January 2012 Volume 15 Number 1 - Educational Technology ...

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memory cycle updates and analytic hierarchy process (AHP) to help a learner in an extensive reading environment. By intensive reading of suitable articles, a learner can maintain their interest in learning English and, at the same time, improve his/her English ability efficiently. The system architecture is shown in Figure 4. System Description The major modules for carrying out the computation procedures are the Article Preprocessing Mechanism, the Degree of Vocabulary Cognition Calculation, the Memory Cycle Calculation, the Fuzzy Inference Mechanism, the Review Value Calculation and the Article Features Calculation. The descriptions of them are given as follows: The Article Preprocessing Mechanism adds a newly collected article to the article database and computes correlations with other articles already in the database. It also calculates the frequencies of the words in the article and includes these values, along with the vocabulary, into the GEPT vocabulary database. A learner interacts with the system via a User Interface. To newcomers, the system provides a self-assessed questionnaire to identify their interests in different topics to create learner profiles in the user portfolio database. For every news article collected, the Article Features Calculation module has five generators for computing the five main feature values, i.e., the Average Difficulty of Vocabulary (ADV), the Average Length of Sentence (ALS), the Total Length (TLA), the Average Ability of Vocabulary of a learner (AAV), and the Article Correlation (AC). The five feature values are used by the Fuzzy Inference Mechanism to generate a document fitness degree for each article for each learner. The Review Value Calculation module computes the review value for each article for a given learner by counting the words that should be reviewed by that learner. The higher the value is, the greater number of words the article contains that should be reviewed by the learner. The Document Recommendation Agent calculates the recommendation score (RS) of every article for a given learner according to that learner’s interest, the document fitness degree, and the review value of the article. The system suggests the article with the highest recommendation score to a learner. After the learner reads the article, the system picks from the article words that the learner is supposed to have comprehended based on his/her ability level and generates an immediate test, which serves to check whether the learner has understood the meaning of newly-encountered words. The test result feeds back via the Learning Feedback Agent to the Degree of Vocabulary Cognition Calculation module for computing and updating the vocabulary abilities in the learner's profile in the user portfolio database; at the same time, the Memory Cycle Calculation module re-evaluates the memory cycles of the learner for the learned vocabularies that appear in the content of the article and updates them in the user portfolio database. When a learner logs in again, a suitable article, which contains words with memory cycles that are due to be reviewed, will be recommended by the Article Recommendation Agent for the learner. The details of each process are discussed as follows. User Interface User registration A learner’s English vocabulary size matters much in learning English (Mochizuki & Aizawa, 2000); in this study, the Degree of Vocabulary Cognition (DVC) is regarded as a learner’s English ability. Table 1 shows the DVC used in this research with its corresponding values. To calculate the English ability of a first-time user, the Computerized Adaptive Testing (CAT) approach is adopted. Ten questions are tested. The vocabulary for CAT is selected from elementary, intermediate, and high-intermediate levels of GEPT; 50 questions are generated for each level. A question contains one English word and four Chinese translations, among which one is correct. According to the result of the CAT and using a one-parameter IRT model and the maximum likelihood estimation method, the vocabulary volume of a learner is evaluated. The proposed system recommends English news articles of various topics collected from Internet for intensive reading. To address learners’ Degree of Article Preferences (DAP), collected news articles are classified into eight categories, including Politics, Society, Sports, Business, Arts, <strong>Technology</strong>, Health and Travel. The proposed system uses a learner-assessment interface to interact with a newcomer in order to evaluate the preferences of the learner 277
with respect to articles by using a value of DAP computed from the answers of an assessment questionnaire. A Likert scale is adapted to represent the degree of article preferences for individual learners such that 0.1 indicates “Not at all; Very much dislike”, 0.3 is “Not all”, 0.5 is “Moderately”, 0.7 is “Well” and 0.9 is “Very well”. User Learning Table 1. The degree of vocabulary cognition with its corresponding value Ranges of Vocabulary Ability of a Learner ( ) Corresponding values (DVC) 2. 0 1. 2 0 1. 2 0. 4 0.25 0. 4 0. 4 0.5 0. 4 1. 2 0.75 1. 2 2. 0 1 In this study, a web portal of the fuzzy logic-based personalized extensive English reading environment is developed. It consists of a set of hyperlinks and a left-side panel for logging in that displays the latest information on the system. In addition, a right-side panel for choosing article topics and displaying recommended articles also appears, as shown in Figure 5. After reading an article, a learner is asked for feedback, which evaluates the degree to which the recommended English article as understood, as shown at the bottom of Figure 5. More details on learning feedback are discussed in the next section. Figure 5. The recommended English article and the feedback 278
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
Page 231 and 232: In this study, the focus of the ins
Page 233 and 234: The student’s degree of mastery i
Page 235 and 236: The questionnaire for the acceptanc
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Page 239 and 240: Scale Questionnaire item Mean S.D.
Page 241 and 242: Hwang, G. J. (2003). A conceptual m
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Page 245 and 246: Virtual learning environment In the
Page 247 and 248: Class Table 1. The survey questions
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Page 251 and 252: collaboration within EVS, thus prev
Page 253 and 254: Huang, T.-H., Liu, Y.-C., & Chang,
Page 255 and 256: y personalised context examples in
Page 257 and 258: Students’ Problem-Solving Guidanc
Page 259 and 260: Research Method Figure 8. The scree
Page 261 and 262: Table 4. Abstract of Pairwise Compa
Page 263 and 264: questions designed for the system h
Page 265 and 266: Lee, J. (2012). Patterns of Interac
Page 267 and 268: This study focused on online fora i
Page 269 and 270: Results and Discussion Due date-cen
Page 271 and 272: In addition, all groups developed 8
Page 273 and 274: future. Therefore, they had an oppo
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Page 277 and 278: Pawan, F., Paulus, T. M., Yalcin, S
Page 279 and 280: systems by using 18 personalization
Page 281: Short-Term Sensory Memory is a temp
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Page 287 and 288: DFL. d ( yi ) is the degree of mem
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
Page 293 and 294: Appendix # Question Description Par
Page 295 and 296: mental models, are cognitive struct
Page 297 and 298: Based on the theoretical implicatio
Page 299 and 300: Data analysis To test the model fit
Page 301 and 302: may be considered an effective inte
Page 303 and 304: Hsu, I.-C. (2012). Intelligent Disc
Page 305 and 306: (Gradinarova, Zhelezov et al. 2006)
Page 307 and 308: RLO denotes a remote learning objec
Page 309 and 310: Figure 2. The flow-oriented LOFinde
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Page 313 and 314: Similarly, the relation metadata of
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Page 317 and 318: References ADL. (2006). Sharable Co
Page 319 and 320: determinants of how people think, b
Page 321 and 322: Mediating effect of learning flow A
Page 323 and 324: The instrument used to measure lear
Page 325 and 326: addition, the cross-loadings of the
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Page 329 and 330: Connell, J. P., Spencer, M. B., & A
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Learning is a cognitive activity th
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The survey consisted of 30 question
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Created data mining model needs to
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dealt with the matter taught at the
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Conclusion Conducted research showe
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Romero, C., Ventura S., García E.,
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prompts as scaffolding strategy to
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Reflection Types This study attempt
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All the three groups completed the
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Results Learning Outcomes Pre- and
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Map Analysis in Transfer Test Figur
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Generic Prompts and Specific Prompt
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Biswas, G., Schwartz, D., Bransford
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Chen, Y.-H., Looi, C.-K., Lin, C.-P
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of correct response, answer until c
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Figure 7 shows a screenshot of one
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Collaboration Questionnaire results
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following diagrams. The double-arro
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“Most students were encouraged to
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Bangert-Drowns, R.L., Kulick, C. C.
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primary research questions. First,
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Individual learning Figure 2. Scree
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Experimental Tools This study emplo
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Table 3 shows that almost all items
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I usually engaged myself in listeni
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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January 2012 Volume 15 Number 1 - Educational Technology ...

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