Volume Two - Academic Conferences

More documents

Recommendations

Info

Nahla Aljojo et al. Adaptive hypermedia research has received more attention during the last two decades, however it still is unclear which aspects of learning styles are worth modeling, and what can be done differently for users with different learning styles. An adaptive e-learning hypermedia is an approach whose target is to personalize the learning experience for the learner (De Bra et al., 2004; Henze and Nejdl, 2004). A number of adaptive educational systems have been developed based on learning styles as a source for adaptation, including: AEC-CS (Trantafillou et al., 2002), INSPIRE (Grigoriadou et al., 2001), iWeaver (Wolf, 2003), MASPLANG (Peña et al, 2002; Peña, 2004), LSAS (Bajraktarevic et al, 2003), EDUCE (Kelly, 2005) and ILASH (Bajraktarevic et al., 2003a). One of the key challenges in such adaptive learning systems is the development of robust experimental evaluation mechanisms to assess their impact on student's achievement. For instance, Brown et al. (2009) investigated adaptive e-learning hypermedia that specially utilizes learning style as their adaptation mechanism; they found that out of 10 systems, 6 systems did not seem to have published any quantitative evaluations in their recent research. Typical would be AES-CS (Triantafillou et al., 2003) and INSPIRE (Papanikolaou et al., 2003) which uses some empirical data in the form of descriptive statistics and but no inferential statistics testing, and the number of users were relatively small (n = 10 and n = 23, respectively). A common evaluation approach involves comparing performance on an adaptive learning system with non-adaptive versions for different cohorts of users. However, there are many challenges with comparing non-adaptive with adaptive version of learning systems (De Bra, 2000). Any difference between the groups’ performances might be attributed to users’ features (e.g. initial knowledge, goals etc) or wider environment. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia system and an approach to evaluate the impact of such a learning system. This paper hopes to make contribution by presenting a further case study of a dedicated adaptive educational system and providing guidance and discussion on both development issues and how to evaluate the effectiveness of an adaptive learning system. Existing adaptive learning systems are predominantly English based. This paper hopes to make further contribution by bring adaptive learning capability to on-English speaking communities. 1.2 Felder-Silverman Learning Style Model In Felder-Silverman learning style model (FSLSM) (Felder and Silverman, 1988), learners are characterized by values on four dimensions. Table 1: summarizes learning environment preferences of typical learners from each of the four dimensions of the Felder Silverman model. The FSLSM provides a good base to develop adaptive learning systems (Aljojo and Adams 2009; Aljojo and Adams 2010) and was used as the base in the Teacher Assisting and Subject Adaptive Material (TASAM) system described in this paper. Table 1: Felder’s learning dimensions Active Explains to others Tries things out, works within a group, discusses and Reflective Thinks before doing something, works alone Sensing Learns from and memorizes facts, solves problems by well-established methods, patient with details, works slower Intuitive Discovers possibilities and relationships, innovative, easily grasp new concepts, abstractions and mathematical formulation, works faster Visual Learns from pictures, diagrams, flow charts, time lines, films, multimedia content and demonstrations Verbal Learns form written and spoken explanations Sequential Learns and thinks in linear/sequential steps Global Learns in large leaps, absorbing material almost randomly The reasons to choose this learning style theory in this research are: Its Index of Learning Style (ILS) questionnaire (Felder and Soloman, 2003) provides a convenient and practical approach to establish the dominant learning style of each student. The results of ILS can be linked easily to adaptive environments (Paredes And Rodriguez, 2002). It is most appropriate and feasible to be implemented for hypermedia courseware (Carver, et al., 1999; Kinshuk and Lin, 2003). 898
2. TASAM design and production 2.1 System architecture Nahla Aljojo et al. The main characteristic of TASAM is that it can be adapted to the learning style. The system was organized in the form of three basic components: The domain model, the learner model and the adaptation model. These three components interacted to adapt different aspects of the instructional process. Figure 1 illustrates the system architecture. Learner Model SQL Database Adaption Model Visual basic.net SQL server 2005 Web Browser Domain Model Active Server Pages 3.5 Figure 1: Illustrates the system architecture When learners enter TASAM for the first time, they signed up to the system by using a registration form. Once a learner registers, a learner profile will be created to store all his/her information and will be saved in the database, a unique identification (ID) is generated for the learner. After that he/she will submit the answered questionnaire to get the results that will show his/her learning style. TASAM uses an Arabic version of the Felder and Soloman’s (1997) Index of Learning Styles (ILS) to generate the learning profile which consists of a personal preference for each of the four dimensions of FSLSM expressed with values between +11 to -11 per dimension (see figures 2&3). TASAM adapts the content sequencing of the course material to match the learning style profile for the student. The lesson contents appear in the navigation area as tree-like structure of hyperlinks, whilst in the content area the learning content is presented by the media matched for the learner preference. TASAM offered many signs to prevent learner from getting lost. First, the learning tree shows already visited pages in a different color (purple instead of blue). Secondly, the learner typically progresses through TASAM in a hierarchical manner. As the learning tree grows, new pages will be added below the last branch. The new branch expands and the first content page is displayed when the learner enters a new lesson. Finally, link annotations have been added to learning contents to show the currently viewed content pages. Learning tree: The learning content was accessible in a hierarchical, tree-like fashion with the aid of a collapsible Active Server Pages tree menu .The tree grew with the progression of the learner. Intra lesson navigation: A small navigation bar offered “previous” and “next” arrows for the content pages of the current lesson. Learning content: The central screen area was reserved for the learning content, presented in the different teaching strategies and electronic Media. 899
Page 1 and 2:
Proceedings of the 10 th European C
Page 3 and 4:
Contents Paper Title Author(s) Page
Page 5 and 6:
Paper Title Author(s) Page No. A Qu
Page 7 and 8:
The Project Mobile Game Based Learn
Page 9 and 10:
Extreme Scaffolding in the Teaching
Page 11 and 12:
Malaysia); Tuomo Kakkonen (Universi
Page 13 and 14:
Preface These Proceedings represent
Page 15 and 16:
Mini Track Chairs Dr Antonios Andre
Page 17 and 18:
Cornélia Castro is a PhD student i
Page 19 and 20:
Manuel Frutos-Perez is the Leader o
Page 21 and 22:
David Mathew works at the Centre fo
Page 23 and 24:
Research interests include the inve
Page 25:
Novita Yulianti is a PhD student at
Page 28 and 29:
Peter Mozelius et al. searching and
Page 30 and 31:
Peter Mozelius et al. Error Handli
Page 32 and 33:
Peter Mozelius et al. were no chanc
Page 34 and 35:
Peter Mozelius et al. single login
Page 36 and 37:
Antoinette Muntjewerff for every st
Page 38 and 39:
Antoinette Muntjewerff All of this
Page 40 and 41:
Figure 1: Metadata editor Antoinett
Page 42 and 43:
Antoinette Muntjewerff The structur
Page 44 and 45:
A Framework for Decision Support fo
Page 46 and 47:
Phelim Murnion and Markus Helfert i
Page 48 and 49:
Phelim Murnion and Markus Helfert 4
Page 50 and 51:
Phelim Murnion and Markus Helfert s
Page 52 and 53:
Phelim Murnion and Markus Helfert P
Page 54 and 55:
Shekhar Murthy and Devi Murthy wisd
Page 56 and 57:
Shekhar Murthy and Devi Murthy educ
Page 58 and 59:
Shekhar Murthy and Devi Murthy Figu
Page 60 and 61:
Shekhar Murthy and Devi Murthy corr
Page 62 and 63:
Shekhar Murthy and Devi Murthy Figu
Page 64 and 65:
Shekhar Murthy and Devi Murthy Stag
Page 66 and 67:
7. Conclusions Shekhar Murthy and D
Page 68 and 69:
Student's Characteristics for Note
Page 70 and 71:
Minoru Nakayama et al. Learning exp
Page 72 and 73:
Minoru Nakayama et al. 3.3 Effectiv
Page 74 and 75:
Minoru Nakayama et al. Table 2: Cor
Page 76 and 77:
Freeing Education Within and Beyond
Page 78 and 79:
Chrissi Nerantzi The aim was to exp
Page 80 and 81:
Chrissi Nerantzi agreeing with Nova
Page 82 and 83:
Chrissi Nerantzi familiarisation wi
Page 84 and 85:
Chrissi Nerantzi Scardamalia, M. an
Page 86 and 87:
Paul Newbury et al. the slides or w
Page 88 and 89:
Paul Newbury et al. multimedia faci
Page 90 and 91:
Paul Newbury et al. It is designed
Page 92 and 93:
Paul Newbury et al. Download figur
Page 94 and 95:
Paul Newbury et al. “Has the fact
Page 96 and 97:
eSubmission - UK Policies, Practice
Page 98 and 99:
Barbara Newland et al. managing stu
Page 100 and 101:
Figure 3: Technical infrastructure
Page 102 and 103:
Barbara Newland et al. and I strong
Page 104 and 105:
Harnessing the Internet for Authent
Page 106 and 107:
Abel Nyamapfene As stated on the co
Page 108 and 109:
Abel Nyamapfene 8. There were suffi
Page 110 and 111:
Abel Nyamapfene Mulnix, J. W. (2011
Page 112 and 113:
Maruff Akinwale Oladejo and Adelua
Page 114 and 115:
2.3 Instrumentation Maruff Akinwale
Page 116 and 117:
Maruff Akinwale Oladejo and Adelua
Page 118 and 119:
References Maruff Akinwale Oladejo
Page 120 and 121:
Adelua Olajide Olawole and Maruff A
Page 122 and 123:
2.2 Sample and sampling techniques
Page 124 and 125:
3.1 Result Adelua Olajide Olawole a
Page 126 and 127:
Adelua Olajide Olawole and Maruff A
Page 128 and 129:
Fatemeh Orooji et al. awareness and
Page 130 and 131:
Fatemeh Orooji et al. Figure 1: The
Page 132 and 133:
Fatemeh Orooji et al. Sensing/intu
Page 134 and 135:
Fatemeh Orooji et al. lower and upp
Page 136 and 137:
Fatemeh Orooji et al. the other dim
Page 138 and 139:
Using Lifeworld-led Multimedia to E
Page 140 and 141:
Andy Pulman et al. A key feature of
Page 142 and 143:
Andy Pulman et al. in this case “
Page 144 and 145:
Andy Pulman et al. The Research Evi
Page 146 and 147:
The Project Mobile Game Based Learn
Page 148 and 149:
Thomas Putz A hybrid game, consisti
Page 150 and 151:
3.3 The different ‘events’ supp
Page 152 and 153:
4. Pedagogical impacts Thomas Putz
Page 154 and 155:
Thomas Putz Fabricatore, C. (2000).
Page 156 and 157:
Ricardo Queirós and José Paulo Le
Page 158 and 159:
Page 160 and 161:
3.6 Discussion Topics Ricardo Queir
Page 162 and 163:
Page 164 and 165:
The Design and Development of an eL
Page 166 and 167:
Andrik Rampun and Trevor Barker as
Page 168 and 169:
Andrik Rampun and Trevor Barker add
Page 170 and 171:
Figure 5: Screen shot of a user’s
Page 172 and 173:
Andrik Rampun and Trevor Barker 3.1
Page 174 and 175:
Andrik Rampun and Trevor Barker cou
Page 176 and 177:
Kansei Design Model for eLearning:
Page 178 and 179:
Fauziah Redzuan et al. presentation
Page 180 and 181:
Fauziah Redzuan et al. A second sur
Page 182 and 183:
Fauziah Redzuan et al. understand t
Page 184 and 185:
Fauziah Redzuan et al. to explain t
Page 186 and 187:
Fauziah Redzuan et al. Websites", P
Page 188 and 189:
Changing Teacher Beliefs Through IC
Page 190 and 191:
Bart Rienties et al. substantial di
Page 192 and 193:
Bart Rienties et al. videoed for re
Page 194 and 195:
5. Discussion Bart Rienties et al.
Page 196 and 197:
Moodle and Affective Computing: Kno
Page 198 and 199:
Manuel Rodrigues et al. Moodle does
Page 200 and 201:
Manuel Rodrigues et al. Other sever
Page 202 and 203:
Manuel Rodrigues et al. Doing this
Page 204 and 205:
Using Google Applications to Facili
Page 206 and 207:
Eleni Rossiou and Erasmia Papadopou
Page 208 and 209:
Page 210 and 211:
Page 212 and 213:
Page 214 and 215:
Page 216 and 217:
Andrée Roy determines if eLearning
Page 218 and 219:
Andrée Roy Table 3: Profiles of eL
Page 220 and 221:
Andrée Roy the cost of production
Page 222 and 223:
Andrée Roy Lawless, N., Allan, J.
Page 224 and 225:
Zuzana Šaffková comment metacogni
Page 226 and 227:
Zuzana Šaffková Moodle applicatio
Page 228 and 229:
Zuzana Šaffková the other propert
Page 230 and 231:
Zuzana Šaffková In the last phase
Page 232 and 233:
Zuzana Šaffková prior knowledge,
Page 234 and 235:
A Mobile aid Tool for Crafting Acti
Page 236 and 237:
Ahmed Salem Question difficulty in
Page 238 and 239:
Ahmed Salem Figure 2: The Simple le
Page 240 and 241:
Ahmed Salem Table 1: The two groups
Page 242 and 243:
King-Sized eLearning - how Effectiv
Page 244 and 245:
Marie Sams et al. MOB was set up in
Page 246 and 247:
Marie Sams et al. attaching the new
Page 248 and 249:
Marie Sams et al. include time cons
Page 250 and 251:
Rowena Santiago et al. However, whe
Page 252 and 253:
Rowena Santiago et al. 6.1.2 Assign
Page 254 and 255:
Rowena Santiago et al. the original
Page 256 and 257:
Rowena Santiago et al. Together, th
Page 258 and 259:
Vitor Santos and Luis Amaral To bui
Page 260 and 261:
Vitor Santos and Luis Amaral Althou
Page 262 and 263:
Figure 4: Technical architecture Vi
Page 264 and 265:
Implementing and Evaluating Problem
Page 266 and 267:
Maggi Savin-Baden et al. different
Page 268 and 269:
Maggi Savin-Baden et al. and second
Page 270 and 271:
Maggi Savin-Baden et al. important
Page 272 and 273:
The Evolution of eLearning Platform
Page 274 and 275:
Adriana Schiopoiu Burlea et al. Hyp
Page 276 and 277:
Adriana Schiopoiu Burlea et al. Tab
Page 278 and 279:
Adriana Schiopoiu Burlea et al. Eve
Page 280 and 281:
Teachers’ Skills set for Personal
Page 282 and 283:
Zaffar Ahmed Shaikh and Shakeel Ahm
Page 284 and 285:
Zaffar Ahmed Shaikh and Shakeel Ahm
Page 286 and 287:
6. Conclusion and recommendations Z
Page 288 and 289:
Bridging the Feedback Divide Utilis
Page 290 and 291:
Angela Shapiro and Aidan Johnston f
Page 292 and 293:
Angela Shapiro and Aidan Johnston F
Page 294 and 295:
Post-Academic Masters Course in Man
Page 296 and 297:
4. Discussion and conclusion Cees T
Page 298 and 299:
Engagement With Students in ‘Midd
Page 300 and 301:
Anne Smith and Sonya Campbell x y c
Page 302 and 303:
4.3 Student led connectivity Anne S
Page 304 and 305:
Figure 2: Conceptualising ‘middle
Page 306 and 307:
The Learning Management System as a
Page 308 and 309:
Dina Soeiro et al. He was older, ol
Page 310 and 311:
Dina Soeiro et al. specially about
Page 312 and 313:
Can the Medium Extend the Message?
Page 314 and 315:
Mekala Soosay elements under study,
Page 316 and 317:
Mekala Soosay to self and peer-asse
Page 318 and 319:
Mekala Soosay flexible web-based ap
Page 320 and 321:
Implementation and Analysis of an O
Page 322 and 323:
Iain Stewart et al. No-one was con
Page 324 and 325:
Iain Stewart et al. The content sh
Page 326 and 327:
Iain Stewart et al. entry per stude
Page 328 and 329:
Iain Stewart et al. Dolnicar, S, Sh
Page 330 and 331:
2. The research Caroline Stockman a
Page 332 and 333:
Caroline Stockman and Fred Truyen S
Page 334 and 335:
Caroline Stockman and Fred Truyen n
Page 336 and 337:
Caroline Stockman and Fred Truyen s
Page 338 and 339:
PeerWise - The Marmite of Veterinar
Page 340 and 341:
Amanda Sykes et al. recently outlin
Page 342 and 343:
Amanda Sykes et al. activity became
Page 344 and 345:
Amanda Sykes et al. students either
Page 346 and 347:
Category Amanda Sykes et al. Q9 : W
Page 348 and 349:
Amanda Sykes et al. Chang, S-B., Hu
Page 350 and 351:
Nicolet Theunissen and Hester Stubb
Page 352 and 353:
Figure 1: Overall structure Nicolet
Page 354 and 355:
Page 356 and 357:
Page 358 and 359:
References Nicolet Theunissen and H
Page 360 and 361:
Tone Vold This idea phase was under
Page 362 and 363:
Tone Vold process. A synchronous di
Page 364 and 365:
The Effects of Self-Directed Learni
Page 366 and 367: Chien-hwa Wang and Cheng-ping Chen
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
Page 412 and 413: 894
Page 414 and 415: 896
Page 418 and 419: Nahla Aljojo et al. Email: The emai
Page 420 and 421: Nahla Aljojo et al. Table 3: 16 typ
Page 422 and 423: Table 7: The results of pairwise co
Page 424 and 425: Nahla Aljojo et al. fact misleading
Page 426 and 427: Nahla Aljojo et al. Aljojo, N. & Ad
Page 428 and 429: Andy Coverdale media and related on
Page 430 and 431: Andy Coverdale A former interdisci
Page 432 and 433: Andy Coverdale Whilst new initiativ
Page 434 and 435: Andy Coverdale the participant inte
Page 436 and 437: Michael Flavin In a subsequent work
Page 438 and 439: Michael Flavin labour” nodes) in
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
Page 452 and 453: Evelyn Kigozi Kahiigi et al. implem
Page 454 and 455: Evelyn Kigozi Kahiigi et al. includ
Page 456 and 457: Evelyn Kigozi Kahiigi et al. had ve
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
Page 470 and 471:
Fabio Serenelli et al. Mangiatordi,
Page 472 and 473:
1.2 VLE (Virtual Learning Environme
Page 474 and 475:
Nazime Tuncay and Hüseyin Uzunboyl
Page 476 and 477:
Page 478 and 479:
Figure 4 Response Screen Nazime Tun
Page 480 and 481:
Page 482 and 483:
Figure 12 An Example of Recorded Vi
Page 484 and 485:
Figure 15 VirtualNeu General Menus
Page 486 and 487:
Page 488 and 489:
Page 490 and 491:
972
Page 492 and 493:
Liz Falconer and Manuel Frutos-Pere
Page 494 and 495:
Liz Falconer and Manuel Frutos-Pere
Page 496 and 497:
Extreme Scaffolding in the Teaching
Page 498 and 499:
Dan-Adrian German first exposure to
Page 500 and 501:
Benefits and Barriers: Applying eLe
Page 502 and 503:
Simon McGinnes view on demand. OSD
Page 504 and 505:
986
Page 506 and 507:
988
Page 508 and 509:
Olivia Billingham 2010); gains in c
Page 510 and 511:
Olivia Billingham Deneen, L., (2010
Page 512 and 513:
Karin Levinsen et al. conference or
Page 514 and 515:
Karin Levinsen et al. in order to s
Page 516 and 517:
Peps Mccrea professional engagement
Page 518 and 519:
A Framework for Understanding Onlin
Page 520 and 521:
Sónia Sousa et al. little sense re
Page 522 and 523:
Sónia Sousa et al. Figure 4: The e
Page 524 and 525:
Trust in Distributed Personal Learn
Page 526 and 527:
2.2 LePress Sónia Sousa et al. Per
Page 528 and 529:
Sónia Sousa et al. 3. Elicitation
Page 530 and 531:
Breaking Down Barriers: Development
Page 532 and 533:
Karen Strickland et al. qualitative
Page 534 and 535:
Novita Yulianti et al. Research con
show all

Volume Two - Academic Conferences

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?