Willmott,
Page 249 and 250: According to Angeli and Valanides (
Page 251 and 252: description, objectives, and entry
Page 253 and 254: TCK: For the fourth activity, the g
Page 255 and 256: A questionnaire focusing on Instruc
Page 257 and 258: eing aware of the importance of sel
Page 259 and 260: References Airasian, P. W., & Walsh
Page 261 and 262: Instructional Technology Instructio
Page 263 and 264: and He (2006) revealed that technol
Page 265 and 266: Model and hypothesis development Sa
Page 267 and 268: Table 1. Student profile Gender: Ma
Page 269 and 270: H3 Student satisfaction has positiv
Page 271 and 272: Conclusions Addressing four PRS iss
Page 273 and 274: Tao, Y.-H., & Yeh, R. C. (2009, Jul
Page 275 and 276: My overall experience of PRS is abs
Page 277 and 278: skills can move to the next learnin
Page 279 and 280: were divided into an experimental g
Page 281 and 282: illustrates the Monopoly gameplay m
Page 283 and 284: Comparison of monopoly and instruct
Page 285 and 286: Guskey, T. R., & Gates, S. L. (1986
Page 287 and 288: Yang, C.-C., Tseng, S.-S., Liao, A.
Page 289 and 290: text only because learners are able
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Figure 2. Structure of poetry multi
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time period. Bandwidth is measured
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Participants and experimental proce
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owses multimedia resources. Fewer p
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the other approach. Meanwhile, the
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Wang, Y.-H., Young, S. S.-C., & Jan
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mistakes in front of the teacher an
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Basic English learning materials we
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The instructor also showed positive
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H.L, M = 38.11) which occurred in b
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Discussions Tangible learning compa
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We can conclude from the current st
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Joo, Y. J., Joung, S., & Kim, E. K.
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on 506 students registered for cybe
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and management systems, learning se
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multivariate normal distribution as
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Structural model examination Table
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Direct effect Flow ← Satisfaction
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Second, the results of this study c
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Kim, H. S. (2008). The effects of t
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language accurately, or product-ori
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Thailand 5 Vietnam 25 Total 208 Res
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Procedures of data collection Figur
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discussions. From Brian’s log fil
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universities. There were 259 studen
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and he provided his arguments and r
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Within groups Total Test 4 Between
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Crooks, B. (2003). The combined use
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Appendix A 342
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teenager. Chapter four, “Identity
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