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Advances in E-learning-Experiences and Methodologies

AI Techniques for

AI Techniques for Monitoring Student Learning Process techniques used in the area of scheduling systems come from the operational research (OR) area (i.e., branch and bound, simulated annealing, lagrangian relaxation). Lately, constraint programming (CP) has been applied to the different scheduling problems with very good results, that is, job-shop scheduling and the RCPSPmax problem (Kolisch & Hartmann, 1999). A RCPSPmax consists of a set of activities where two kinds of constraints can be interrelated: • Precedence constraints that impose the restriction that an activity cannot start before its predecessor activities, and • Resource constraints among activities that consume the same resource due to the limited capacity of the resource itself. The objective is to find precedence and resource assignments for all the activities in the horizon imposed. Figure 2 shows a simple example of a Job-Shop Scheduling problem with two resources: resource R1 with a capacity of 2, and resource R2 with a capacity of 3. The left part of the figure shows the precedence constraints among activities and the resources that each one requires. The right part of the figure shows the solution to the problem. Since R1 has a maximum capacity of 2, the 3 activities that consume this resource cannot be performed in parallel. Then, the scheduler will add a precedence constraint to one of them. Resource R2 has a capacity of 3 but none of the activities that require this resource have to be executed in parallel, so there are no conflicts. If we impose a deadline of 5 time units to the original problem (we consider that each activity has a duration equal to 1), the solution given by the scheduler will be also time consistent. However, a value lower than 5 will make the solution inconsistent. Then, scheduling techniques can be easily generalized and applied to a learning environment. In this case, instead of having machines and jobs (Job-Shop Scheduling problem), we have students, educators, and learning units (LU) in courses. Each learning unit (operation) needs to be processed during a period of time for a given student (machine), and the unit will be supervised by an educator. The course will also have a limited duration (deadline). Each instructor will have a maximum number of students (we consider an instructor as a resource with a total resource capacity given by the number of students the instructor is able to advise). We need to know the initial and end time of each LU considering precedence constraints among them. The variable values are imposed by the problem conditions: learning activity durations, course duration, number of learners, and so forth. AI integrated planner and scheduler systems generate a plan or a set of plans if a solution exists for the given deadline. A plan can be seen as a sequence of operator applications (learning activities) with a specific duration that can lead from the initial state to a state in which the goals are reached with the resources available. Figure 2. A job-shop scheduling example with two resources Resource capacity R R R R R R R R R R R R R R R R

AI Techniques for Monitoring Student Learning Process In a specific learning design, we need to impose a deadline, that is, the total duration of the course, and the resources that are available, that is, the number of educators. Then, it is up to the educator and the pedagogical responsible to study the best way to distribute the number of hours and their contents among the different units in order to assure the quality of the education process. This task can be done automatically by applying Planning and Scheduling techniques to a new domain: e- learning environments. Although the process will be explained in detail in the following sections, the basic idea is to change some parameters and to use the feedback from the students that have already followed the course. tAngow: A tooL For vIrtuAL educAtIon TANGOW facilitates the development and deployment of adaptive courses. In these courses the contents, the navigational options, and the flexibility of the guidance process are adapted to both the user features and their actions while interacting with the course. Adaptivity is an important feature because the lifelong learning philosophy is growing in importance in many environments and the same virtual education course can be accessed by students with different backgrounds, age, and interests. Teachers can describe adaptive courses by means of tasks and rules. Tasks are the basic units in the learning process. They include topics to be learned, exercises to be done, examples to be observed, and so forth, that is, tasks to be performed in order to learn or put into practice the concepts or procedures involved within the course. Rules specify the way of organizing tasks in the course along with information about the task execution (order among tasks—if any—free task selection, prerequisites among tasks, etc.). Figure 3 presents an example of the (partial) structure of a TANGOW course on operative systems, as taught to second year students of a computer science degree. The current version of the course is composed by a number of tasks representing theory units and also examples. The example shows how tasks are decomposed into subtasks according to specific rules. The complete course consists of four tasks: operative system overview, operative system concepts, distributed systems and security. These tasks are combined through an AND rule, which states that all the subtasks must be completed exactly in the order in which they are listed in the rule. The operative system overview task is divided into services, security, and architecture subtasks, combined through an OR rule. The OR rule dictates that, in order to complete the composed task, at least one of the subtasks must be completed, in any chronological sequence. Differently, the operative system concepts task is decomposed, through an ANY rule, into the process, memory, scheduling, input-output, and file subtasks. An ANY rule means that all the subtasks must be completed but the order is not relevant. Following with the course, it can be seen that, in order to execute the Scheduling task, the student has to sequentially execute the scheduling principles and scheduling algorithms tasks. Scheduling algorithms consists of the study of five specific algorithms in any order. Finally, the task round-robin algorithm can be learned by reading either the description or any of the three examples provided. Besides the correct sequencing, the rule may state conditions that must be fulfilled in order for the rule to be applied. These conditions are expressed by means of attribute values regarding user features, such as personal characteristics (age, language, experience, etc.), learning style (visual/verbal, intuitive/deductive, etc.) (Paredes & Rodriguez, 2002), preferences (type of information desired, learning strategy, etc.), and actions while interacting with the course (tasks visited, exercises performed, results obtained in the tests, time spent in every task, and so on). The latter type of attributes are called “dynamic attributes”

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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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  • 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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    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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