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Table 7: The results of pairwise comparisons Measure: MEASURE_1 (I) test 1 2 3 (J) test 2 3 1 3 1 2 Nahla Aljojo et al. Pairwise Comparisons Mean Difference (I-J) Std. Error Sig. -.714* .340 .045 -1.411 -.017 a 95% Confidence Interval for Difference Lower Bound Upper Bound a -.500 .670 .462 -1.875 .875 .714* .340 .045 .017 1.411 .214 .496 .669 -.803 1.231 .500 .670 .462 -.875 1.875 -.214 .496 .669 -1.231 .803 Based on estimated marginal means *. The mean difference is significant at the .05 level. a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments). 4.1.2 Result of A comparison of first case of group (A) with third case of group (A) H1: group (A) using the TASAM system with no professor explanation of the chapters correlation and Measures of Variability) will learn significantly better than group (A) using the TASAM system with professor explanation of the chapter (correlation and Measures of Variability). To determine if the students of group (A) using the TASAM system with no professor explanation significantly better than the students of group (A) using the TASAM system with professor explanation, The main results of the one way repeated measures analysis of variance are presented in Table 5. The main values for the scores students of group (A) using the TASAM system with no professor explanation and the scores students of group (A) using the TASAM system with professor explanation and standard deviation are listed in Table 5 and it appears that the main scores for the scores students of group (A) using the TASAM system with professor no explanation are much higher than for the scores students of group (A) using the TASAM system with professor explanation (12.63> 12.37). The scores students of group (A) using the TASAM system with no professor explanation and the scores students of group (A) using the TASAM system with professor explanation, it has been found that there wasn`t significant difference of scores P=.453 >0.05, see table 7. 4.1.3 Comparing the two different groups H2: students of group(B) using the TASAM system with no professor explanation of the chapter (Measures of Central tendency and Measures of Variability) will learn significantly better than group (A) not using the TASAM system with professor explanation of the chapters Measures of Central tendency. An Independent – samples t-test was conducted to evaluate the hypothesis that students of group(B) using the TASAM system with no professor explanation will learn significantly better than group (A) not using the TASAM system with professor explanation. The test was significant, t (51) =2.244, p = .029
Table: 8: Descriptive statistics Scores Type before Adaptive After Adaptive Table: 9: Independent samples test Scores Equal variances assumed Equal variances not assumed Levene's Test for Equality of Variances F Sig. 4.2 Student’s evaluation questionnaire Nahla Aljojo et al. Group Statistics N Mean Std. Deviation Std. Error Mean 28 11.7500 4.22405 .79827 25 13.7600 1.56205 .31241 Independent Samples Test t df Sig. (2-tailed) t-test for Equality of Means Mean Difference 95% Confidence Interval of the Difference Std. Error Difference Lower Upper 15.985 .000 -2.244 51 .029 -2.01000 .89562 -3.80804 -.21196 -2.345 34.981 .025 -2.01000 .85723 -3.75029 -.26971 Section 4.2 covers Test-Retest Reliability of student`s evaluation survey, Result of Student`s evaluation Survey and the final evaluation and assessment of the adaptive learning system by students. 4.2.1 Test-retest reliability of student`s evaluation survey In estimating test-retest reliability, the same test is administered to the same or similar sample, on more than one occasion. Time elapsing between the measurements is critical. Typically the longer the time-gap is, the lower the correlation. In the study, the time lapse of one month was dictated by the classroom realities, as described above. Table 10 shows a moderate to strong correlation between the test and the retest questions of evaluation. Table: 10 Paired samples correlations N Correlation Sig. Pair 1 Q1_before & Q1_after 48 -.185 .209 Pair 2 Q2_before & Q2_after 48 -.021 .888 Pair 3 Q3_before & Q3_after 42 -.013 .934 Pair 4 Q4_before & Q4_after 47 -.189 .204 Pair 5 Q5_before & Q5_after 49 .082 .576 Pair 6 Q6_before & Q6_after 48 .048 .745 Pair 7 Q7_before & Q7_after 47 -.121 .416 Pair 8 Q8_before & Q8_after 48 .011 .942 Pair 9 Q9_before & Q9_after 49 -.010 .948 Pair 10 Q10_before & Q10_after 49 .070 .633 Pair 11 Q11_before & Q11_after 49 -.096 .511 Pair 12 Q12_before & Q12_after 47 -.117 .435 Pair 13 Q13_before & Q13_after 49 -.064 .662 However, the difference between the means of answers fifth, ninth, tenth and thirteen questions were borderline significant (P = 0.002, P=.000, P=.017, P=.027 respectively ) and the correlation between the before and after of answers fifth, ninth, tenth and thirteen questions were the second lowest, at 0. .082,.010, .070 , 064 respectively (Table 11). Yet, the use of such standard statistical tools may be in 905
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Proceedings of the 10 th European C
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. A Qu
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The Project Mobile Game Based Learn
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Extreme Scaffolding in the Teaching
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Malaysia); Tuomo Kakkonen (Universi
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Preface These Proceedings represent
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Mini Track Chairs Dr Antonios Andre
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Cornélia Castro is a PhD student i
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Manuel Frutos-Perez is the Leader o
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David Mathew works at the Centre fo
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Research interests include the inve
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Novita Yulianti is a PhD student at
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Peter Mozelius et al. searching and
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Peter Mozelius et al. Error Handli
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Peter Mozelius et al. were no chanc
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Peter Mozelius et al. single login
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Antoinette Muntjewerff for every st
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Antoinette Muntjewerff All of this
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Figure 1: Metadata editor Antoinett
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Antoinette Muntjewerff The structur
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A Framework for Decision Support fo
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Phelim Murnion and Markus Helfert i
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Phelim Murnion and Markus Helfert 4
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Phelim Murnion and Markus Helfert s
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Phelim Murnion and Markus Helfert P
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Shekhar Murthy and Devi Murthy wisd
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Shekhar Murthy and Devi Murthy educ
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Shekhar Murthy and Devi Murthy Figu
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Shekhar Murthy and Devi Murthy corr
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Shekhar Murthy and Devi Murthy Figu
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Shekhar Murthy and Devi Murthy Stag
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7. Conclusions Shekhar Murthy and D
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Student's Characteristics for Note
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Minoru Nakayama et al. Learning exp
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Minoru Nakayama et al. 3.3 Effectiv
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Minoru Nakayama et al. Table 2: Cor
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Freeing Education Within and Beyond
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Chrissi Nerantzi The aim was to exp
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Chrissi Nerantzi agreeing with Nova
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Chrissi Nerantzi familiarisation wi
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Chrissi Nerantzi Scardamalia, M. an
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Paul Newbury et al. the slides or w
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Paul Newbury et al. multimedia faci
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Paul Newbury et al. It is designed
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Paul Newbury et al. Download figur
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Paul Newbury et al. “Has the fact
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eSubmission - UK Policies, Practice
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Barbara Newland et al. managing stu
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Figure 3: Technical infrastructure
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Barbara Newland et al. and I strong
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Harnessing the Internet for Authent
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Abel Nyamapfene As stated on the co
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Abel Nyamapfene 8. There were suffi
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Abel Nyamapfene Mulnix, J. W. (2011
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Maruff Akinwale Oladejo and Adelua
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2.3 Instrumentation Maruff Akinwale
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Maruff Akinwale Oladejo and Adelua
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References Maruff Akinwale Oladejo
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Adelua Olajide Olawole and Maruff A
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2.2 Sample and sampling techniques
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3.1 Result Adelua Olajide Olawole a
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Adelua Olajide Olawole and Maruff A
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Fatemeh Orooji et al. awareness and
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Fatemeh Orooji et al. Figure 1: The
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Fatemeh Orooji et al. Sensing/intu
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Fatemeh Orooji et al. lower and upp
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Fatemeh Orooji et al. the other dim
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Using Lifeworld-led Multimedia to E
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Andy Pulman et al. A key feature of
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Andy Pulman et al. in this case “
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Andy Pulman et al. The Research Evi
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The Project Mobile Game Based Learn
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Thomas Putz A hybrid game, consisti
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3.3 The different ‘events’ supp
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4. Pedagogical impacts Thomas Putz
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Thomas Putz Fabricatore, C. (2000).
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Ricardo Queirós and José Paulo Le
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3.6 Discussion Topics Ricardo Queir
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The Design and Development of an eL
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Andrik Rampun and Trevor Barker as
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Andrik Rampun and Trevor Barker add
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Figure 5: Screen shot of a user’s
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Andrik Rampun and Trevor Barker 3.1
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Andrik Rampun and Trevor Barker cou
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Kansei Design Model for eLearning:
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Fauziah Redzuan et al. presentation
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Fauziah Redzuan et al. A second sur
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Fauziah Redzuan et al. understand t
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Fauziah Redzuan et al. to explain t
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Fauziah Redzuan et al. Websites", P
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Changing Teacher Beliefs Through IC
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Bart Rienties et al. substantial di
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Bart Rienties et al. videoed for re
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5. Discussion Bart Rienties et al.
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Moodle and Affective Computing: Kno
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Manuel Rodrigues et al. Moodle does
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Manuel Rodrigues et al. Other sever
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Manuel Rodrigues et al. Doing this
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Using Google Applications to Facili
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Eleni Rossiou and Erasmia Papadopou
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Andrée Roy determines if eLearning
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Andrée Roy Table 3: Profiles of eL
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Andrée Roy the cost of production
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Andrée Roy Lawless, N., Allan, J.
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Zuzana Šaffková comment metacogni
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Zuzana Šaffková Moodle applicatio
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Zuzana Šaffková the other propert
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Zuzana Šaffková In the last phase
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Zuzana Šaffková prior knowledge,
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A Mobile aid Tool for Crafting Acti
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Ahmed Salem Question difficulty in
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Ahmed Salem Figure 2: The Simple le
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Ahmed Salem Table 1: The two groups
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King-Sized eLearning - how Effectiv
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Marie Sams et al. MOB was set up in
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Marie Sams et al. attaching the new
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Marie Sams et al. include time cons
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Rowena Santiago et al. However, whe
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Rowena Santiago et al. 6.1.2 Assign
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Rowena Santiago et al. the original
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Rowena Santiago et al. Together, th
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Vitor Santos and Luis Amaral To bui
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Vitor Santos and Luis Amaral Althou
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Figure 4: Technical architecture Vi
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Implementing and Evaluating Problem
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Maggi Savin-Baden et al. different
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Maggi Savin-Baden et al. and second
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Maggi Savin-Baden et al. important
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The Evolution of eLearning Platform
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Adriana Schiopoiu Burlea et al. Hyp
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Adriana Schiopoiu Burlea et al. Tab
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Adriana Schiopoiu Burlea et al. Eve
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Teachers’ Skills set for Personal
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Zaffar Ahmed Shaikh and Shakeel Ahm
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Zaffar Ahmed Shaikh and Shakeel Ahm
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6. Conclusion and recommendations Z
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Bridging the Feedback Divide Utilis
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Angela Shapiro and Aidan Johnston f
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Angela Shapiro and Aidan Johnston F
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Post-Academic Masters Course in Man
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4. Discussion and conclusion Cees T
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Engagement With Students in ‘Midd
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Anne Smith and Sonya Campbell x y c
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4.3 Student led connectivity Anne S
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Figure 2: Conceptualising ‘middle
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The Learning Management System as a
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Dina Soeiro et al. He was older, ol
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Dina Soeiro et al. specially about
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Can the Medium Extend the Message?
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Mekala Soosay elements under study,
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Mekala Soosay to self and peer-asse
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Mekala Soosay flexible web-based ap
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Implementation and Analysis of an O
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Iain Stewart et al. No-one was con
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Iain Stewart et al. The content sh
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Iain Stewart et al. entry per stude
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Iain Stewart et al. Dolnicar, S, Sh
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2. The research Caroline Stockman a
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Caroline Stockman and Fred Truyen S
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Caroline Stockman and Fred Truyen n
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Caroline Stockman and Fred Truyen s
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PeerWise - The Marmite of Veterinar
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Amanda Sykes et al. recently outlin
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Amanda Sykes et al. activity became
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Amanda Sykes et al. students either
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Category Amanda Sykes et al. Q9 : W
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Amanda Sykes et al. Chang, S-B., Hu
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Nicolet Theunissen and Hester Stubb
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Figure 1: Overall structure Nicolet
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References Nicolet Theunissen and H
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Tone Vold This idea phase was under
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Tone Vold process. A synchronous di
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The Effects of Self-Directed Learni
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Chien-hwa Wang and Cheng-ping Chen
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Page 372 and 373: Usage Cases: A Useful way to Improv
Page 374 and 375: Cristina Wanzeller and Orlando Belo
Page 380 and 381: The Virtual Path to Academic Transi
Page 382 and 383: Julie Watson course overall, with s
Page 384 and 385: Julie Watson practical and economic
Page 386 and 387: 6. First phase Julie Watson Followi
Page 388 and 389: Identifying and Locating Frames of
Page 390 and 391: Ethical approval Katherine Wimpenny
Page 392 and 393: Katherine Wimpenny et al. SL was a
Page 394 and 395: Katherine Wimpenny et al. This in t
Page 396 and 397: Reaction Lecture: Text Messaging to
Page 398 and 399: Koos Winnips et al. order to make a
Page 400 and 401: Table 2: Overview of student reacti
Page 402 and 403: Koos Winnips et al. Students liked
Page 404 and 405: A Holistic Approach to Instructiona
Page 406 and 407: Li Zhong Zhang There is a wealth of
Page 408 and 409: Figure 1: An integrated design fram
Page 410 and 411: 6.1 Design implications Li Zhong Zh
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Page 414 and 415: 896
Page 416 and 417: Nahla Aljojo et al. Adaptive hyperm
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Page 424 and 425: Nahla Aljojo et al. fact misleading
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Page 430 and 431: Andy Coverdale A former interdisci
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Page 436 and 437: Michael Flavin In a subsequent work
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Page 440 and 441: Michael Flavin The lecturer also sp
Page 442 and 443: Michael Flavin Quality Assurance Ag
Page 444 and 445: Fortunate Gunzo and Lorenzo Dalvit
Page 446 and 447: 4. Possible scenarios 4.1 Computer
Page 448 and 449: Fortunate Gunzo and Lorenzo Dalvit
Page 450 and 451: Collaborative eLearning in a Develo
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Page 458 and 459: Evelyn Kigozi Kahiigi et al. consid
Page 460 and 461: Evelyn Kigozi Kahiigi et al. So, H.
Page 462 and 463: Fabio Serenelli et al. is to identi
Page 464 and 465: Figure 3: Screenshots of LO3 - Lear
Page 466 and 467: Managing Essential Processing Foste
Page 468 and 469: 6. Results and analysis Fabio Seren
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1.2 VLE (Virtual Learning Environme
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Nazime Tuncay and Hüseyin Uzunboyl
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Figure 4 Response Screen Nazime Tun
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Figure 12 An Example of Recorded Vi
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Figure 15 VirtualNeu General Menus
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972
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Liz Falconer and Manuel Frutos-Pere
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Liz Falconer and Manuel Frutos-Pere
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Extreme Scaffolding in the Teaching
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Dan-Adrian German first exposure to
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Benefits and Barriers: Applying eLe
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Simon McGinnes view on demand. OSD
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986
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988
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Olivia Billingham 2010); gains in c
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Olivia Billingham Deneen, L., (2010
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Karin Levinsen et al. conference or
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Karin Levinsen et al. in order to s
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Peps Mccrea professional engagement
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A Framework for Understanding Onlin
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Sónia Sousa et al. little sense re
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Sónia Sousa et al. Figure 4: The e
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Trust in Distributed Personal Learn
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2.2 LePress Sónia Sousa et al. Per
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Sónia Sousa et al. 3. Elicitation
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Breaking Down Barriers: Development
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Karen Strickland et al. qualitative
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Novita Yulianti et al. Research con
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