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

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Structural matching [STM] Structural indicator Gamma matching [GAM] Structural indicator Concept matching [CCM] Semantic indicator Propositional matching [PPM] Semantic indicator Balanced propositional matching [BPM] Semantic indicator Results The structural matching (Pirnay-Dummer & Ifenthaler, 2010) compares the complete structures of two graphs without regard to their content. This measure is necessary for all hypotheses which make assumptions about general features of structure (e.g. assumptions which state that expert knowledge is structured differently from novice knowledge). The gamma or density of vertices (Pirnay-Dummer & Ifenthaler, 2010) describes the quotient of terms per vertex within a graph. Since both graphs which connect every term with each other term (everything with everything) and graphs which only connect pairs of terms can be considered weak models, a medium density is expected for most good working models. Concept matching (Pirnay-Dummer & Ifenthaler, 2010) compares the sets of concepts (vertices) within a graph to determine the use of terms. This measure is especially important for different groups which operate in the same domain (e.g. use the same textbook). It determines differences in language use between the models. The propositional matching (Ifenthaler, 2010a) value compares only fully identical propositions between two graphs. It is a good measure for quantifying semantic similarity between two graphs. The balanced propositional matching (Pirnay-Dummer & Ifenthaler, 2010) is the quotient of propositional matching and concept matching. In specific cases (e.g., when focusing on complex causal relationships), balanced propositional matching could be preferred over propositional matching. Initial data checks showed that the distributions of ratings and scores satisfied the assumptions underlying the analysis procedures. All effects were assessed at the .05 level. As effect size measures, we used Cohen’s d (small effect: d < .50, medium effect .50 ≤ d ≤ .80, strong effect d > .80) and partial ƞ 2 (small effect: ƞ 2 < .06, medium effect .06 ≤ ƞ 2 ≤ .13, strong effect ƞ 2 > .13). More than half of the participants (58%) did not use concept maps to structure their own learning materials before our experiment. Only 5% of the participants used concept mapping software to create their own concept maps beforehand. On the other hand, over 60% of the participants answered that they did not find it difficult to create a concept map. Consequently, there was no significant difference in the learning outcome as measured by the domainspecific knowledge posttest between participants who used concept mapping software before the experiment and those who did not use concept mapping software at all, t(96) = .105, ns. Domain-specific knowledge On the domain-specific knowledge test (pre- and posttest), participants could score a maximum of 13 correct answers. In the pretest they scored an average of M = 4.38 correct answers (SD = 1.71) and in the posttest M = 6.71 correct answers (SD = 2.49). The increase in correct answers was significant, t(97) = 9.611, p < .001, d = 1.068. ANOVA was used to test for knowledge gain differences among the three experimental groups. The experimental groups did not differ with regard to the results in the pretest, F(2, 95) = 2.14, p > .05. However, the increase in correct answers differed significantly across the three experimental groups, F(2, 95) = 8.21, p = .001, 2 = .147. Tukey HSD post-hoc comparisons of the three groups indicate that the generic prompt group (M = 3.66, SD = 2.40, 95% CI [2.79, 4.52]) gained significantly more correct answers than the directed prompt group (M = 1.68, SD = 2.14, 95% CI [.99, 2.36]), p = .001, and the control group (M = 1.73, SD = 2.20, 95% CI [.84, 2.62]), p = .005. Comparisons between the directed prompt group and the control group were not statistically significant at p < .05. Accordingly, the results support the hypothesis that participants who receive generic prompts outperform those in other groups with regard to their domain-specific understanding. 45
HIMATT structural measures The participants’ understanding of the problem scenario as illustrated by concept maps was analyzed automatically with the HIMATT tool. The four structural measures reported in Table 2 show the average similarity between the participants’ solution and the referent solution (expert concept map). Four separate ANOVAs (for HIMATT measures SFM, GRM, STM, GAM) with Tukey HSD post-hoc comparisons were computed to test for differences between the three experimental groups. ANOVA revealed a significant difference between participants in the three experimental groups for the HIMATT measure STM, F(2, 95) = 7.77, p = .001, 2 = .141. Tukey HSD post-hoc comparisons of the three groups indicate that the complete structure (STM) of the generic prompt group’s concept maps (M = .84, SD = .14, 95% CI [.79, .89]) was significantly more similar to the expert solution than that of the directed prompt group’s maps (M = .70, SD = .14, 95% CI [.66, .75]), p = .001. Additionally, the complete structure (STM) of the control group’s concept maps (M = .80, SD = .19, 95% CI [.73, .88]) was significantly more similar to the expert solution than that of the directed prompt group’s maps, p = .026. Comparisons between the directed prompt group and the control group were not statistically significant at p < .05. For the HIMATT measure GAM, ANOVA revealed a significant difference between the three experimental groups, F(2, 95) = 5.49, p = .006, 2 = .104. Tukey HSD post-hoc comparisons of the three groups indicate that the density of vertices (GAM) of the generic prompt group’s concept maps (M = .83, SD = .10, 95% CI [.79, .87]) was significantly more similar to the expert solution than that of the directed prompt group’s maps (M = .70, SD = .19, 95% CI [.64, .76]), p = .004. All other comparisons between groups were not statistically significant at p < .05. ANOVAs for the HIMATT measures SFM and GRM revealed no significant differences between the experimental groups. Accordingly, the results support the hypothesis that participants who receive generic prompts outperform participants in other groups with regard to the HIMATT measures STM and GAM. Table 2. Means (SD) HIMATT structural measures for the three experimental groups (N = 98) GP (n1 = 32) DP (n2 = 40) CG (n3 = 26) Surface matching [SFM] .73 (.19) .60 (.25) .68 (.28) Graphical matching [GRM] .77 (.18) .72 (.21) .71 (.21) Structural matching [STM] .84 (.14) .70 (.14) .80 (.19) Gamma matching [GAM] .83 (.10) .70 (.19) .74 (.19) Note. HIMATT similarity measures between participant’s solution and expert’s solution (0 = no similarity; 1 = total similarity); GP = generic prompt, DP = directed prompt, CG = control group HIMATT semantic measures Additional HIMATT analysis for the semantic measures of the participants’ understanding of the problem scenario as expressed by concept maps was computed. The three semantic measures reported in Table 3 show the average similarity between the participants’ solution and the referent solution (expert concept map). Three separate ANOVAs (for HIMATT measures CCM, PPM, BPM) with Tukey HSD post-hoc comparisons were computed to test for differences between the three experimental groups. ANOVA revealed a significant difference between participants in the three experimental groups for the HIMATT measure CCM, F(2, 95) = 7.40, p = .001, 2 = .135. Tukey HSD post-hoc comparisons of the three groups indicate that the semantic correctness of single concepts used in the concept maps (CCM) of the generic prompt group (M = .43, SD = .19, 95% CI [.37, .50]) was significantly more similar to the expert solution than in those of the directed prompt group (M = .30, SD = .14, 95% CI [.26, .34]), p = .001, and the control group (M = .31, SD = .<strong>15</strong>, 95% CI [.25, .37]), p = .011. Comparisons between the directed prompt group and the control group were not statistically significant at p < .05. ANOVA revealed a significant difference between participants in the three experimental groups for the HIMATT measure PPM, F(2, 95) = 10.80, p < .001, 2 = .185. Tukey HSD post-hoc comparisons of the three groups indicate that the semantic correctness of propositions (concept-link-concept) used in the concept maps (PPM) of the generic prompt group (M = .17, SD = .16, 95% CI [.11, .23]) was significantly more similar to the expert solution than in 46
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 and 38: Results Detection rates As seen in
Page 39 and 40: 2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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: deductive reasoning inventory (33 m
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
Page 89 and 90: The canonical variable for the emot
Page 91 and 92: Literature in the field of online l
Page 93 and 94: Cornelius, R. R. (1996). The scienc
Page 95 and 96: Shih, W.-C., Tseng, S.-S., Yang, C.
Page 97 and 98: To provide personalized (e-)learnin
Page 99 and 100: 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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January 2012 Volume 15 Number 1 - Educational Technology ...

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