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Computer-Driven Instructional Design with INTUITEL: An Intelligent Tutoring Interface for Technology-Enhanced Learning [Kietas viršelis]

Edited by (University of Applied Sciences Karlsruhe, Germany), Edited by (University of Applied Sciences Karlsruhe, Germany)
INTUITEL is a research project that was co-financed by the European Commission with the aim of advancing state-of-the-art e-learning systems via the addition of guidance and feedback for learners. Through a combination of pedagogical knowledge, measured learning progress, and a broad range of environmental and background data, INTUITEL systems will provide guidance towards an optimal learning pathway. This allows INTUITEL-enabled learning management systems to offer learners automated, personalized learning support so far only provided by human tutors.

INTUITEL is— in the first place— a design pattern for the creation of adaptive e-learning systems. It focuses on the reusability of existing learning material and especially the annotation with semantic meta data. INTUITEL introduces a novel approach that describes learning material as well as didactic and pedagogical meta knowledge by the use of ontologies. Learning recommendations are inferred from these ontologies during runtime. This way INTUITEL solves a common problem in the field of adaptive systems— it is not restricted to a certain field. Any content from any domain can be annotated. The INTUITEL research team also developed a prototype system. Both the theoretical foundations and how to implement your own INTUITEL system are discussed in this book.
Preface xi
List of Contributors
xiii
List of Figures
xv
List of Tables
xix
List of Abbreviations
xxi
1 Introduction
1(2)
Kevin Fuchs
2 Intelligent Tutoring Systems: Preliminary Thoughts
3(20)
2.1 Organized Teaching and Learning Processes
3(10)
Christian Swertz
Alexander Schmoelz
Alessandro Barberi
Alexandra Forstner
2.1.1 The Open Future of Human Beings
5(4)
2.1.2 Learning to Determine Oneself
9(1)
2.1.3 Education as a Non-Deterministic Process
10(3)
2.2 Computer Technology as a Medium in Teaching and Learning
13(2)
Christian Swertz
Alexander Schmoelz
Alessandro Barberi
Alexandra Forstner
2.3 The History of Adaptive Assistant Systems for Teaching and Learning
15(4)
Christian Swertz
Alexander Schmoelz
Alessandro Barberi
Alexandra Forstner
2.4 Conclusions
19(4)
3 The INTUITEL Approach: Foundations and Design
23(56)
3.1 Pedagogical Ontology and Reasoning
23(7)
Christian Swertz
Alexander Schmoelz
Alessandro Barberi
Alexandra Forstner
3.1.1 Learning Objects
25(2)
3.1.2 Vocabulary of Knowledge Types
27(1)
3.1.2.1 Receptive knowledge types
27(1)
3.1.2.2 Interactive knowledge types
28(1)
3.1.2.3 Cooperative knowledge types
28(1)
3.1.3 Media Type Vocabulary
28(1)
3.1.3.1 Communication
28(1)
3.1.3.2 Interaction
28(1)
3.1.4 Learning Pathways
28(2)
3.2 Learning Analytics by Didactic Factors
30(7)
Christian Swertz
Alexander Schmoelz
Alessandro Barberi
Alexandra Forstner
Alexander Streicher
Florian Heberle
3.3 Learning Progress and Learning Pathways
37(18)
Alexander Streicher
Florian Heberle
3.3.1 INTUITEL Recommendation Process
38(1)
3.3.2 Comparison to Real-Life Tutoring
38(1)
3.3.3 Reflex Reactions
39(1)
3.3.4 LPM Input
40(1)
3.3.5 Learner Input
40(1)
3.3.6 Pedagogical Input
41(1)
3.3.7 Domain Input
41(1)
3.3.8 Set-based Rating of Learning Objects
42(4)
3.3.9 Learning Progress and Learner Position
46(1)
3.3.10 Determination of the Next KO
46(3)
3.3.11 Cognitive Content Space
49(1)
3.3.12 Multidimensional Cognitive Space
49(2)
3.3.13 Example for Learner Positions
51(3)
3.3.14 Learner-State Ontology: The Output of the LPM
54(1)
3.4 Software Architecture
55(7)
Florian Heberle
Peter A. Henning
Kevin Fuchs
3.4.1 LMS Integration
55(1)
3.4.1.1 Learner update
56(1)
3.4.1.2 User Score Extraction (USE)
56(1)
3.4.1.3 Learning Object Recommender (LORE)
57(1)
3.4.1.4 Tutorial Guidance (TUG)
57(1)
3.4.2 SLOM Repository
58(1)
3.4.3 INTUITEL Backend
59(1)
3.4.3.1 LPM
59(1)
3.4.3.2 Query builder
60(1)
3.4.3.3 Reasoning engine
60(1)
3.4.3.4 Recommendation rewriter
61(1)
3.4.4 Data Exchange
61(1)
3.5 Data Model
62(14)
Peter A. Henning
Florian Heberle
3.5.1 Push Update
66(1)
3.5.2 Push Update with Learner Polling
66(1)
3.5.3 Learning Objects Mapping and Inventory
67(2)
3.5.4 Authentication
69(1)
3.5.5 Tutorial Guidance -- TUG
70(1)
3.5.6 Immediate Response from the LMS
71(1)
3.5.7 Delayed Response from the Learner
71(1)
3.5.8 Learning Object Recommender -- LORE
72(1)
3.5.9 User Score Extraction -- USE
73(1)
3.5.9.1 Primary use case: Performance data request
73(1)
3.5.9.2 Secondary use case: Environmental data request
74(2)
3.6 The Semantic Learning Object Model SLOM
76(3)
Stefan Zander
Florian Heberle
4 Prototype Implementation
79(56)
4.1 Back End
79(2)
Kevin Fuchs
4.2 LMS Plugins
81(1)
4.2.1 Moodle
82(1)
Elena Verdu
Maria J. Verdu
Luisa M. Regueras
Juan P. de Castro
4.2.1.1 Implementation and architecture
82(4)
4.2.1.2 How to configure INTUITEL in a Moodle site
86(1)
4.2.1.3 How to enable INTUITEL in a Moodle course
86(1)
4.2.2 IMC Learning Suite
87(1)
Uta Schwertel
Sven Steudter
4.2.2.1 Course Creation in IMC learning suite
88(2)
4.2.2.2 Accessing courses through IMC learning portal
90(3)
4.2.2.3 Summary
93(1)
4.2.3 Ilias
94(1)
Kevin Fuchs
Peter A. Henning
4.2.3.1 Plugin implementation
95(2)
4.2.3.2 Learner tracking
97(1)
4.2.3.3 Architecture
98(1)
4.2.3.4 Maintainability
99(1)
4.2.4 eXact learning LCMS
99(1)
Elisabetta Parodi
4.2.4.1 About eXact learning LCMS
100(1)
4.2.4.2 Extension
101(1)
4.2.4.3 Demonstrator
102(2)
4.3 Compatibility to Existing Learning Formats
104(6)
Luis de-la-Fuente-Valentin
Daniel Burgos
4.3.1 The Transformation Approach
105(3)
4.3.2 Implemented Transformations
108(1)
4.3.2.1 SCORM
108(1)
4.3.2.2 IMS Learning Design
108(1)
4.3.2.3 Semantic MediaWiki
109(1)
4.3.2.4 Integration in a common format: INTUITEL merger
109(1)
4.3.2.5 Architectural principles
109(1)
4.3.2.6 Merger use cases: Standalone or integrated
110(1)
4.4 Sample Courses
110(18)
4.4.1 Advanced Java Concepts
111(1)
4.4.1.1 Course design
111(1)
4.4.1.2 Example run through the course
112(4)
4.4.2 Network Design
116(4)
Elena Verdu
Maria J. Verdu
Luisa M. Regueras
Juan P. de Castro
4.4.3 Special Relativity
120(8)
Peter A. Henning
4.5 Evaluation and Testing
128(7)
Luis de-la-Fuente-Valentin
Daniel Burgos
5 Conclusion and Outlook
135(8)
Kevin Fuchs
5.1 Summarizing INTUITEL
135(1)
5.2 Operationalization of Didactic Factors
136(2)
5.3 The Hypercube Database Project
138(5)
5.3.1 The Advanced Hypercube Model
138(1)
5.3.2 Example Applications
139(1)
5.3.3 Implementation of the Hypercube Database
139(4)
5.4 Conclusion
143(1)
References 143(8)
Index 151(8)
About the Editors 159
Kevin Fuchs, Peter A. Henning