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

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Stability of Indices Figure 1. Estimated marginal means of detection rates by indices Table 2 shows a summary of one-way MANOVA results of indices for the AT condition (Wilk’s Lambda = .690, p < .001, partial 2 = .169). As in evident from the table, except for the Wc&Bs indices, all indices performed more sensitively on the “both spuriously high and low” aberrances than on the “spuriously high” or “spuriously low” aberrances. Note that three IRT-based indices, lz, ECI4z, and OUTFITz, varied greatly under this condition with partial 2 values of .21, .13, and .23, respectively. This implies that these three IRT-based indices are more unstable across different types of aberrances than the five group-based indices did. Although ECI2z and INFITz exhibited the lowest effect size, their detection rates were still very small. This does not make much sense. With respect to the group-based indices, the BW indices exhibited similar detection rates to Wc&Bs for spuriously “high,” “low,” and “both” score aberrances (Mean = .91, .89, and, .95 respectively). Since the BW indices exhibited a relatively low effect size (partial meaning. This indicated that the BW indices were stable across the three types of aberrances. 2 = .01), the detection rate differences among aberrant types did not have much practical Table 2. One-way MANOVA for the types of aberrances (AT) condition (N = 81,000) Index AT Mean SD df F partial 2 Comparisons 1 SCI a .97 .12 2 2 1288.12*** .03 Both>High>Low b .98 .10 3 c .92 .21 1 .96 .<strong>15</strong> MCI 2 .96 .16 2 2993.40*** .07 Both>High>Low 3 .84 .30 1 .64 .40 NCI 2 .71 .39 2 755.71*** .02 Both>High>Low 3 .57 .42 1 .99 .05 Wc&Bs 2 .98 .11 2 258.35*** .01 High >Both= Low 3 .98 .11 1 .91 .23 BW 2 .95 .17 2 519.23*** .01 Both>High>Low 3 .89 .24 l z 1 2 3 .77 .79 .39 .35 .35 .40 2 10629.56*** .21 Both>High>Low ECI 2 1 .00 .04 2 330.30*** .01 Both>High>Low Z 33
2 .00 .05 3 .02 .10 1 .60 .40 ECI 4 Z 2 3 .62 .28 .41 .37 2 6278.41*** .13 Both>High>Low INFIT Z 1 2 3 .00 .00 .00 .02 .01 .01 2 1 4.9 .00 Both>High)>Low 1 .64 .39 OUTFIT Z 2 .73 .38 2 12214.40*** .23 Both>High)>Low 3 .25 .35 Note: a “spuriously high” aberrances, b “both spuriously high and low” aberrances, and c “spuriously low” aberrances. ***p < .001. Regarding the condition of aberrant severity, Table 3 shows a summary of the results of a one-way MANOVA for the indices under the AS condition (Wilk’s Lambda = .604, p < .001, 2 = .223). The NCI and ECI4z indices had large effect sizes (partial 2 = .34, and .12, respectively). This may indicate that these two indices were more influenced by different levels of aberrant severity than other indices. Even though ECI2z and INFITz exhibited the lowest effect sizes of all indices, their detection rates were very low. This does not make much sense. On the other hand, the BW indices exhibited high detection rates (.95, .92, and .89 for different levels of aberrance severity, respectively) but low effect size (partial 2 = .01). This indicates that the differences in detection rates among levels of aberrant sensitivity did not contain much practical meaning; furthermore, it reveals the strong detection power and stability of the BW indices across different levels of aberrant severity. Table 3. One-way MANOVA for the aberrant severity (AS) condition (N = 81,000) Index AS Mean SD df F partial 2 Comparisons 1 SCI a .90 .23 2 2 2822.87*** .07 30% >20% >10% b .98 .10 3 c .99 .06 1 .83 .30 MCI 2 .95 .17 2 3271.02*** .07 30% >20% >10% 3 .97 .13 1 .32 .37 NCI 2 .70 .36 2 20917.06*** .34 30% >20% >10% 3 .89 .24 1 .95 .16 Wc&Bs 2 .99 .03 2 1990.97*** .05 30% >20% >10% 3 .99 .03 1 .95 .16 BW 2 .92 .21 2 5<strong>15</strong>.97*** .01 10% >20% >30% 3 .89 .26 l z 1 2 3 .49 .70 .76 .42 .39 .37 2 3416.95*** .08 30% >20% >10% ECI 2 Z 1 2 3 .01 .01 .01 .07 .06 .07 2 3.85 .00 30% >20% >10% 1 .30 .37 ECI 4 Z 2 3 .56 .65 .41 .41 2 5782.52*** .12 30% >20% >10% 1 .00 .00 INFIT Z 2 3 .00 .00 .00 .00 2 22.84*** .00 30% >20% >10% OUTFIT 1 .41 .41 2 1987.38*** .05 30% >20% >10% Z 34
Page 1 and 2: January 2012 Volume 15 Number 1
Page 3 and 4: Supporting Organizations Centre for
Page 5 and 6: Analyzing the Learning Process of a
Page 7 and 8: Angeli, C., & Valanides, N. (2012).
Page 9 and 10: ased on an integration and evaluati
Page 11 and 12: ased on the assumption that no sing
Page 13 and 14: Diagram type D thinking (shown in F
Page 15 and 16: collaborative task in terms of inte
Page 17 and 18: Table 4. Descriptive statistics for
Page 19 and 20: Hong, N. S., Jonassen, D. H., & McG
Page 21 and 22: commercial off-the-shelf digital ga
Page 23 and 24: The post-questionnaire was used aft
Page 25 and 26: Phase Table 1. Phases and activitie
Page 27 and 28: Research results Describing student
Page 29 and 30: Assessment results for each one of
Page 31 and 32: References Annetta, L.A., Minogue,
Page 33 and 34: Huang, T.-W. (2012). Aberrance Dete
Page 35 and 36: Detection power Typically, the rela
Page 37: Results Detection rates As seen in
Page 41 and 42: their easily understandable devices
Page 43 and 44: Ifenthaler, D. (2012). Determining
Page 45 and 46: activities is assumed to be reflect
Page 47 and 48: that the problem representations (i
Page 49 and 50: deductive reasoning inventory (33 m
Page 51 and 52: HIMATT structural measures The part
Page 53 and 54: outcomes, r = .297, p < .01. Accord
Page 55 and 56: Azevedo, R. (2009). Theoretical, co
Page 57 and 58: Seel, N. M. (1999b). Educational se
Page 59 and 60: Initially this paper explores defin
Page 61 and 62: gamers were boys, and 35% were girl
Page 63 and 64: Teachers underwent eight hours of p
Page 65 and 66: A correlation analysis was used to
Page 67 and 68: playing the game. This would mean P
Page 69 and 70: Yu, F. Y. (2012). Any Effects of Di
Page 71 and 72: Considering that identity concealme
Page 73 and 74: In the nickname group, the student
Page 75 and 76: Attitudes toward assessors Percepti
Page 77 and 78: Liu, Z. F., Chiu, C. H., Lin, S. S.
Page 79 and 80: Appendix A: Attitudes toward Peer-A
Page 81 and 82: Appendix C: Perception toward the I
Page 83 and 84: Han, H., & Johnson, S. D. (2012). R
Page 85 and 86: Research participants The target po
Page 87 and 88: 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
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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sharing, problem solving, and achie
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Virtual learning environment In the
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Class Table 1. The survey questions
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“…the distance and the lack of
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collaboration within EVS, thus prev
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Huang, T.-H., Liu, Y.-C., & Chang,
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y personalised context examples in
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Students’ Problem-Solving Guidanc
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Research Method Figure 8. The scree
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Table 4. Abstract of Pairwise Compa
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questions designed for the system h
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Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Mediating effect of learning flow A
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The instrument used to measure lear
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addition, the cross-loadings of the
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and ease of use had significant eff
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Connell, J. P., Spencer, M. B., & A
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Despotović-Zrakić, M., Marković,
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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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