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Advances in E-learning-Experiences and Methodologies

AI Techniques for

AI Techniques for Monitoring Student Learning Process and resource consumption, together with some quality criteria (usually centred around time or resource consumption) but they cannot produce the required activities and their precedence relations given that they lack an expressive language to represent activities. These techniques have been applied with success in different real (and complex) environments such as industry, robotics, or information retrieval. Of special interest in the last few years has been the development of autonomous architectures (Muscettola, Dorais, Fry, Levinson, & Plaunt, 2002) that can carry out a large number of functions, such as tracking a spacecraft’s internal hardware or rover’s position, ensuring the correct working, and repairing when possible, without (or little) human intervention. In these new operation models, scientists and engineers communicate the high-level goals to the spacecraft or to the rovers, which are translated into planning and/or scheduling sequences. Then, a continuous status checking is performed in order to detect any damage, and, finally, the plan is executed. These systems must also have the capability to understand that the errors occurred during the process of accomplishing the goals. This chapter shows how this kind of AIbased techniques can be appropriately used into e-learning, and more specifically into virtual education or VLE domains (Sicilia, Sánchez- Alonso, & García-Barriocanal, 2006). We will apply these techniques to a specific e-learning tool called Task-Vased Adaptive Learner Guidance on the Web (TANGOW) developed by some of the authors of this chapter (Carro, Pulido, & Rodriguez, 1999b). rePresentAtIon FormALIsms In LeArnIng domAIns In this section we will describe different formalisms that have been used in e-learning systems to represent (a) the learning area (domain model) which includes the course concepts and the relationships between them, and (b) the current situation of a given learner with respect to the whole learning process. These models will be later considered, by using a particular e-learning tool, to understand how traditional AI techniques can be incorporated into a particular e-learning system. Several standards and guides have been proposed related to learning object metadata, student profiles, course sequencing, and so forth. The IEEE Learning Technology Standards Committee (LTSC, 2006) has developed the learning object metadata (LOM, 2006) standard which specifies the attributes required to describe a learning object, where a learning object is defined as any entity, digital or nondigital, which can be used, re-used or referenced during technology supported learning. Relevant attributes of learning objects to be described include type of object, author, owner, terms of distribution, format, and pedagogical attributes, such as teaching or interaction style. The standard also defines how LOM records should be represented in XML and RDF. Promoting Multimedia Access to Education and Training in European Society (PROMETEUS) tries to apply the IEEE LTSC standards into Europe context and cultures. Another specification which allows the modelling of learning processes is the learning design (LD) information model (IMS LD, 2006) from the IMS Global Learning consortium. A learning design is a description of a method enabling learners to attain certain learning objectives by performing certain learning activities in a certain order in the context of a certain learning environment. LD is designed to integrate with other existing specifications. Among these, it is worth mentioning the IMS content packaging (IMS CP, 2006), which can be used to describe a learning unit. A learning unit can have prerequisites which specify the overall entry requirements for learners to follow that unit. In addition, a learning unit can have different components such as roles and activities.

AI Techniques for Monitoring Student Learning Process Roles allow the type of participant in a unit of learning to be specified. There are two basic role types: learner and staff. Activities describe the actions a role has to undertake within a specified environment composed of learning objects. LD also integrates the IMS simple sequencing (IMS SS, 2006), which can be used to sequence the resources within a learning object as well as the different learning objects and services within an environment. Content is organized into a hierarchical structure where each activity may include one or more child activities. Each activity has an associated set of sequencing rules which describe how the activity or how the children of the activity are used to create the desired learning experience. The learning process can be described as the process of traversing the activity tree, applying the sequencing rules, to determine the activities to deliver to the learner. The general format of a sequencing rule can be expressed informally as: if condition set then action. There may be multiple conditions. Conditions may be combined with a single and combination (all conditions must be true) or a single or combination (only one condition must be true). Individual condition values may be negated before being combined in the rule evaluation. The U.S. Federal Government Advanced Distributed Learning (ADL) initiative has also proposed a model called shareable courseware object reference model (SCORM) which describes how the Department of Defense will use learning technologies to build, and use the learning environment of the future. The standard defines what is called “Learning Object Metadata,” which is a dictionary of tags that are used to described learning content in a variety of ways. For a given learning object, these metadata describe, for example, what the content is, who owns it, what costs (if any), technical requirements, educational purpose, and so forth. The order in which learning objects are presented to the learner is specified by using sequencing rules. A sequencing rule has the format if condition then action, where condition can be chosen among, for example, “completed,” “score less than,” or “time limit exceeded.” On the other hand, an action could be, for example, “skip,” “disable,” or “hide from choice.” The Aviation Industry CBT (Computer-Based Training) Committee (AICC) is in charge of developing guidelines for the aviation industry in the development, delivery, and evaluation of CBT and related training technologies. In addition to these standards, there are other specific proposals such as the GRAFCET representation formalism described in M’tir, Jeribi, Rumpler, and Ghazala (2004), which uses a graph to represent the sequences of course concepts and the possible learning itineraries. Other existing work (Ahmad, Basir, & Hassanein, 2004) uses fuzzy logic to relate attributes in the learner module and concepts in the domain model. The motivation for the use of fuzzy logic is that it is appropriate for representing and reasoning with vague concepts and that the formalisation of the level of understanding of a given concept by a learner is an inherently vague process. AutomAted Processes In e-LeArnIng tooLs Most of the current VLE contain prefixed courses where the user navigates and learns the concepts that they have been planned for. Some e-learning tools include situation learning (SL) courses where the user is presented with different predefined situations where the user has to choose among different options. The drawback of this type of course is that nothing is dynamically generated and a lot of effort is required to create challenging situations that keep the user’s attention. Although the instructors can get statistics as well as other information about the student progress, there is still a lack of feedback among the previous users, the tool, the instructors, what the user is interesting in, and the future users. Among the tools that have worked on this direc-

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    Advances in E-Learning: Experiences

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

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    Chapter XIV Open Source LMS Customi

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    Chapter III Philosophical and Epist

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    of constructive and cooperative met

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    Chapter XIV Open Source LMS Customi

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    contents, learning contexts, proces

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    xv these organizations do not get a

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    xvii QuALIty In e-LeArnIng Before t

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    allow that the teachers in training

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    xxi ISO. (1986). Quality-Vocabulary

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    Chapter I RAPAD: A Reflective and P

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    RAPAD in fields such as law, engine

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    RAPAD mystery to the new student. B

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    RAPAD example, whereas Laurillard h

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    RAPAD Ontologically, systems philos

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    RAPAD information related processes

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    RAPAD methods and techniques accord

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    RAPAD 2. An introduction to learnin

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    RAPAD then asked to reflect on and

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    RAPAD Figure 4. A rich picture to h

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    RAPAD Again using techniques from t

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    RAPAD university preparation course

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    RAPAD The third interface is at the

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

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    RAPAD AddItIonAL reAdIngs Goodyear,

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    A Heideggerian View on E-Learning t

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    A Heideggerian View on E-Learning (

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    A Heideggerian View on E-Learning s

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    A Heideggerian View on E-Learning r

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    A Heideggerian View on E-Learning o

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    A Heideggerian View on E-Learning n

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    A Heideggerian View on E-Learning M

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    A Heideggerian View on E-Learning W

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Philisophical and Epistemological B

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    Chapter IV E-Mentoring: An Extended

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    E-Mentoring However, what is unders

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    E-Mentoring baugh, & Williams, 2004

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    E-Mentoring Table 2. Contact. Diffe

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    E-Mentoring Table 10. Ethical impli

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    E-Mentoring Table 15. Technology st

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    E-Mentoring Table 21. Coaching. Bes

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    E-Mentoring Table 27. Moment. Best

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    E-Mentoring Moreover, existing rese

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    E-Mentoring Kasprisin, C. A., Singl

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

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    Chapter V Training Teachers for E-L

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    Training Teachers for E-Learning FL

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    Training Teachers for E-Learning ne

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    Training Teachers for E-Learning A

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    Training Teachers for E-Learning yo

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    Training Teachers for E-Learning Di

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    Training Teachers for E-Learning ht

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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  • Page 156 and 157: Integrating Technology and Research
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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Swarm-Based Techniques in E-Learnin

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    Chapter XII E-Learning 2.0: The Lea

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    E-Learning 2.0 Table 1. Different s

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    E-Learning 2.0 Figure 1. Difference

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    E-Learning 2.0 where the blog is al

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    E-Learning 2.0 process. Along this

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    E-Learning 2.0 forth, and, of cours

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    E-Learning 2.0 Finally, it is impor

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    E-Learning 2.0 never be a hotchpotc

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    E-Learning 2.0 McPherson, K. (2006)

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

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Telematic Environments and Competit

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    Open Source LMS Customization Intro

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    Open Source LMS Customization or ev

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    Open Source LMS Customization compa

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    Open Source LMS Customization Figur

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    Open Source LMS Customization Figur

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    Open Source LMS Customization Figur

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    Open Source LMS Customization Haina

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    Evaluation and Effective Learning p

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    Evaluation and Effective Learning r

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    Evaluation and Effective Learning t

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    Evaluation and Effective Learning p

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    Evaluation and Effective Learning m

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    Evaluation and Effective Learning c

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    Evaluation and Effective Learning H

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    Chapter XVI Formative Online Assess

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    0 Chapter XVII Designing an Online

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Quality Assessment of E-Facilitator

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    Chapter XIX E-QUAL: A Proposal to M

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    E-QUAL is proposed to evaluate the

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    E-QUAL provide competent, service-o

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    E-QUAL 2004; Scalan, 2003) and qual

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    E-QUAL benchmarks address technolog

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    E-QUAL E-learning added two differe

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    E-QUAL Table 6. Application of the

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    E-QUAL Future trends The future of

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

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    E-QUAL SMEs: An analysis of e-learn

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

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    Compilation of References Argyris,

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    Compilation of References Biggs, J.

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    Compilation of References Cabero, J

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    Compilation of References Comezaña

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    Compilation of References Downes, S

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    Compilation of References Fandos, M

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    Compilation of References national

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    Compilation of References Hudson, B

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    Compilation of References Harbour.

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    Compilation of References Little, J

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    Compilation of References Metros, S

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    Compilation of References ONeill, K

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    Compilation of References Preece, J

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    Compilation of References Sadler, D

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    Compilation of References Shin, N.,

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    Compilation of References tional Co

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    Compilation of References Vermetten

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    Compilation of References Yu, F. Y.

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    About the Contributors Juan Pablo d

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    About the Contributors part: “An

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    About the Contributors María D. R-

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    About the Contributors Applications

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    Index e-learning tools, automated p

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    Socrates 55 Sophists 55 student-foc

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