Views
7 months ago

Advances in E-learning-Experiences and Methodologies

Chapter IX AI Techniques

Chapter IX AI Techniques for Monitoring Student Learning Process David Camacho Universidad Autonoma de Madrid, Spain Álvaro Ortigosa Universidad Autonoma de Madrid, Spain Estrella Pulido Universidad Autonoma de Madrid, Spain María D. R-Moreno Universidad de Alcalá, Spain ABstrAct The evolution of new information technologies has originated new possibilities to develop pedagogical methodologies that provide the necessary knowledge and skills in the higher education environment. These technologies are built around the use of Internet and other new technologies, such as virtual education, distance learning, and long-life learning. This chapter focuses on several traditional artificial intelligence (AI) techniques, such as automated planning and scheduling, and how they can be applied to pedagogical and educational environments. The chapter describes both the main issues related to AI techniques and e-learning technologies, and how long-life learning processes and problems can be represented and managed by using an AI-based approach. Copyright © 2008, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.

AI Techniques for Monitoring Student Learning Process IntroductIon The e-learning (Clark, 2001; Kozma, 1991; Meyen et al., 2002) research field has become a hot topic in recent years. Many educators have seen it as a way to re-use previous courses stored in a database, or in other electronic formats (Schmitz, Staab, Studer, Stumme, & Tane, 2002), and to give flexibility to existing ones. Moreover, the increasing computing power and the available network infrastructure allows sharing and distributing these courses among public institutions and private corporations. These new educational approaches are evolving to use the new information technologies, and the Internet, as a virtual platform where all the involved people can implement new ways of communication. Current e-learning techniques are modifying the traditional learning environment with a classroom, desktops with students, and a blackboard. These new techniques offer individualised contents and learning methodologies, which traditional courses cannot provide, and allow advanced learners to speed through or bypass contents that are redundant, whereas beginners slow down through them (Small & Lohrasbi, 2003). The progress made by each student can be monitored in order to determine the main problems that the students face when studying the units of a course. By knowing those problems, it is possible to propose e-learning activities that can improve the quality of the learning process and, as a consequence, improve the learning designs. A learning design (LD) can be defined as an application of a pedagogical model for a specific learning objective, a target group, and a specific context or knowledge domain (Koper & Olivier, 2004). Different systems have been implemented to help course designers to specify and implement LDs. Two examples are the open-source system learning activity management system, or LAMS (LAMS, 2006), or the course management system Moodle (Moodle, 2006), which supports sequences of activities that can be both adaptive and collaborative. The different research works in the e-learning area led to the development of the IMS Learning design specification which is currently used as a standard format for learning designs (IMS LD, 2006). This specification is based on a metalanguage which allows modelling learning processes. In IMS LD model concepts like roles, activities, or environments are defined for describing learning designs. In higher education, the increasing tendency is to create virtual learning environments (VLE) which are designed to facilitate teachers the management tasks of educational courses for their students. This increasing number of platforms, systems and tools related to virtual education has led to the creation of different e-learning standards. These standards, such as SCORM (2006), have been developed to facilitate the utilization (and reutilization) of teaching materials (through the definition and creation of learning objects). Currently, these technologies and standards are mature enough to incorporate innovative techniques that could provide new mechanisms to deal with learning processes. The new virtual learning environments provide an interesting field for different kinds of researchers. We will focus on artificial intelligence (AI) researchers that can experiment with their automatic problem solving algorithms, or develop and design new algorithms in this complex domain; and educational researchers that can use a new kind of tools and techniques that could aid to detect, reason, and solve (automatically) deficiencies detected in their initial learning designs. One of the areas of AI most suitable to be applied within this context is the automated planning and scheduling. Planning techniques generate a plan (sequence or parallelization of activities) that achieves a set of goals given an initial state and satisfies a set of domain constraints represented by operators schemas. In scheduling systems, activities are organised along the time line by having in mind the resources available. These systems can perfectly handle temporal reasoning 0

  • Page 2 and 3:

    Advances in E-Learning: Experiences

  • Page 4 and 5:

    Table of Contents Preface .........

  • Page 6 and 7:

    Chapter XIV Open Source LMS Customi

  • Page 8 and 9:

    Chapter III Philosophical and Epist

  • Page 10 and 11:

    of constructive and cooperative met

  • Page 12 and 13:

    Chapter XIV Open Source LMS Customi

  • Page 14 and 15:

    contents, learning contexts, proces

  • Page 16 and 17:

    xv these organizations do not get a

  • Page 18 and 19:

    xvii QuALIty In e-LeArnIng Before t

  • Page 20 and 21:

    allow that the teachers in training

  • Page 22 and 23:

    xxi ISO. (1986). Quality-Vocabulary

  • Page 24 and 25:

    Chapter I RAPAD: A Reflective and P

  • Page 26 and 27:

    RAPAD in fields such as law, engine

  • Page 28 and 29:

    RAPAD mystery to the new student. B

  • Page 30 and 31:

    RAPAD example, whereas Laurillard h

  • Page 32 and 33:

    RAPAD Ontologically, systems philos

  • Page 34 and 35:

    RAPAD information related processes

  • Page 36 and 37:

    RAPAD methods and techniques accord

  • Page 38 and 39:

    RAPAD 2. An introduction to learnin

  • Page 40 and 41:

    RAPAD then asked to reflect on and

  • Page 42 and 43:

    RAPAD Figure 4. A rich picture to h

  • Page 44 and 45:

    RAPAD Again using techniques from t

  • Page 46 and 47:

    RAPAD university preparation course

  • Page 48 and 49:

    RAPAD The third interface is at the

  • Page 50 and 51:

    RAPAD Knight, P.T., & Trowler, P. (

  • Page 52 and 53:

    RAPAD AddItIonAL reAdIngs Goodyear,

  • Page 54 and 55:

    A Heideggerian View on E-Learning t

  • Page 56 and 57:

    A Heideggerian View on E-Learning (

  • Page 58 and 59:

    A Heideggerian View on E-Learning s

  • Page 60 and 61:

    A Heideggerian View on E-Learning r

  • Page 62 and 63:

    A Heideggerian View on E-Learning o

  • Page 64 and 65:

    A Heideggerian View on E-Learning n

  • Page 66 and 67:

    A Heideggerian View on E-Learning M

  • Page 68 and 69:

    A Heideggerian View on E-Learning W

  • Page 70 and 71:

    Philisophical and Epistemological B

  • Page 72 and 73:

    Philisophical and Epistemological B

  • Page 74 and 75:

    Philisophical and Epistemological B

  • Page 76 and 77:

    Philisophical and Epistemological B

  • Page 78 and 79:

    Philisophical and Epistemological B

  • Page 80 and 81:

    Philisophical and Epistemological B

  • Page 82 and 83:

    Philisophical and Epistemological B

  • Page 84 and 85:

    Chapter IV E-Mentoring: An Extended

  • Page 86 and 87:

    E-Mentoring However, what is unders

  • Page 88 and 89:

    E-Mentoring baugh, & Williams, 2004

  • Page 90 and 91:

    E-Mentoring Table 2. Contact. Diffe

  • Page 92 and 93:

    E-Mentoring Table 10. Ethical impli

  • Page 94 and 95:

    E-Mentoring Table 15. Technology st

  • Page 96 and 97:

    E-Mentoring Table 21. Coaching. Bes

  • Page 98 and 99:

    E-Mentoring Table 27. Moment. Best

  • Page 100 and 101:

    E-Mentoring Moreover, existing rese

  • Page 102 and 103:

    E-Mentoring Kasprisin, C. A., Singl

  • Page 104 and 105:

    E-Mentoring Ensher, E. A., Heun, C.

  • Page 106 and 107:

    Chapter V Training Teachers for E-L

  • Page 108 and 109:

    Training Teachers for E-Learning FL

  • Page 110 and 111:

    Training Teachers for E-Learning ne

  • Page 112 and 113:

    Training Teachers for E-Learning A

  • Page 114 and 115:

    Training Teachers for E-Learning yo

  • Page 116 and 117:

    Training Teachers for E-Learning Di

  • Page 118 and 119:

    Training Teachers for E-Learning ht

  • Page 120 and 121:

    The Role of Institutional Factors i

  • Page 122 and 123: The Role of Institutional Factors i
  • Page 124 and 125: The Role of Institutional Factors i
  • Page 126 and 127: The Role of Institutional Factors i
  • Page 128 and 129: The Role of Institutional Factors i
  • Page 130 and 131: The Role of Institutional Factors i
  • Page 132 and 133: The Role of Institutional Factors i
  • Page 134 and 135: The Role of Institutional Factors i
  • Page 136 and 137: E-Learning Value and Student Experi
  • Page 138 and 139: E-Learning Value and Student Experi
  • Page 140 and 141: E-Learning Value and Student Experi
  • Page 142 and 143: E-Learning Value and Student Experi
  • Page 144 and 145: E-Learning Value and Student Experi
  • Page 146 and 147: E-Learning Value and Student Experi
  • Page 148 and 149: E-Learning Value and Student Experi
  • Page 150 and 151: E-Learning Value and Student Experi
  • Page 152 and 153: E-Learning Value and Student Experi
  • Page 154 and 155: E-Learning Value and Student Experi
  • Page 156 and 157: Integrating Technology and Research
  • Page 158 and 159: Integrating Technology and Research
  • Page 160 and 161: Integrating Technology and Research
  • Page 162 and 163: Integrating Technology and Research
  • Page 164 and 165: Integrating Technology and Research
  • Page 166 and 167: Integrating Technology and Research
  • Page 168 and 169: Integrating Technology and Research
  • Page 170 and 171: Integrating Technology and Research
  • Page 174 and 175: AI Techniques for Monitoring Studen
  • Page 176 and 177: AI Techniques for Monitoring Studen
  • Page 178 and 179: AI Techniques for Monitoring Studen
  • Page 180 and 181: AI Techniques for Monitoring Studen
  • Page 182 and 183: AI Techniques for Monitoring Studen
  • Page 184 and 185: AI Techniques for Monitoring Studen
  • Page 186 and 187: AI Techniques for Monitoring Studen
  • Page 188 and 189: AI Techniques for Monitoring Studen
  • Page 190 and 191: AI Techniques for Monitoring Studen
  • Page 192 and 193: AI Techniques for Monitoring Studen
  • Page 194 and 195: AI Techniques for Monitoring Studen
  • Page 196 and 197: Chapter X Knowledge Discovery from
  • Page 198 and 199: Knowledge Discovery from E-Learning
  • Page 200 and 201: Knowledge Discovery from E-Learning
  • Page 202 and 203: Knowledge Discovery from E-Learning
  • Page 204 and 205: Knowledge Discovery from E-Learning
  • Page 206 and 207: Knowledge Discovery from E-Learning
  • Page 208 and 209: Knowledge Discovery from E-Learning
  • Page 210 and 211: Knowledge Discovery from E-Learning
  • Page 212 and 213: Knowledge Discovery from E-Learning
  • Page 214 and 215: Knowledge Discovery from E-Learning
  • Page 216 and 217: Knowledge Discovery from E-Learning
  • Page 218 and 219: Knowledge Discovery from E-Learning
  • Page 220 and 221: Knowledge Discovery from E-Learning
  • Page 222 and 223:

    Chapter XI Swarm-Based Techniques i

  • Page 224 and 225:

    Swarm-Based Techniques in E-Learnin

  • Page 226 and 227:

    Swarm-Based Techniques in E-Learnin

  • Page 228 and 229:

    Swarm-Based Techniques in E-Learnin

  • Page 230 and 231:

    Swarm-Based Techniques in E-Learnin

  • Page 232 and 233:

    Swarm-Based Techniques in E-Learnin

  • Page 234 and 235:

    Swarm-Based Techniques in E-Learnin

  • Page 236 and 237:

    Chapter XII E-Learning 2.0: The Lea

  • Page 238 and 239:

    E-Learning 2.0 Table 1. Different s

  • Page 240 and 241:

    E-Learning 2.0 Figure 1. Difference

  • Page 242 and 243:

    E-Learning 2.0 where the blog is al

  • Page 244 and 245:

    E-Learning 2.0 process. Along this

  • Page 246 and 247:

    E-Learning 2.0 forth, and, of cours

  • Page 248 and 249:

    E-Learning 2.0 Finally, it is impor

  • Page 250 and 251:

    E-Learning 2.0 never be a hotchpotc

  • Page 252 and 253:

    E-Learning 2.0 McPherson, K. (2006)

  • Page 254 and 255:

    E-Learning 2.0 Rosen, A. (2006). Te

  • Page 256 and 257:

    Telematic Environments and Competit

  • Page 258 and 259:

    Telematic Environments and Competit

  • Page 260 and 261:

    Telematic Environments and Competit

  • Page 262 and 263:

    Telematic Environments and Competit

  • Page 264 and 265:

    Telematic Environments and Competit

  • Page 266 and 267:

    Telematic Environments and Competit

  • Page 268 and 269:

    Telematic Environments and Competit

  • Page 270 and 271:

    Telematic Environments and Competit

  • Page 272 and 273:

    Telematic Environments and Competit

  • Page 274 and 275:

    Open Source LMS Customization Intro

  • Page 276 and 277:

    Open Source LMS Customization or ev

  • Page 278 and 279:

    Open Source LMS Customization compa

  • Page 280 and 281:

    Open Source LMS Customization Figur

  • Page 282 and 283:

    Open Source LMS Customization Figur

  • Page 284 and 285:

    Open Source LMS Customization Figur

  • Page 286 and 287:

    Open Source LMS Customization Haina

  • Page 288 and 289:

    Evaluation and Effective Learning p

  • Page 290 and 291:

    Evaluation and Effective Learning r

  • Page 292 and 293:

    Evaluation and Effective Learning t

  • Page 294 and 295:

    Evaluation and Effective Learning p

  • Page 296 and 297:

    Evaluation and Effective Learning m

  • Page 298 and 299:

    Evaluation and Effective Learning c

  • Page 300 and 301:

    Evaluation and Effective Learning H

  • Page 302 and 303:

    Chapter XVI Formative Online Assess

  • Page 304 and 305:

    Formative Online Assessment in E-Le

  • Page 306 and 307:

    Formative Online Assessment in E-Le

  • Page 308 and 309:

    Formative Online Assessment in E-Le

  • Page 310 and 311:

    Formative Online Assessment in E-Le

  • Page 312 and 313:

    Formative Online Assessment in E-Le

  • Page 314 and 315:

    Formative Online Assessment in E-Le

  • Page 316 and 317:

    Formative Online Assessment in E-Le

  • Page 318 and 319:

    Formative Online Assessment in E-Le

  • Page 320 and 321:

    Formative Online Assessment in E-Le

  • Page 322 and 323:

    Formative Online Assessment in E-Le

  • Page 324 and 325:

    0 Chapter XVII Designing an Online

  • Page 326 and 327:

    Designing an Online Assessment in E

  • Page 328 and 329:

    Designing an Online Assessment in E

  • Page 330 and 331:

    Designing an Online Assessment in E

  • Page 332 and 333:

    Designing an Online Assessment in E

  • Page 334 and 335:

    Designing an Online Assessment in E

  • Page 336 and 337:

    Designing an Online Assessment in E

  • Page 338 and 339:

    Designing an Online Assessment in E

  • Page 340 and 341:

    Designing an Online Assessment in E

  • Page 342 and 343:

    Quality Assessment of E-Facilitator

  • Page 344 and 345:

    Quality Assessment of E-Facilitator

  • Page 346 and 347:

    Quality Assessment of E-Facilitator

  • Page 348 and 349:

    Quality Assessment of E-Facilitator

  • Page 350 and 351:

    Quality Assessment of E-Facilitator

  • Page 352 and 353:

    Chapter XIX E-QUAL: A Proposal to M

  • Page 354 and 355:

    E-QUAL is proposed to evaluate the

  • Page 356 and 357:

    E-QUAL provide competent, service-o

  • Page 358 and 359:

    E-QUAL 2004; Scalan, 2003) and qual

  • Page 360 and 361:

    E-QUAL benchmarks address technolog

  • Page 362 and 363:

    E-QUAL E-learning added two differe

  • Page 364 and 365:

    E-QUAL Table 6. Application of the

  • Page 366 and 367:

    E-QUAL Future trends The future of

  • Page 368 and 369:

    E-QUAL (EQO) co-located to the 4 th

  • Page 370 and 371:

    E-QUAL SMEs: An analysis of e-learn

  • Page 372 and 373:

    E-QUAL Meyer, K. A. (2002). Quality

  • Page 374 and 375:

    Compilation of References Argyris,

  • Page 376 and 377:

    Compilation of References Biggs, J.

  • Page 378 and 379:

    Compilation of References Cabero, J

  • Page 380 and 381:

    Compilation of References Comezaña

  • Page 382 and 383:

    Compilation of References Downes, S

  • Page 384 and 385:

    Compilation of References Fandos, M

  • Page 386 and 387:

    Compilation of References national

  • Page 388 and 389:

    Compilation of References Hudson, B

  • Page 390 and 391:

    Compilation of References Harbour.

  • Page 392 and 393:

    Compilation of References Little, J

  • Page 394 and 395:

    Compilation of References Metros, S

  • Page 396 and 397:

    Compilation of References ONeill, K

  • Page 398 and 399:

    Compilation of References Preece, J

  • Page 400 and 401:

    Compilation of References Sadler, D

  • Page 402 and 403:

    Compilation of References Shin, N.,

  • Page 404 and 405:

    Compilation of References tional Co

  • Page 406 and 407:

    Compilation of References Vermetten

  • Page 408 and 409:

    Compilation of References Yu, F. Y.

  • Page 410 and 411:

    About the Contributors Juan Pablo d

  • Page 412 and 413:

    About the Contributors part: “An

  • Page 414 and 415:

    About the Contributors María D. R-

  • Page 416 and 417:

    About the Contributors Applications

  • Page 418 and 419:

    Index e-learning tools, automated p

  • Page 420:

    Socrates 55 Sophists 55 student-foc

MTM Advanced Pharmacy Practice Experience in Ambulatory Care
The Joy of Learning - ACT - Advanced Communication Technologies
Methodology for Monitoring and Assessment of the Level of ...
Organizational Change Management Methodology
slides - Advanced Distributed Learning
What is the student experience of learning in practice? - ECE
A Study of Community Hazard Risk Assessment Methodologies - Ning
Teacher's Handbook on e-Assessment - Office for Learning and ...
TOP - IRC - ACT - Advanced Communication Technologies
Methodological Framework for Vulnerability ... - SEA Change CoP
DIRECTOR OF UCL CENTRE FOR ADVANCEMENT OF LEARNING AND TEACHING ...
Reading 2009 - African American Communication and Collaboration ...
Non-inferiority trials: advances in concepts and methodology
October 2009 Volume 12 Number 4 - Educational Technology ...
Getting Started With Blended Learning ( PDF 2.7 ... - Griffith University
The Quality Turn. Political and Methodological Challenges in ...
PDF: Advances in the Management of Dupuytren's ... - CMEcorner.com
ADVANCES in DATA NETWORKS - Wseas.us
Overview of ISU Learning Communities
Online Communication in Language Learning and Teaching
Cooperative Learning - NIE Digital Repository - National Institute of ...
Risk Analysis: Advancing Analysis - The Society for Risk Analysis
Special Education Directors' Meeting - ESC2 Special Education
Teaching and Assessing Soft Skills - MASS - Measuring and ...
October 2006 Volume 9 Number 4
October 2006 Volume 9 Number 4 - CiteSeerX