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Advances in E-learning-Experiences and Methodologies

Swarm-Based Techniques

Swarm-Based Techniques in E-Learning when they where in the same state as she is now. The state is defined as the last activity or sequence of activities performed by the student. Information is presented as a ratio for each available activity in the menu: n students tried this activity when they were in the same state as you are now, m were successful. If there is no information about the success, or if that information makes no sense (e.g., if an activity consists on looking at a photograph for five minutes or reading a piece of text, there may be no concept of “success”), only the first half of the information is shown: n students tried this activity when they were in the same state as you are now. It may be the case that some activities are presented with a success ratio while others are not. Students then have the possibility of selecting an activity using the information they have about their peers. Students that are conscious of their relative competence will select those activities that seem to be harder, looking for bigger challenges or trying to avoid the longer and more boring sequences of learning material. Students that find the learning material more difficult will choose the longer but easier path to achieve the same learning goals. In effect, the information about the results of their peers places the students in a metacognitive state in which they do have to think about their relative performance compared with other students as much as their level of skill in a particular domain. This makes the system specially suited as a complement to classical teacher-classroom-student scenarios, in which the students interact frequently with each other and have a more accurate picture of their relative skill level compared to the rest of the group. The system is valid for distance learning as well, as the comparative position with respect to the group can be inferred as the learning process goes on. A student who finds that she is successful where many others have failed will probably look for harder challenges and vice versa. This approach discriminates between students by skill level in a sort of stigmergic development. There is no need to mark the student with any tag at the beginning of the process, nor is there any need for a complex student modelling guiding this classification (although this auto-organizative procedure is complementary to any student modelling performed by the intelligent tutoring system built on top of SIT). Students situate themselves in their appropriate level of skill following the traces left by others. It is important to note that this process has two virtues: it is automatic and auto-organizative, and it is flexible. Students that perform very well on the first activities of a module may not do so later, and the system can adapt to them at every moment. From a certain point of view, there can be as many levels as students, although this is dependant of the sequencing strategy of the tutoring system. In other words, the “skill levels” of the students are fractal: after each and every step, some of the activities present greater challenges than others and students have to choose at every moment which path they want to follow. It is obvious that the technique used by SIT bears many similarities to that of the learning networks, but there are several differences. The first difference is that SIT does not have any resources of its own, but relies on forwarding external resources as directed by the tutoring system. This flexible approach is a two-ended sword, as it means that information about success on the activities is not always available. If the auto-organizative feature of SIT is to be used, resources and activities have to be adapted so that they can send that information to the platform (this is performed using a tag on the HTTP request). Without success or failure information, SIT is only able to show the number of students that have performed one or another activity and most of the advantages of the stigmergic process are lost. Another difference is that the learner plays a central part in the adapting and stigmergic processes. The “pheromone” information is shown directly to the student, and it is the student who makes the decision, not the system. This is much 0

Swarm-Based Techniques in E-Learning more flexible than the former approach and allows for the formation of several skill levels. When only one activity is recommended, most students (both skillful and modest) will tend to follow the same path, leading to a Stalinist regime (Kauffman, 1996); even those that rebel to a path that may not be suited to them and chose any other, will be lost in the absence of any other guidance and their traces will not be significant. Presenting different options with different information in each case allows the users to follow different paths according to their capabilities. In all cases, the traces left behind are significant to other students that come afterwards. A third difference is the definition of state of the learner. The state of the student is the last activity or sequence of activities that has been completed by the student. The length of the sequence is a parameter to be set by the administrator of the system. Longer sequences mean more precise adaptation between the cognitive state of the peers (inferred from the information shown) and the cognitive state of the student. Shorter sequences mean that it is easier (i.e., takes less time) to get results that are meaningful to the student. This a common problem in most social system and is referred in the literature as the cold start problem. In the case of the learning networks, success is calculated only from the current activity node to the next one. SIT can calculate longer sequences of activities from the past history of the student in order to give more precise information. Related to the former two issues, there is the fact the SIT calculates the success information in relative terms. Observing the case where the state of the student is of length 1 (as in the learning networks), SIT has not only one transition matrix, but two: one stores the successful transitions from the current activity to all the others, while the second one stores total transitions. In the case of the learning networks, it is not important if 5 or 500 students have tried to complete activity node B after completing A. With the SIT approach, both numbers are important and it is their joint information that shows that transition is -probably- a very bad one. coLLABorAtIve FILterIng Over the last years, the growth of e-commerce has stimulated the use of collaborating filtering systems as recommender systems. The goal of a modern collaborative filtering system may be stated as predicting the utility of a certain item for a particular user based on the user’s previous likings and the opinions of other like-minded users. Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated. In a way, collaborative filtering systems are organisers of knowledge: the preferences of the users and their processing create the classification scheme. Modern collaborative filtering system can be classified into memory-based and model-based. The first ones employ a user-item database to generate a prediction. These systems use statistical techniques to find a set of users (neighbours) that have a similar profile of agreeing with the target user (Pennock, Horvitz, Lawrence, & Lee, 2000). Model-based collaborative filtering algorithms provide item recommendation by first developing a model of user ratings. Algorithms in this category take a probabilistic approach and envision the collaborative filtering process as computing the expected value of a user prediction, given the user’s ratings on other items. The model building process is performed by different techniques such as Bayesian networks (Miyahara & Pazzan, 2000), latent semantic analysis (Hofmann, 2003), and rule-based approaches (Boley, 2003). There have been some collaborative filtering systems explicitly designed for assistance in the learning process, like PHOAKS (www.phoaks. com) or LON-CAPA, but the most interesting for the scope of this chapter is CoFIND. Not only is it a collaborative filtering system and it 0

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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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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Chapter IX AI Techniques for Monito

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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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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