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# Advances in E-learning-Experiences and Methodologies

## AI Techniques for

AI Techniques for Monitoring Student Learning Process task decomposition, their priorities and estimated duration time. 6. For each kind of student (end user, application programmer, OS designer) a plan is generated with a possible scheduled course. The following subsections describe the previously listed processes in detail by using the TANGOW course example of Figure 3. From tAngow metadata to P/s-Based Problems As mentioned, the planner domain theory contains all the actions represented by operators. The language for describing an IPSS domain theory is based on an augmentation of the representation originally proposed by Fikes and Nilsson (Fikes & Nilsson, 1971). Since this representation is quite restrictive, it has been extended to allow disjunctive preconditions, conditional effects and universally-quantified preconditions and effects, quality metrics, durations, time and resource constraints, and continuous values. The first step for defining a domain consists of identifying the operators and the object types that are needed in the domain (for declaring the type of each operator variable). Types can be defined as structured in a hierarchy. A special type, the infinite type, allows representing continuous valued variables, while finite standard types represent nominal types. In our domain, we have, among others, the following types: STUDENT, ROLE, SUBTASK, COURSE, DURATION, PRIOR- ITY, and TIME. Variables of type STUDENT instantiate to the possible student stereotypes. The variable ROLE represents the relevant user features (end user, application programmer, OS designer) and COURSE represents the courses that we want to track. Finally, DURATION, PRIOR- ITY, and TIME are infinite types that allow us to handle numerical values needed to calculate the duration and priorities of each task. By following the TANGOW example described in Section 4 (Figures 3, 4, and 5 and Table 1), we can obtain the needed metadata for IPSS (see Table 2). The IPSS operator in Figure 8 is composed of the following fields: Table 2. TANGOW metadata subtasks task name conditions Task Priority Duration (min,max) sequencing … … … … ‘Operative System Overview’ role = ‘End user’ ‘Services’ ‘Security Overview’ , , AND ‘Architecture Overview’ , ‘Operative System Overview’ role = ‘Application programmer’ ‘Services’ ‘Security Overview’ , , ANY ‘Operative System Overview’ role = ‘OS Designer’ ‘Services’ ‘Security Overview’ , , OR ‘Architecture’ , … … … …

AI Techniques for Monitoring Student Learning Process • Params field: Contains a list of the variables whose values will be printed out through the user interface when a solution is found. • Preconds field: Ccontains the preconditions of the operator. • Effects field: contains the add and delete effects of the operator. • Constraints field: Contains the temporal constraints. The symbols within < > are variables that are instantiated during the problem solving process. The operator in Figure 8 has two preconditions: (compose-subtask End_User ) and (student-role ) and three add effects (done ), (done ) and (done ). This operator, called “T_EU_Operating- System_Overview,” corresponds to the “Operating System Overview” task under the “End User role” condition in Table 2 which is composed of “Services,” “Security Overview,” and “Architecture Overview” substasks. These subtasks are represented within the operator as the variables , and . The variable will be instantiated by the End_user value. The variables , , and represent priorities and can take numbers as values. We need to use the gen-from-pred IPSS function to constraint the values that these three variables can take. This function generates a list of values as the bindings for a variable by using the information on the current state. In this example, in the case of , gen-from-pred returns the list of values {x} greater or equal to 1 such that the literal (prioritySe End_user Services x) is true in the current state. The variables , , and represent task duration. As the values they can take are lists of two elements corresponding to the minimum and the maximum duration, we need Figure 8. An IPSS operator corresponding to the “operating system overview” task

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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 2. An introduction to learnin

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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 The third interface is at the

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

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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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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 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 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 María D. R-

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Index e-learning tools, automated p

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Socrates 55 Sophists 55 student-foc

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