Reusability and Adaptability of Interactive Resources in Web-Based Educational Systems. 01/06/2003

Transcription

1 Reusability and Adaptability of Interactive Resources in Web-Based Educational Systems 01/06/2003

2 Reference A. El Saddik et al., Reusability and Adaptability of Interactive Resources In Web-Based Educational Systems, ACM Journal of Educational Resources in Computing, Vol. 1, No. 1, Spring Draft Standard for Learning Object Metadata (v6.3a), IEEE Learning Technology Standardization Committee (LTSC), January The SCORM Content Aggregation Model (V1.2), Advanced Distributed Learning (ADL) Initiative, October

3 Outline Why do we need Learning Objects Standard of Learning Objects-IEEE LOM Smart Learning Objects and Dynamic Metadata Conclusion

4 Why do we need Learning Objects?

5 Motivation of learning objects (1/2) New computer communication and presentation technologies help to increase the quality of educational documents. To help students learn difficult concepts, interactive learning software needs specific capabilities for simulation, visualization, and read-time data collection, as well as tools for analyzing, modeling, and annotating data. Such interactive, dynamic representations are the core content of educational learning modules.

6 Motivation of learning objects (2/2) Interactive, dynamic representations have to be flexibly combined in many kinds of contents: diverse classroom presentations, tutorials, experimental notebooks, and standardized assessments. Standard learning objects are proposed to archive this goal.

7 What is a learning object (1/2) IEEE s Learning Objects Metadata (LOM) defines: A learning object is defined as any entity, digital or non-digital, which can be used, re-used, or referenced during technology-supported learning. Examples: multimedia content, instructional content, instructional software, and software tools referenced during technology-supported learning. In a wider sense, LO could include learning objectives, persons, organizations, or events.

8 What is a learning object (2/2) IEEE s learning object model is characterized by the belief that independent chunks of educational content can be crated to provide an educational experience. These chunks of educational content may be of any type, interactive (e.g., simulation) or passive (e.g., simple animation), and they may be in any format or media type.

9 Tagging and metadata of LO To use LO in an intelligent fashion, LO must be labeled as what they contain, what they communication, and what requirements with regard to their use exist. Hence, a reliable and valid scheme for tagging learning objects is necessary. The LO model provides a framework for the exchange of learning objects.

10 LOM Standards IEEE LTSC. IMS Global Learning Consortium. Alliance of Remote Instructional Authoring and Distribution Networks for Europe (ARIADNE). Dublin Core Metadata Initiative. ADL (SCORM).

11 IEEE LTSC scope Architecture and Reference Model Computer Managed Instruction (CMI) Learning Objects Metadata (LOM) Platform and Media Profiles Competency Definitions Digital Rights Expression Language

12 IMS scope Meta-data Enterprise Content Packaging Digital Repositories Interoperability Question and Test Interoperability Learner Information Packaging Learning Design Reusable Definition of Competency or Educational Objective Simple Sequencing Implementation Handbooks Accessibility

13 SCORM scope BOOK 1: The SCORM Overview SCORM BOOK 2: The SCORM Content Aggregation Model BOOK 3: The SCORM Run Time Environment Meta-data Dictionary (from IEEE) Content Packaging (from IMS) Content Structure (derived from AICC) Data Model (from AICC) Launch, Communication API (from AICC) (Meta-data XML Binding and Best Practice (from IMS)

14 IEEE LO metadata structure (1/2) Data elements describe a learning object and are grouped into categories. The LOMv1.0 Base Schema consists of nine such categories: 1. The General category groups the general information that describes the learning object as a whole. 2. The Lifecycle category groups the features related to the history and current state of this learning object and those who have affected this learning object during its evolution. 3. The Meta-Metadata category groups information about the metadata instance itself (rather than the learning object that the metadata instance describes).

15 IEEE LO metadata structure (2/2) 4. The Technical category groups the technical requirements and technical characteristics of the learning object. 5. The Educational category groups the educational and pedagogic characteristics of the learning object. 6. The Rights category groups the intellectual property rights and conditions of use for the learning object. 7. The Relation category groups features that define the relationship between the learning object and other related learning objects. 8. The Annotation category provides comments on the educational use of the learning object and provides information on when and by whom the comments were created. 9. The Classification category describes this learning object in relation to a particular classification system.

16 SCORM metadata XML record Symbols: +: 1.. *? : * : 0.. *

17 Symbols used in SCORM

18 Data elements of metadata (1/2) Name : the name by which the data element is referenced. Explanation : the definition of the data element. Size : the number of values allowed Called multiplicity in SCORM: how many instances of the element are allowed within the immediate parent element. Order : whether the order of the values is significant (only applicable for data elements with list values).

19 Data elements of metadata (2/2) Value space : the set of allowed values for the data element- typically in the form of a vocabulary or a reference to another standard. Vocabulary Type in SCORM can be Restricted or Best Practice. Datatype : indicates whether the values are LangString, DateTime, Duration, Vocabulary, CharacterString or Undefined. Example : an illustrative example.

20 SCORM LOM general catalog 1 Symbols: +: 1.. *? : * : 0.. *

21 IEEE LOM General catalog (1/5) Nr: hierarchical number system Size: multiplicity

22 IEEE LOM General catalog (2/5)

23 IEEE LOM General catalog (3/5)

24 IEEE LOM General catalog (4/5)

25 IEEE LOM General catalog (5/5)

26 SCORM LOM lifecycle catalog 2 Symbols: +: 1.. *? : * : 0.. *

27 IEEE LOM Lifecycle catalog (1/2)

28 IEEE LOM Lifecycle catalog (2/2)

29 SCORM LOM metametadata catalog 3 Symbols: +: 1.. *? : * : 0.. *

30 IEEE LOM Meta-Metadata catalog (1/3)

31 IEEE LOM Meta-Metadata catalog (2/3)

32 IEEE LOM Meta-Metadata catalog (3/3)

33 SCORM LOM technical catalog 4 Symbols: +: 1.. *? : * : 0.. *

34 IEEE LOM Technical catalog (1/4)

35 IEEE LOM Technical catalog (2/4)

36 IEEE LOM Technical catalog (3/4)

37 IEEE LOM Technical catalog (4/4)

38 SCORM s educational catalog 5 Symbols: +: 1.. *? : * : 0.. *

39 IEEE LOM Educational catalog (1/9)

40 IEEE LOM Educational catalog (2/9)

41 IEEE LOM Educational catalog (3/9)

42 IEEE LOM Educational catalog (4/9)

43 IEEE LOM Educational catalog (5/9)

44 IEEE LOM Educational catalog (6/9)

45 IEEE LOM Educational catalog (7/9)

46 IEEE LOM Educational catalog (8/9)

47 IEEE LOM Educational catalog (9/9)

48 SCORM LOM rights catalog 6 Symbols: +: 1.. *? : * : 0.. *

49 IEEE LOM Rights catalog (1/2)

50 IEEE LOM Rights catalog (2/2)

51 SCORM LOM relation catalog 7 Symbols: +: 1.. *? : * : 0.. *

52 IEEE LOM Relation catalog (1/2)

53 IEEE LOM Relation catalog (2/2)

54 SCORM s annotation catalog 8 Symbols: +: 1.. *? : * : 0.. *

55 IEEE LOM Annotation catalog

56 SCORM s classification catalog 9 Symbols: +: 1.. *? : * : 0.. *

57 IEEE LOM Classification catalog (1/3)

58 IEEE LOM Classification catalog (2/3)

59 IEEE LOM Classification catalog (3/3)

60 This paper proposes (1/2) Developing and customizing dynamic multimedia objects using dynamic metadata. Smart learning objects (SLOs). The term customization refers to changes and/or modifications to a learning object.

61 This paper proposes (2/2) Current versions of learning metadata do not address specific issues of dynamic content such as interactivity or reusability. The authors examine the current learning metadata standards and to propose extensions of IEEE s Learning Objects Metadata (v4.0) in order to match the specific constraints of multimedia content.

62 The Multibook project

63 Multibook project It is a web-based adaptive hypermedia learning system for multimedia and communication technology. Currently under development at the Darmstad University of Technology These lessons are created using a knowledge base of multimedia elements, especially interactive animation, and are created automatically (course sequencing).

64 Multibook s knowledge base Multibook s knowledge base consists of two separate knowledge spaces: Concept Space : contains a networked model of learning topics and uses well-known approaches from KM. The knowledge topics are interconnected via semantic relations. MediaBrick Space : stores atomic information units in various multimedia formats. These units are interconnected via rhetorical relations. Each media brick is described using IEEE s LOM scheme. Media bricks are referred as learning objects (LO). Each LO can have a relation to one or more related topics.

65 MultiBook architecture 1 2

66 Generates adaptive lessons The separation of both spaces is the way in which Multibook generates adaptive lessons, because: a set of media bricks (texts in different granularities, animation, video, etc.) for each topic is available. Thus, the selection of media bricks is determined by the preferences of each user.

67 Multibook and IEEE In Multibook, the generation of lessons depends on the knowledge base stored in the Concept Space. It s similar to the standardization by IEEE. IEEE proposes a knowledge library responsible for sequencing a lesson while the actual compilation of the lesson is done by a delivery component.

68 IEEE-LTSC architecture (1/7) Concept Space LMS

69 IEEE-LTSC architecture (2/7) Content Repository

70 IEEE-LTSC architecture (3/7) Content Repository

71 IEEE-LTSC architecture (4/7)

72 IEEE-LTSC architecture (5/7)

73 IEEE-LTSC architecture (6/7)

74 IEEE-LTSC architecture (7/7)

75 IMS Content framework goals

76 IMS Content framework

77 Write a document in Multibook Scenarios: 1. An author acquires background knowledge. 2. An author creates an outline for a document. 3. An author fills the outline with content. These steps are modeled by different spaces in Multibook: The Concept Space contains keywords for creating outline of lessons. After sequencing the outline, the real content (text, images, audio, video, animation) is filled into the outline using elements of the MediaBrick Space.

78 The problem of traditional metadata Static resources (images or text) can be described properly, but dynamic resources (animation) is feasible only to a limited extent. The reason is dynamic multimedia objects can process input parameters, generate output parameters, and also work internally with data that cannot be described with traditional metadata schemes.

79 Interactive multimedia content and static metadata Learning systems enriched with multimedia elements can be divided into two categories: Learning objects are relatively simple, but are described by metadata in detail. A learning system operates on the metadata with intelligence. Learning objects are very smart in that they can change their behavior. A learning system has to pass on specific information, and each LO has to adhere to a specific stipulated set of input/output parameters.

80 Multimedia learning objects Can be characterized: Multicodality: Use of various symbol systems (images, pictographs, texts). Multimodality: LOs make use of discrete (text, images) or continuous media (video, animation). Dynamics: LOs realize to some extent the interaction between learner and learning system. Interactivity: LOs can address various senses: visual, aural, or both at the same time.

81 Characteristics of LOs (SLO)

82 Smart Learning Object (SLO) The authors denote interactive visualized multimedia elements as smart learning objects. The behavior of SLOs can be changed, as well as adapted, according to parameters passed by the system. A example of SLO is a simulation, which visualize complex procedures dynamically and interactively.

83 Problem and solution Problem in developing learning systems: The integration of user requirements that change over time. Learning systems must be flexible to adapt easily to new and changing user requirements. Solution-component software: Build applications by putting together high-level components. The system s functionality can then be changed or extended by substituting or plugging in new components.

84 Granularity of Smart Learning Objects

85 Coarse-grained SLOs In Multibook project, the authors use Java applets to develop coarse-grained SLOs to visualize algorithms and communication technology concepts. Problems of coarse-grained SLOs: Coarse-grained animation is useful in demonstrating the final concept, but hard to use in teaching individual ideas that are parts of the concept. Example: The discrete cosine transform and Huffman encoding are both used in JPEG and MPEG. A coarse-grained JPEG animation component cannot be reused to visualized a step in the MPEG-compression process.

86 Component-based development The authors develop a fine-grained approach to create applets composed of small, atomic units to solve the problem of coarse-grained SLOs. A component-based framework is developed to generate complex animations based on simple modules that visualize the different steps of an algorithm (Java technology)

87 Granularity The best granularity of the developed modules is strongly correlated to the domain being addressed, and varies widely among concepts. The authors goal for the modular model is to strive for the smallest possible scope for each concept.

88 How to use small-grained SLOs Several fine-grained applets can serve as stand-alone applets illustrating individual ideas. They can also be reused and combined with others to visualize a more complex topic. The authors tag all animations with the IEEE s Learning Objects Metadata.

89 Metadata for Dynamic Learning Objects

90 Multimedia LOM (1/2) Dublin Core, IMS, ARIADNE, and IEEE LOM are the most important initiatives dealing with metadata for computerized learning.

91 Multimedia LOM (2/2) All the methods describe static data: to summarize the meaning of the data (i.e., what the data is about); to allow users to search for data; to allow users to determine if the data is what they want; to prevent some users (e.g., children) from accessing data; to retrieve and use a copy of the data (i.e., where do I go to get the data); To instruct us on how to interpret the data (e.g., format, encoding, encryption). Problem: the data s granularity is defined by the original metadata author.

92 Dynamic metadata Dynamic metadata: the description used to adapt the content of an object and/or to change the behavior of a learning object. An example: the simulation of the CSMA/CD protocol (Ethernet). Collision of packets on the bus. Shortframe problem. The key idea behind dynamic metadata is that the same visualization, if it is configured by parameters, can be used to explain different problems.

93 Extension of IEEE s LOM Language DifficultyLevel InteractivityLevel Bidirectional Dimension Topic Scenario InputData OutputData Explanation

94 Proposed metadata fields for dynamic content (1/2)

95 Proposed metadata fields for dynamic content (2/2)

96 Application of Dynamic Metadata

97 Reusability, flexibility & interactivity Coding (JavaBeans). Designer of the visualization. Customize the LO to visualize a desired behavior appropriate for the course. End-user of the customized course.

98 SLOs tagging and customization process

99 xlom Editor

100 Content customizer

101 Conclusion Content packaging VS SLO How to apply component software technology to learning community? Need a runtime environment to communicate with LMS.

102 Q&A