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

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Questionnaire analysis To further evaluate the usability of system developed in this study, a questionnaire that contains 18 questions was used to survey the learners who participated in the experiments. It consisted of four parts entitled “Learning motivation and attitude”, “System operation”, “Degree of satisfaction in learning” and “Learner feedback”. Each question included five answer options, including ‘Strongly Agree’, ‘Agree’, ‘Neutral’, ‘Disagree’, and ‘Strongly disagree’. Appendix A presents further details on the questionnaires and evaluations. The questionnaire is highly internal consistency and has a 0.771 of Cronbach’s alpha coefficient, which indicates a higher reliability with a higher value. Among the 18 questions, questions No. 6, 7 and 8 are only included for the experimental group, and questions No. 9 and 10 are included only for the control group. For the control group, results reveal only 30% of the learners spend most of their time searching for suitable articles according to their English ability (Median = 2.97 < 3), while 43.3% of learners spend most of their time finding articles related to their preferences (Median = 3.23 > 3). This fact indicates that some of the control group learners spent most of their time searching articles they preferred to read rather than articles they could probably have easily understood. In contrast, the results show that most of the experimental group learners said that the proposed system not only offers appropriate English articles according to their abilities and preferences (40% and 43.3%) but also reduces their effort in searching articles they prefer (60%). Although this percentage is not greater than 50%, Table 7 shows that the medians for questions No. 6 and 7 are both more than question No. 3 (3.37 and 3.43). Table 7. Descriptive statistics Question (#) Samples Total Median Standard deviation Q6 30 101 3.37 .850 Q7 30 102 3.43 .855 Q8 30 112 3.73 .785 Q9 30 89 2.97 .964 Q10 30 97 3.23 .858 Figure 10(A). Evaluation result of learner’s feedbacks of the question 17 Figure 10(B). Evaluation result of learner’s feedbacks of the question 18 285
Finally, Figure 10(A) and Figure 10(B) show the results of the survey regarding the advantages and disadvantages of the proposed system. Most of the learners (62%) thought that the proposed system could improve their English vocabulary ability. In contrast, 52% of participants suggested that there should be some listening and reading tests after they finish reading an article. As such, this provides a potential topic for future study. Conclusion In Taiwan, there are ongoing discussions and suggestions as to how to improve a student’s English ability. This paper proposes a unique approach that draws on the intensive reading to harness both fuzzy logic and memory cycleadjusting methods in order to improve personalized English learning programs. The approach uses a questionnaire to understand a learner’s preferences and then uses fuzzy inference to find article of suitable difficulty levels for the learner. It then employs review values to compute the percentage of article vocabulary that the learner should review. It combines these three parameters to establish the article’s suitability formulae to compute the suitable level of articles for the learner. The system also uses memory cycle updating to adjust the memory cycles for words that a learner learns for the first time in a given article as well as the words that appear that need to be reviewed based on the learner’s learning feedback. The results of these experiments confirm that with intensive reading of articles as recommended by the approach, a learner can remember both new words and previously-learned words easily and for longer time, thereby efficiently improving the vocabulary ability of the learner. Acknowledgements This research was supported by the National Science Council in Taiwan under project NSC 99-2511-S-006-003- MY3. References Airasian, P. W., & Gay, L. R. (2003). Educational research: Competencies analysis and application. Englewood cliffs, N. J.: Prentice-Hall. Bai, S. M., & Chen, S. M. (2008). Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert Systems with Applications, 35(1-2), 41-49. Baker, F. B. (1992). Item response theory: parameter estimation techniques. New York: Marcel Dekker. Chen, C. M., & Hsu, S. H. (2008a). Personalized intelligent mobile learning system for supporting effective English learning. Educational Technology & Society, 11(3), 153-180. Chen, C. M., & Chung, C. J. (2008b). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624-645. Carlsson, C., Fedrizzi, M., & Fuller, R. (2004). Fuzzy Logic in Management. Kluwer Academy Publisher. Ebbinghaus, H. (1885). Über das Gedächtnis. Untersuchungen zur Experimentellen Psychologie. Leipzig: Duncker & Humblot, Germany. Essalmi, F., Ayed, L. J. B., Jemni, M., Kinshuk, & Graf, S. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581-591. Ferreira, A., & Atkinson J. (2009). Designing a feedback component of an intelligent tutoring system for foreign language. Knowledge-Based Systems, 22(7), 496-501. Hajek, P. (2006). What is mathematical fuzzy logic. Fuzzy Sets and Systems, 157(5), 597-603. Huang, Y., & Bian, L. (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert Systems with Applications, 36(1), 933-943. Hsu, M. H. (2008). A personalized English learning recommender system for ESL students. Expert Systems with Applications, 34(1), 683-688. 286
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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
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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 253 and 254: Huang, T.-H., Liu, Y.-C., & Chang,
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Page 259 and 260: Research Method Figure 8. The scree
Page 261 and 262: Table 4. Abstract of Pairwise Compa
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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
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Page 293 and 294: Appendix # Question Description Par
Page 295 and 296: mental models, are cognitive struct
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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
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Page 307 and 308: RLO denotes a remote learning objec
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Page 317 and 318: References ADL. (2006). Sharable Co
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Page 321 and 322: Mediating effect of learning flow A
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Page 333 and 334: Learning is a cognitive activity th
Page 335 and 336: The survey consisted of 30 question
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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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