CN1647134A - E-learning authoring tool - Google Patents

Abstract

A course authoring system may include an author station to create a course, a content management system to store content and structure associated with the course, a learning management system to determine learning content to present to the learner based on a learning strategy applied to the content and the structure, and a communications link to transfer the course from the author station to the content management system. The author station may include an author tool configured to add structural elements to the course. The structural elements may include one or more of a course, a sub-course, a learning unit, or a knowledge item. The author tool is configured to associate the content with the structural elements. The course and its structural elements do not enforce a sequence of the content associated with the structural elements that a learner should use to traverse the course. Instead various learning strategies may be applied to a course to generate different sequences of the content associated with the structural elements.

Description

eLearning Writing Tools

technical field

The following descriptions relate to e-learning in general and, in particular, to tools for writing tutorials on e-learning.

Background technique

Systems and applications for delivering computer-based training (CBT) have existed for many years. However, CBT systems have not historically gained widespread acceptance. One issue hindering the acceptance of CBT as a means of training workers and learners is compatibility between systems. As a stand-alone system, the CBT system cannot use content designed for other CBT systems.

Early CBT was also based on hypermedia systems that linked content statistically. Provide user guidance by annotating hyperlinks with descriptive information. Trainees can use the learning materials by traversing links embedded in the learning materials. The structure associated with the material is rigid, and the material cannot be easily written, edited, or reused to create additional or new learning material.

The update method for smart tutoring and CBT systems is based on a special domain model that has to be defined before creating tutorials or content. Once a course is created, the course material cannot easily be adapted or changed for the training needs of different learners. As a result, the courses often fail to meet the needs of the trainees and/or trainers.

The special domain model also has many complex rules that must be understood before designing a tutorial. As a result, creating a tutorial is difficult for most authors who have not had extensive training in the use of the system. Even authors who have received adequate training can find the system difficult to use and become discouraged from using it. Also, the resulting tutorial may not be understandable due to incorrect use of the domain model by the author who created the tutorial. Accordingly, for the above and other reasons, new methods and techniques are needed to supplement traditional computer-based training and teaching.

Contents of the invention

According to one general aspect, a tutorial writing system includes: an author station for creating tutorials; a content management system for storing content and structure associated with the tutorial; a learning management system for A learning strategy to determine learning content to present to learners; a communication link for transferring tutorials from the author site to the content management system.

The author station may include author tools configured to add structural elements to the tutorial. The structural elements may include one or more of tutorials, sub-tutorials, learning units or knowledge items. The author tool is configured to associate content with structural elements. The content and its structural elements do not enforce the order in which the learner should use the structural elements to traverse the tutorial.

The author tool is also configured to create metadata to be associated with the structural elements. The metadata includes attributes, including knowledge types and/or capabilities.

The author station is also configured to create relationships between structural elements. The relationship may be directional.

The content management system can include a content player, and the content player can be configured to apply learning policies to the tutorial to determine a path to navigate the content to present to the learner. The tutorial includes structural elements and relationships between structural elements that do not enforce the order in which the learner should use the structural elements to traverse the tutorial. The content player may be configured to apply a learning strategy to the tutorial to generate a sequence of structural elements suggested to the user.

The learning strategies applied to the tutorial may be macro strategies such as goal-based top-down strategies, goal-based bottom-up strategies, and table-of-contents strategies. The learning strategy may also be a micro-strategy, such as a location-only strategy, a behavior-oriented strategy, an explanation-oriented strategy, a location-oriented strategy, and a table of contents strategy.

The content management system may include a content repository configured to store tutorial content as well as structural elements and metadata associated with the structural elements. The content player is configured to access tutorial content, structural elements, and metadata associated with structural elements from a content repository, and apply policies to the accessed structural elements to suggest structural elements and structural elements to learners according to the applied policies. associated content.

The author station may include: an input terminal for selecting a learning strategy; an embedded learning management system for creating a navigation path according to the selected learning strategy. After reviewing the navigation path, the author can use the author station to edit the tutorial to achieve desired or suitable tutorial navigation in response to the selected learning strategy.

The tutorial writing system may also include a business management system and a communication link for transmitting tutorial information to the business management system. The course information may include one of title, author, description, prerequisites, competencies acquired, and competencies required.

The author station can be configured to preview or modify tutorials.

According to another general aspect, an author station may include: an author input device for entering commands; an author tool for receiving input commands and assembling structural elements in response to said commands, said author tool including an interface, Used for presenting structural elements, for selecting structural elements and for associating content with structural elements to form a tutorial. The author station may include a communication interface for transferring tutorials to a content management system. The structural elements include one or more of tutorials, sub-tutorials, learning units or knowledge items.

The author tool can create metadata to be associated with structural elements. The metadata includes attributes, including one of knowledge type or capability.

Author stations can be configured to create relationships between structural elements. The relationship may be directional.

The author station may include an embedded learning management system configured to apply a learning strategy to the tutorial to generate a suggested sequence of structural elements. The learning strategy may be a macro strategy, such as one of a goal-based top-down strategy, a goal-based bottom-up strategy, and a table-of-contents strategy. The learning strategy can also be a micro-strategy, such as one of a location-only strategy, a behavior-oriented strategy, an explanation-oriented strategy, a location-oriented strategy, and a table of contents strategy.

The author station may also include a communication interface for transmitting tutorial information to the management system. The course information may include one of title, author, description, prerequisites, competencies acquired, and competencies required.

The author tools may also include a tutorial editor. The tutorial editor may present a tutorial overview to display and select representations of structural elements of the tutorial. The tutorial editor can also present a workspace for editing and displaying structural elements of a tutorial. In addition, the tutorial editor can present a dialog to display the data fields corresponding to the structural elements shown in the working window. The tutorial editor can also associate attributes with structural elements. The attributes may include behavioral knowledge, orientation knowledge, reference knowledge, and example knowledge.

Other features and advantages will be apparent from the description, drawings, and claims.

Description of drawings

Figure 1 is an exemplary tutorial content collection model.

Figure 2 is an example of an ontology of knowledge types.

Figure 3 is an example of a tutorial diagram for e-learning.

Figure 4 is an example of a sub-tutorial diagram for e-learning.

Figure 5 is an example of a unit of study diagram for e-learning.

6 and 7 are exemplary block diagrams of electronic learning systems.

Fig. 8 is an exemplary tutorial editor interface that may be implemented using an authoring tool.

Figure 9 is an exemplary tutorial overview of the tutorial editor interface.

Figure 10 is a schematic dialog box of the tutorial editor interface.

Figure 11 is a schematic workspace of the tutorial editor.

Fig. 12 shows an example of v, v is used as a vertex to represent a learning unit LU, where v ₁ and v ₂ are vertices.

Like reference numerals in the various figures indicate similar elements.

Detailed ways

eLearning content structure

E-learning systems and methods structure content such that the content is reusable and flexible. For example, the content structure allows tutorial creators to reuse existing content to create new or additional tutorials. In addition, the content structure also provides flexible content presentation that can be adapted to the learning styles of different learners.

E-learning content can be aggregated using multiple structural elements arranged on different aggregation layers. Each structural element on a higher layer can refer to any instance of all structural elements on a lower layer. At its lowest level, structural elements refer to content and cannot be further divided. According to one implementation shown in FIG. 1 , the tutorial material 100 can be divided into four structural elements: tutorial 110 , sub-tutorial 120 , learning unit 130 and knowledge item 140 .

Starting from the lowest level, the knowledge item 140 is the basis for other structural elements and is the basic building block of the tutorial content structure. Each knowledge item 140 may include content that illustrates, explains, exercises or quizzes a subject area or aspect of a subject. Typically, knowledge items 140 are of small size (ie, short duration, eg, about 5 minutes or less).

A knowledge item 140 may be described using a number of attributes, such as name, media type, and knowledge type, among others. The learning system can use the name to identify and locate content related to the knowledge item 140 . A media type describes the form of content associated with the knowledge item 140 . For example, media types include presentation types, communication types, and interaction types. Presentation media types may include text, tables, diagrams, diagrams, images, animations, audio clips, and video clips. Communication media types can include chat sessions, groups (eg, newsgroups, teams, classes, and groups of peers), email, Short Message Service (SMS), and instant messaging. Interactive media types can include computer-based training, simulations, and quizzes.

Knowledge items 140 may also be described by attributes of knowledge types. For example, knowledge types include knowledge of direction, knowledge of behavior, knowledge of interpretation, knowledge of source/citation. Knowledge types can vary in terms of learning objectives and content. For example, orientation knowledge provides learners with points of reference and thus overall information that leads to a better understanding of the structure of interconnected structural elements. Each knowledge type is described in more detail below.

A variety of techniques can be used to generate knowledge items 140, however, browsers (including plug-in applications) should be able to interpret and display the file format associated with each knowledge item. For example, markup languages (such as Hypertext Markup Language (HTML), Standard Generalized Markup Language (SGML), Dynamic HTML (DHTML) or Extensible Markup Language (XML)), JavaScript (client-side scripting language) and/or Flash may be used To create a knowledge item 140.

HTML can be used to describe the logical elements of a document and the presentation of the document, for example, text, headings, paragraphs, lists, tables or image references.

Flash can be used as a file format for Flash movies and as a plug-in to play Flash files in browsers. For example, use vector and bitmap images, animations, slideshows, transitions, MP3 audio files, input forms, and interactive Flash movies. In addition, Flash allows for pixel-accurate positioning of diagram elements to create lively and interactive applications that present tutorial material to learners.

A unit of study 130 may be combined using one or more knowledge items 140 to represent, for example, different thematically coherent units. Therefore, the learning unit 130 can be regarded as a container for knowledge items 140 on the same topic. It can also be considered that the learning unit 130 has a relatively small size (ie, duration), but is larger than the knowledge item 140 .

Sub-tutorials 120 may be combined with other sub-tutorials 120 , learning units 130 and/or knowledge items 140 . Sub-tutorials 120 may be used to divide a large tutorial into several smaller sub-tutorials. Subtutorials 120 can be used to build nested structures of any depth by referencing other subtutorials 120 .

Tutorials can be combined by all subordinate structural elements including sub-tutorials 120 , learning units 130 and knowledge items 140 . To facilitate maximum reuse, all structural elements should be self-contained and context-free.

Structural elements may also be tagged with metadata used to support adaptive presentation, reusability, and search/retrieval of content associated with the structural element. For example, Learning Object Metadata (LOM) defined by the IEEE "Learning Object Metadata Working Group" can be attached to each course structure element. Metadata may be used to indicate capabilities associated with structural elements. Other metadata can include various types of knowledge (eg, location, behavior, interpretation, and resource) that can be used to classify structural elements.

As shown in FIG. 2 , structural elements can be classified using a teaching ontology 200 of knowledge type 201 , which includes: positioning knowledge 210 , behavioral knowledge 220 , interpretive knowledge 230 , and reference knowledge 240 . Orientation knowledge 210 helps learners find their way through a topic without being able to act in a way that is specific to a topic, and can be called "knowing what." Action (Action) knowledge 220 helps learners acquire subject-related techniques, which can be called "know how". Explanation (Explanation) knowledge 230 provides learners with an explanation of "why something is so", which can be called "knowing why", and reference (Reference) knowledge 240 teaches learners where to find information about a particular topic. additional information, which can be called "knowing where".

These four knowledge types (location, action, interpretation, and reference) can also be further divided into fine-grained ontologies, as shown in FIG. 2 . For example, location knowledge 210 may refer to subtypes 250 including history, plot, facts, overview, and summary. Behavioral knowledge 220 may refer to subtypes 260 including policies, procedures, rules, principles, orders, laws, notes to laws, and checklists. Interpretive knowledge 230 may refer to subtypes 270 that include examples, intentions, reflections, explanations of why or what, and arguments. Citation knowledge 240 may refer to subtypes 280, which include citations, document citations, and archive citations.

When combining structural elements on a pooling layer, dependencies between structural elements can be described by relationships. Relationships can be used to describe natural, thematic relationships between structural elements. Relationships can be directed or undirected. Directed relationships can be used to indicate that relationships between structural elements are true in one direction only. A directed relationship should be followed. Relationships can be divided into two categories: topical and non-topic.

The relationship classified by subject can be further divided into hierarchical relationship and association relationship. Hierarchical relationships can be used to represent relationships between structural elements that have a subordinate or superordinate relationship. For example, if knowledge item B is a part of knowledge item A, there is a hierarchical relationship between A and B. Hierarchical relationships can be divided into two categories: part/whole (ie "has part") and abstract relationships (ie "generalize" (gerneralize)). For example, a part/whole relationship "A owns part B" describes: B is a part of A. The abstract relation "A generalizes B" means: B is a concrete type of A (eg, airplane generalizes jet, or jet is a concrete type of airplane).

You can use the Associative relationship to indicate the relationship between two structural elements. Associative relationships can help learners gain a better understanding of facts related to structural elements. The association relationship describes multiple relationships between two structural elements, and is mainly directed (that is, the relationship between structural elements is only established in one direction). Examples of associative relationships include "determine", "side-by-side", "alternative to", "opposite to", "precedes", "context "(context of), "process of", "values", "means of" and "affinity".

A "deterministic" relationship describes a deterministic relationship between A and B (eg, B causally depends on A). The "parallel" relationship can be observed from the perspective of space, concept, theory or ontology (for example, if two knowledge objects A and B are part of a superordinate whole, then A and B are juxtaposed). For example, the parallel relationship can be further divided into "similar to", "alternative to" and "analogous to". A "relative to" relationship means that two structural elements are opposite, at least with respect to a quantity. A "before" relationship describes a sequential temporal relationship (e.g., A occurs before B in terms of time (without implying that A is a prerequisite for B). A "context" relationship is based on which of the related structural elements can are derived to describe actual and situational relationships. A "kinship" between two structural elements means that there is a close functional relationship between the structural elements (for example, a relationship between a book and the act of reading, Because reading is the main function of the book).

Non-subjective relationships can include the relationships "prerequisite" and "belongs to". The "prerequisite" and "belongs to" relationships do not refer to thematically related interconnections of the knowledge being taught. Instead, these relationships refer to the progression of the tutorial in the learning environment (eg, as the learner traverses the tutorial). A "prerequisite" relationship is a directed relationship, while a "belongs to" relationship is an undirected relationship. Both relationships can be used for knowledge items 140 that cannot be further divided. For example, if the size of the screen is too small to display the entire content on one page, the page displaying the content can be divided into two pages connected by the relationship "prerequisite".

Another type of metadata is competence. Capabilities may be assigned to structural elements, such as sub-tutorials 120 or learning units 130, among others. Competencies can be used to indicate and assess a learner's performance as they traverse the course material. Abilities can be categorized as: cognitive skills, emotional skills, sensorimotor skills, or social skills.

The content structure associated with the tutorial can be represented as a set of diagrams. Structural elements can be represented as nodes in the graph. Node attributes are used to convey metadata (such as name, knowledge type, capability and/or media type) attached to the corresponding structural element. A relationship between two structural elements can be represented as an edge. For example, Figure 3 shows a diagram 300 for a course. The tutorial is divided into 4 structural elements or nodes (310, 320, 330 and 340): 3 sub-tutorials (eg knowledge structure, learning environment and tools) and a learning unit (eg basic concepts). The node attributes 350 for each node are shown in parentheses (eg, a node labeled "Basic Concept" has an attribute identifying it as a reference to a unit of study). In addition, the unit of study has been assigned an edge 380 representing a "context" relationship with respect to each sub-tutorial. Therefore, the basic concepts explained in the study units provide the context for the concepts covered in the 3 sub-tutorials.

FIG. 4 shows a diagram 400 of the sub-tutorial "Knowledge Structure" 350 of FIG. 3 . In this example, the sub-tutorial "Knowledge Structure" is further divided into 3 nodes (410, 420, and 430): a learning unit (for example, about relations) and two sub-tutorials (for example, topics covering methods and knowledge objects ). An edge 440 denoting the relation "determined" has been provided between the structural elements (eg, the sub-tutorial "Method" defines the sub-tutorial "Knowledge Object" and the learning unit "Relationship"). Additionally, the properties 450 of each node are shown in parentheses (e.g., nodes "Methods" and "Knowledge Objects" have properties that identify them as references to other sub-tutorial nodes, nodes "Relationships" have properties that identify them as references to units of learning Attributes).

FIG. 5 shows a graph 500 for the learning unit "Relationship" 450 shown in FIG. 4 . This learning unit includes 6 nodes (510, 515, 520, 525, 530, 535, 540, and 545): 6 knowledge items (namely "association relationship (1)", "association relationship (2)", "pair relationship Tests of", "Hierarchical Relationships", "Non-Subject Relationships", and "Different Relationships"). An edge 547 representing the relationship "precondition" is provided between the knowledge items "association relationship (1)" and "association relationship (2)". Additionally, the attributes 550 of each node are indicated in parentheses (eg, node "Hierarchy" includes attributes "Example" and "Picture").

e-learning strategy

The collection and structure of tutorial-related content described above does not automatically enforce any order in which learners can traverse the tutorial-related content. Therefore, different collations can be applied to the same content structure to provide different paths through the tutorial. The ordering rules applied to the knowledge structure of the tutorial are learning strategies. Learning strategies can be used to pick specific structural elements to be suggested to the learner as the learner completes the tutorial. When accessing a tutorial, a learner or supervisor (eg, a tutor) can choose from a number of different learning strategies. In turn, the chosen learning strategy takes into account both content structure requirements and learner preferences.

In a traditional classroom, the teacher determines the learning strategies used to study the course material. For example, in this case, the learning progression could begin with a tutorial orientation, followed by explanation (using examples), behavior, and exercises. Using the e-learning system and method, a learner can choose between one or more learning strategies to determine which path to take to complete the course. As a result, learners may progress differently through the tutorial.

Learning policies can be created using macro-policies and micro-policies. When accessing the tutorial, the learner can choose from a number of different learning strategies. The learning strategy is chosen at runtime when the tutorial content is presented to learners (rather than during the design of the tutorial's knowledge structure). Thus, the author of the tutorial is relieved from the burden of determining the presentation sequence or sequence of the tutorial material. Instead, tutorial authors can concentrate on structuring and annotating the tutorial material. In addition, authors are not required to impose complex rules or Boolean expressions on the domain model, thereby minimizing the training necessary to use the system. Also, the tutorial material can be easily trimmed and reused for editing and creating new tutorials.

The use of macro-strategies in learning strategies is intended to be related to the coarse structure of the tutorial (ie the composition of sub-tutorials 120 and learning units 130). The macro policy determines the order in which the sub-tutorials 120 and learning units 130 of the tutorial are presented to the learner. Basic macro strategies include "inductive" and "deductive," which allow learners to work through the tutorial from the general to the specific, or from the specific to the general, respectively. Other examples of macro strategies include "goal-based top-down," "goal-based bottom-up," and "table of contents."

Goal-based top-down follows a deductive scheme. Traverse the hierarchy of the structure from top to bottom. Relationships within a structural element are ignored if the relationship does not indicate a hierarchical dependency. Goal-based bottom-up follows an inductive scheme, which is implemented by a depth-first traversal of the tutorial material. The table of contents simply ignores all relationships.

A micro-policy implemented by a learning policy, targeting the learning progress within a learning unit. Micropolicies determine the order in which knowledge items for a unit of learning are presented. A micropolicy reference describes the attributes of a knowledge item. Examples of micropolicies include orientation only, action oriented, explanation oriented, and table of contents.

Micropolicies "Only for location" ignore all knowledge items that are not classified as location knowledge. The "Targeting only" strategy is best suited for implementing overviews of tutorials. Microstrategy "behavior-oriented" first selects knowledge items classified as behavioral knowledge. Order all other knowledge items in their natural order (ie, in the order in which they appear in the knowledge structure of the unit of study). Microstrategies are "interpretation-oriented" similar to behavior-oriented, and focus on explaining knowledge. Position-oriented is similar to action-oriented and focuses on positional knowledge. The micropolicy "table of contents" operates similarly to the macropolicy table of contents (but at the unit of learning level).

In one implementation, there is no dependency between macro-policies and micro-policies. Therefore, any combination of macro and micro strategies can be used when taking the tutorial. The process of applying learning strategies to the knowledge structure is described in more detail below.

e-learning system

As shown in FIG. 6 , e-learning architecture 600 may include a learning station 610 and a learning system 620 . A learner may use a learning station 610 (eg, using a learning portal) to access tutorial materials. Learning station 610 may be implemented using a workstation, computer, portable computing device, or any intelligent device capable of executing instructions and connected to a network. Learning station 610 may include any number of devices and/or peripherals (eg, displays, memory/storage devices, input devices, interfaces, printers, communication cards, and speakers) that facilitate access and use of instructional materials.

Learning station 610 may execute any number of software applications, including applications configured to access, interpret, and present tutorials and related information to learners. This software can be implemented using a browser, such as Netscape Communicator, Microsoft's Internet Explorer, or any other software application that can interpret and process markup languages such as HTML, SGML, DHTML, or XML.

Browsers may also include software plug-in applications that allow the browser to interpret, process, and present different types of information. A browser may include any number of utilities, such as Java, ActiveX, JavaScript, and Flash.

A browser can be used to implement a learning portal that allows learners to access the learning system 620 . Link 621 between learning portal and learning system 620 can be configured to send and receive signals (eg, electrical, electromagnetic or optical signals). Alternatively, the link may be a wireless link that uses electromagnetic signals (eg, radio frequency, infrared, or microwave) to transfer information between the learning station and the learning system.

A learning system may include one or more servers. As shown in FIG. 6 , the learning system 620 includes a learning management system 623 , a content management system 625 , and an operation management system 627 . Each of these systems can be implemented using one or more servers, processors, or intelligent network devices.

As shown in Figures 6 and 7, servers, such as SAP R/3 4.6C+LSO external accessories, can be used to implement the management system. Business management system 627 may include a database of learner accounts and course information. For example, a learner account may include demographic data about the learner (such as name, age, gender, address, company, school, account number, and bill) and his/her progress in completing the course material (such as places visited , tests completed, skills acquired, knowledge acquired, and ability to use the material). The business management system 627 can also provide additional information about the courses, such as course name, description, courses offered, course author/instructor, and most popular courses.

Content management system 625 may include learning content server 730 . The learning content server can be implemented using a WebDAV server. A learning content server may include a content repository. The content repository may store tutorial files and media files used to present the tutorial to learners at the learning station 610 . A tutorial file may include the structural elements that make up the tutorial and may be stored as an XML file. Content contained in a tutorial can be stored using media files and combined for presentation to a learner at a learning system.

The learning management system 623 may include a content player 720 . The content player 720 can be implemented using a server, for example, SAP J2EE engine. The content player 720 is used to obtain tutorial materials from a content repository. The content player 720 also applies learning strategies to the acquired tutorial material to generate a navigation tree or path for the learner. The navigation tree or path is used to recommend to the learner a route to complete the tutorial material, and to generate the presentation of the tutorial material to the learner according to the learning strategy selected by the learner.

Learning management system 623 may also include an interface for exchanging information with business management system 627 . For example, when a learner completes the tutorial material, the content player 720 may update the learner account information to indicate, for example, competencies acquired, quizzes passed, tutorials completed.

Tutorial author site

As shown in FIGS. 6 and 7 , the electronic learning system may also include an author station 630 . Author station 630 may be implemented using a workstation, computer, portable computing device, or any intelligent device capable of executing instructions and connected to a network. Author station 630 may include any number of devices and/or peripherals (e.g., displays, memory/storage devices, input devices, interfaces, printers, communication cards) that facilitate access, presentation and creation of tutorials and their associated content. ,speaker).

Author station 630 may execute any number of software applications, including author tool 740, which is configured to create, access, interpret, and present tutorials (and associated tutorial data/information). Author tools 740 may include a tutorial editor 750 and a browser such as Netscape communicator, Microsoft Corporation's Internet explorer, or any browser that can interpret and process markup languages such as HTML, SGML, DHTML, XML, or XHTML. other software applications. The browser may also include software plug-in applications that allow the browser to interpret, process, create and present different types of information. The browser can include any number of utilities, such as Java, Active X, JavaScript, and Flash.

Tutorial author tool 740 may access content and associate the content with structural elements. Author tool 740 can also associate knowledge types, relationships, and metadata with structural elements. Author tool 740 may be used to create the structure of the tutorial, ie its structural elements and relationships. Author tool 740 may save structural elements as tutorial files and associated content as media files.

The author station 630 may also include an embedded learning management system 760 . Embedded learning management system 760 is an application similar to learning management system 623 that enables an author to preview a tutorial by applying a learning policy to a tutorial (e.g., it is Learner suggested navigation paths. According to different suggested navigation paths, the author can determine how to create the structure of the tutorial and how to interpret the created structure by the learning management system 623 . As a result, authors can edit, modify, or add structure to a tutorial before publishing it to the learning system 620 .

The author station 630 may also include a communication interface 631 . After creating a tutorial, the author station 630 can use the communication interface 631 to connect to the learning system 620 to publish the tutorial so that learners can subscribe and obtain the tutorial. Specifically, the communication interface of author station 630 may connect to content management system 625 using communication link 635 . To publish the tutorial on the learning system 620 , the author station 630 transmits the tutorial structure and content (eg, tutorial files and media files) to the content management system 625 . As mentioned above, the tutorial files may be formatted in a markup language such as XML. Communication link 635 may be implemented using any permanent or temporary communication link configured to transmit tutorial files and associated media files (eg, a communication medium configured to transmit data signals like electrical, electromagnetic, or light waves). Content management system 625 stores tutorial files and associated media files in a content repository for access by content player 720 .

Communication interface 631 of author station 630 may also connect to business management system 627 using communication link 637 . The communication links may be implemented by any communication medium that can be configured to send and receive signals, such as electrical, electromagnetic or light waves. Author station 630 provides course information (eg, title, author, description, credits, prerequisites, and acquired/needed competencies) to business management system 627 for use by learners to, for example, book a course. Once a course is published, the business management system 627 enables the learning station 610 to obtain course information using the learning portal.

tutorial editor

Author tools 740 and author station 630 may include a tutorial editor 750, which may be used in conjunction with a browser to create, modify, build, combine, and preview tutorial structures and their associated content. Tutorial editor 750 may be used to structure content for use in tutorials. Tutorial editor 750 includes a tutorial editor interface.

Tutorial editor 750 may be used to create the structure of the tutorial content. The structure can be stored as metadata. The metadata can be interpreted by the content player 720 of the learning management system 623 to present the tutorials to learners in accordance with the learning strategy selected at runtime. Specifically, the tutorial editor 750 enables authors to classify and describe structural elements, assign attributes to structural elements, assign relationships between structural elements, and build topic-category tutorial structures. Tutorial editor 750 primarily generates the structure of the tutorial rather than the structure of the content (although the structure of the content could also be provided).

As shown in FIG. 8 , the tutorial editor interface 800 may include a menu bar 810 , a button bar 820 , a tutorial overview 830 , a dialog box 850 and a workspace 860 . Menu bar 810 may include various pull-down menus, such as File, Edit, Tools, Options, and Help. The drop-down menu can include functions such as creating a new tutorial, opening an existing tutorial, editing a tutorial, or saving a tutorial. The button bar 820 may include a plurality of buttons. The buttons may be shortcuts to functions in frequently used drop-down menus and available tools and functions for the tutorial editor 750 . The remainder of the tutorial editor interface 800 can be divided into three main sections or windows: tutorial overview 830 , dialog 850 and workspace 860 . Each section may be provided with horizontal or vertical scroll bars or other means to resize the window to fit different monitors, while providing access to elements that may not appear in the window.

As shown in FIG. 9, a tutorial overview 830 may be used to select and view components within a tutorial. The author can select various components within the tutorial overview 830 to turn the components on and off, such as structure elements. The components in the tutorial overview 830 may be arranged in a probe format. Tutorial overview 830 may include a directory 920 of components containing files and folders. The files and folders can be expanded to view their contents. However, unlike a detector, the tutorial overview 830 distinguishes between structural elements 930 and relations 940 (eg, it may include learning content). The sub-tutorials 120, learning units 130 and knowledge items 140 may be displayed in the tutorial overview 830 using icons. To access the shown sub-tutorial 120 or learning unit 130 in the overview, the author can right-click and select the open sub-tutorial 120 or learning unit 130 . A knowledge item 140 can be opened by double-clicking the associated icon.

As shown in Figure 10, a dialog box 850 may be used to interact with and edit the tutorial components. For example, dialog box 850 may be arranged using tabs (eg, General, Notes, Keywords, and Capabilities) that may be used to describe structural elements. Each tab can be used to edit structural elements. Dialog box 850 shown in FIG. 10 includes tabs General 1010 , Notes 1020 and Keywords 1030 . Dialog 850 shown in FIG. 10 also includes fields Subject 1040 , Content 1050 , Learning Time 1060 , LOM File 1070 and Thumbnail 1080 . Authors can use dialog 850 to add content (eg, HTML pages) and attributes (eg, name, knowledge type, media type, LOM, and capabilities) to structural elements. Dialog box 850 is automatically configured to correspond to any structural elements that have been selected and created within workspace 860 .

The General tab 1010 allows the author to determine general information and/or attributes associated with the selected structural element. The name of the structural element may be provided in a name field (not shown). Thumbnails 1080 may be used to provide the author with an impression of the content associated with the structural element. Topic 1040 may be included to describe the subject, attribute, or knowledge type of the structural element. Learning time 1060 may be used to indicate the average amount of time required for a learner to complete content associated with a structural element. A LOM file 1060 may be included to add comprehensive metadata to structural elements.

Capabilities obtained by completing or viewing structural elements may be categorized using capability tags (not shown). Examples of abilities include cognitive, affective, sensorimotor, and social. Capabilities that are required and recommended to use the associated structural elements may also be included.

Annotations tab 1020 may be used to insert comments about the content associated with the structural element. For example, comments or descriptions of the content associated with the structural element can be inserted.

Keywords tab 1030 may be used to enter keywords for searching and/or organizing structure elements. Keywords can also be used to classify structural elements.

As shown in Figure 11, workspace 860 may be used to create the structure of the tutorial. Workspace 860 displays structural elements and relationships between structural elements. Structural elements selected in the overview may be displayed in work area 860 . Similarly, new structural elements and any associated relationships may be created in workspace 860 .

Structural elements may be represented in workspace 860 as rectangles. The rectangles may be color-coded to indicate the type of structural element (eg, sub-tutorial 120, learning unit 130, knowledge item 140) and whether the structural element is selected or active. Alternatively, the structural element may be represented in the work area by a thumbnail that indicates content associated with the structural element (eg, as specified in 1080). Relationships can be represented as lines (ie, non-directional relationships) or arrows (ie, directional relationships). Displayed rectangles, lines, and arrows may be labeled with corresponding names (eg, assigned using dialog 850). Workspace 860 may also include tabs 1110, which correspond to each pooling layer. For example, the structural elements belonging to each pooling layer can be accessed by selecting the corresponding tab.

As shown in Figure 11, the tab "Tutorial Creation Using L3" indicates the currently viewed tutorial in the workspace. The labels "tutorial", "basic idea" and "knowledge structure" correspond to pooling layers within the tutorial. The tab "Knowledge Structure" is selected and the corresponding structural elements and relationships are shown. The label may be populated to correspond to the name of the structural element or pooling layer, which may be assigned using dialog 850 .

Structural elements can be added to tutorials and constructed using workspace 860. For example, a pop-up menu can be accessed by right-clicking on workspace 860 . The pop-up menu includes any available structural elements that can be added to the selected tab. To generate a structure element, the author selects the appropriate button from the pop-up menu corresponding to the type of structure element to be added.

For example, to generate sub-tutorial 120, the author selects sub-tutorial 120 from a pop-up menu. When the button New Sub-Tutorial 120 is selected, the menu presents a choice of Empty or User. The empty option can be selected to open an empty sub-tutorial 120 . User options may be used to open sub-tutorials 120 with predetermined content, such as learning units 130 and/or knowledge items 140 . Dialog 850 corresponding to sub-tutorial 120 is automatically configured. Using the corresponding dialog box 850 , the author can enter the name, properties, comments and keywords for the sub-tutorial 120 . Subtutorials 120 are added to tutorial overview 830 and a rectangle is added to workspace 860 with a color code (eg, green) corresponding to the subtutorial.

To generate the learning unit 130, the author can select the appropriate button from the pop-up menu. When the button New Learning Unit 130 is selected, the menu appears empty or a user's choice. The empty option may be selected to open any empty learning unit 130 . User options may be used to open learning units 130 , such as knowledge items 140 , with predetermined content. The corresponding dialog box 850 prompts the author to enter the name, attributes, notes and keywords of the learning unit 130 . Learning units 130 are displayed in tutorial overview 830 and in workspace 860 as rectangles with corresponding color coding (eg, purple).

To create a knowledge item 140, the author selects the appropriate button from the pop-up menu. For example, selecting the button Create knowledge item 140 automatically configures dialog box 850 corresponding to knowledge item 140 . Dialog box 850 may be used to enter a name, attributes, notes and keywords corresponding to knowledge item 140 . Knowledge items 140 appear in tutorial overview 830, and workspace 860 is filled with a corresponding color-coded (eg, brown) rectangle. Dialog 850 also enables authors to assign content to knowledge items 140 . For example, content field 1050 in dialog 850 may be used to create a reference to a media file corresponding to knowledge item 140 . The media file can be any media that can be displayed by a browser. Authors can also assign appropriate knowledge types (e.g., directions, explanations, actions, quotes) and media types (such as text, images, graphs, pictures, sounds, movies, video, audio, chat groups, email, etc.) to knowledge items 140. video conferencing, whiteboards, phones and PDAs).

The pop-up menu can also be used to create quizzes and collaboration scenarios. When Quiz is selected from the pop-up menu, dialog 850 appears. Dialog 850 includes tabs General, Test Parameters, Notes, and Keywords. Users can use regular tags to insert name, content, time and LOM files. Additionally, rectangular quizzes with corresponding color codes appear in the workspace. The quiz parameter tag may be used to indicate the type of quiz (eg, pre-test, practice, self-test, or post-test). Collaborative scenarios can be used to provide learners with opportunities to interact with other learners.

To create a relationship between structural elements, the author selects a structural element in workspace 860 . The author then selects a relationship from the pop-up menu. The author can drag the line and arrow corresponding to the relationship from the selected structural element to the second structural element. Authors can also edit and remove unwanted relationships by selecting existing relationships in the work area and using the pop-up menus to edit and delete said relationships.

After completing the tutorial and associated tutorial structure, the author can activate the embedded learning management system 760 to preview the tutorial. The embedded learning management system 760 applies the selected policies to the lessons. To preview the tutorial, the author activates the embedded learning management system 760 using the authoring tool. The authors then choose a strategy. The embedded learning management system 760 applies the selected strategy to the course structure and determines a navigation path. The navigation path is presented to the author (in a manner similar to the display a learner would receive). Authors can continue to select other strategies and view the corresponding tutorial navigation path views. Once the author is satisfied with the tutorial structure, the author may transmit the tutorial to the learning system 620 for publishing the tutorial.

Tutorial Navigation

The structure of the tutorial consists of several diagrams of the structural elements contained in the tutorial. Navigation trees can be determined from graphs by applying selected learning strategies to these graphs. The navigation tree can be used to navigate the learner's path through the tutorial. Depending on where the learner is in the tutorial, only certain parts of the navigation tree are shown to the learner at the learning portal.

As described above, the learning strategy is applied to a static tutorial structure comprising structural elements (nodes), metadata (attributes) and relationships (edges). These data are created when the content structure is determined (for example by the tutorial author). Once the tutorial structure is created, the tutorial player processes the content structure using policies to present the material to learners at the learning portal.

To process the tutorial, the tutorial player grants policies permission to access the tutorial data and corresponding properties. Policies are used to prepare records of predicates, functions, operations, and sequences for computing navigation suggestions, as explained in more detail below.

The content player 720 accesses files in the content repository (eg, XML storing tutorial graphs and related media content) and applies learning policies to the files to generate a path to complete the tutorial. By imposing a learning policy, the content player 720 generates a collection of tutorial-related graphs (which are simply ordered lists of nodes) that are used to generate a node navigation tree. This collection of nodes can be stored to generate an ordered list of nodes that can be used to present the learner's path through the material. The embedded LMS 760 can also generate routes in the same way. In general, graphs and policies can "interact" in the following ways:

1. Strategies implement a set of Boolean predicates that can be applied to graph nodes. For example: isCompleted(node).

2. Possibility to inform policies of events that a certain behavior has been performed on a graph node. For example: Navigated(node).

3. A policy may provide a function for computing a new set of nodes for a given node. For example: NavigationNodes(node).

4. The strategy provides a sorting function that converts the node set calculated in 3 into an ordered list.

5. A policy may decide to change the attributes of certain policy-related nodes. For example: node. setVisited(true).

Note that the reason for using the last point is that the strategy does not maintain any internal state. Instead, all policy-related properties are stored in properties of graph nodes, allowing policies to be changed "on the fly" during graph traversal.

As mentioned above, there is a collection of nodes that can be used to generate a path through the tutorial. A collection of nodes is a "navigation node". Navigation nodes may include all nodes identified by the policy that are immediately reachable from the current node. In other words, a navigation node represents a potential immediate successor from the current node. Another set of nodes is the "start node". The start node is the potential starting point when entering a new graph. The more starting points this set contains, the more choices the learner will have when entering the unit. Therefore, any strategy should implement at least two functions that can compute these sets and a function that converts these sets into ordered lists. These functions are described in more detail below using the following examples.

In the following example, these definitions are used:

C is all tutorial collections.

G is a collection of graphs.

V is a collection of vertices (eg, knowledge items, references to units of study, references to subtutorials, and quizzes). When talking about graphs in the mathematical sense, vertices are used (whereas nodes can be used to refer to the resulting tutorial structure).

E is a collection of edges (eg, relation types, as used in a mathematical sense).

TG = {sc, lu} is a set of graph types, where:

sc = sub-tutorial; and

lu = unit of study.

TC = {sc, lu, co, tst} is the set of content types, where:

sc = sub-tutorial;

lu = unit of study;

co = content; and

tst = test.

(For empowering learners when passing the test, only pre-test and post-test are defined as tests, and self-tests and exercises are content rather than pre-tests).

TK={...} is the set of all knowledge types (such as described in the e-learning content structure section).

TR={...} is the set of all relation types (eg, as described in the e-learning content structure section).

BOOL={true, false} is a Boolean set, having values true and false.

MAC={...} is a set of macro policies (eg, as described in the eLearning Policies section).

MIC={...} is a set of micropolicies (eg, as described in the eLearning Policies section).

COMP={...} is the set of all capabilities.

LCOMPCOMP is the set of learner capabilities.

TST={pre, post} is a collection of test types, for example:

pre = pretest; and

post = post-test.

Tutorial c=(G _c , g _s , mac, mic)∈C can be defined as follows:

G _c is the collection of all sub-tutorials and learning units that are elements of c;

g _s is the starting graph of tutorial c, in particular, g _s ∈ G;

mac®MAC is the selected macro strategy for the navigation tutorial; and

mic∈MIC is the chosen micro-strategy for the navigation tutorial.

The processing of the tutorial begins with the start diagram.

A graph g=(V _g , E _g , t _g , comp _g )∈G can be defined as follows:

V _g is the set of all vertices in g;

E _g V _g ×V _g ×TR is the set of all edges in g;

t _g ∈ TG is the graph type of g; and

comp _g COMP is the graph capability.

In the following description, the term "content graph" is used to identify the subgraph to which a vertex refers, rather than the graph that includes the vertex. Vertices can be thought of as representing "place markers" for subgraphs.

Vertex v=(vs _v , tc _v , gc _c , tk _v , tt _v , mscore _v , ascore _c )∈V can be defined as follows,

in:

vs _v ∈ BOOL is the visited state of v;

tc _v ∈ TC is the content type of v;

gc _v ∈ G is the content graph of v;

tk _v ∈ TK is the knowledge type of v;

tt _v ∈ TST is the test type of v;

mscre _v is the highest possible test score for v; and

ascore _v is v's actual achieved test score.

An edge or relation type e=(v _s , v _e , tr _e )∈E can be defined such that:

v _s ∈ V is the starting vertex of e;

v _E ∈ V is the end vertex of e; and

tr _e ∈ TR is the relation type of e.

The predicate is the mapping p: V → BOOL, which assigns the value b _p ∈ BOOL to each vertex v ∈ V. therefore:

b _p =p(v).

The order is the map ord:V×V→BOOL, which assigns the value b _ord ∈ BOOL to a pair of vertices v ₁ , v ₂ ∈V. therefore:

b _ord =ord(v ₁ , v ₂ ).

Mapping sort: V ⁿ , ord→V ⁿ is a sorting function from the vertex set V ⁿ to the vertex set (v ₁ ,...,v _n )=V ⁿ using the order ord, if:

(v ₁ ,...,v _n )=sort(V ⁿ ,ord) such that for i≤j,

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; j</span>}}{<span\; class="notranslate">\; \&ForAll;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}^{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; ord</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; true</span>}<span\; class="notranslate">\; .</span>$

The following descriptions explain the usage of the attributes. Attributes are used to define and implement learning strategies.

Let g=(V _g , E _g , t _g , comp _g )∈G be a graph with the following properties:

g.nodes=V _g is the vertices of g;

g.type = t _g is the type of g; and

g.comp=comp _g is the capability of the graph.

Let v = (vs _v , tc _v , gc _c , tt _v , tt _v , mscore _v , ascore _v ) ∈ V be a vertex with the following properties:

v.visited=vs _v is the visited state of the vertex (initially the value is false);

v.graph={g=(V _g , E _g , t _g )∈G|v∈V _g } is a graph containing v;

v.cotentType=tc _v is the content type of v;

为v的内容图；

is the content map of v;

v.knowType=tk _v is the knowledge type of v;

$v.\mathrm{testType}=\left\{\begin{array}{c}t{t}_v\&Element;\mathrm{TST}:t{c}_v=\mathrm{tst}\\ \mathrm{undef}:\mathrm{otherwise}\end{array}\right.$ is the test type of v;

v.mscore=mscore _v is the highest possible test score of v (initially the value is 0);

v.ascore=ascore _v is the test score actually achieved by v (initially the value is -1).

Let e=(v _S , v _E , tr _e )∈E be an edge with the following properties:

e.start=v _s is the starting vertex of e;

e.end=v _E is the end point of e;

e.type=tr _e is the relationship type of e.

The logical direction of the edges does not have to match the direction indicated by the tutorial player, since the tutorial player displays edges in "read direction". This applies to subsequent edges, eg e = (v _S , v _E , "is a subset of"). The following explanations refer to logical directions, in other words, in the case described above, the directions of the edges are considered "rotated". In the following, undirected edges are treated as two opposite-directed edges.

Predicates are "dynamic properties" of vertices. Strategies compute dynamic properties for individual vertices when needed.

The following are examples of predicates:

Visited(v): Vertex v has been visited;

Suggested(v): The vertex v is suggested;

CanNavigate(v): can navigate the vertex v; and

Done(v): Vertex v is completed.

If the vertex is within a learning unit (ie v.graph.type=lu), micropolicies are used to compute the predicate. The selected macro strategy is responsible for determining all other vertices.

The "function" is used to compute the navigation set (the displayed vertices). The function should return a collection of vertices. Policy implementation function.

For example, the following functions are:

V = StartNodes(g) = { v| v is the start vertex of g} is the set of all start vertices of graph g.

Start vertices are the vertices of the graph from which navigation can be initiated according to the chosen strategy.

V = NextNodes(v) = { v| v is the successor of v} is the set of all successor vertices of vertex v.

For micropolicies, the selected macropolicy calls the function when needed. When entering a unit of study, the macro-policy selects the appropriate (chosen) micro-policy.

"Actions" provide information to the selected strategy, ie information about specific events that occurred during the navigation tutorial. Policies can use these operations to change attributes. These operations are:

navigate(v); during the tutorial's navigation, once the vertex v is navigated, run the time loop

The environment calls this operation.

testDone(v, MaxScore, ActScore); if vertex v is a completed test

(v.connectType=tst), the runtime environment invokes this operation. MaxScore contains

The highest possible grade, the ActScore contains the actual achieved grade.

Micropolicy computes these operations if the vertex is in a learning unit, which means v.graph.type=lu. Macro policies are responsible for all other vertices.

The runtime environment uses the sort function to sort the evaluated navigation collection. The order determines the order in which vertices are displayed. Put the "most important" vertex (eg from a policy point of view) at the beginning of the list (as the next suggested vertex). Strategies implement these ordering functions, and the runtime environment provides them. The following examples of sort functions can be defined:

sortNav(V) is used to sort the collection of navigation vertices.

The sort function is called automatically whenever the function has returned the set of vertices to the current policy. Therefore every macro and micropolicy must have a sort function for its use.

The following descriptions explain the predicates, operations, functions, and ordering functions associated with macro policies.

Here is an example of how to implement a top-down (deductive) learning strategy.

A predicate for a top-down strategy can be defined as follows:

Visited(v): v.visited

Sets the "visited" property of the vertex.

Suggested(v):( v, v, tr)∈E, where tr=prerequisite, then:

Done( v)=true

All prerequisites for Vertex are met.

CanNavigate(v): Suggested(v)

In this example, used in a similar fashion to Suggested.

Done(v):

(v.contentType∈{sc,lu}∧v.contentGraph.comp≠LCOMP)∨

(v.contentType≠tst∧v.visited=true∧( v∈StartNodes(v.eontentGraph)

: Done(v)＝true))∨(c.contentType＝tst∧(v.ascore*2)≥v.mscore)

A vertex v is considered complete if at least one of the following conditions holds:

It includes learning units or sub-tutorials that have a non-empty set of competencies that the learner already possesses;

It contains no quizzes, has been visited, and has completed the start vertex of all content graphs;

and / or

It handles the quiz and has achieved at least half of the maximum grade.

The function for the top-down strategy can be defined as follows:

$\mathrm{<span\; class="notranslate">\; startNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; undef</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; \&phi;</span>}\\ \mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; type</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; lu</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; mic</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; StartNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; type</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; sc</span>}<span\; class="notranslate">\; :</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&ForAll;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; hierarchical</span>}<span\; class="notranslate">\; \}</span>\end{array}\right.$

If g is undefined (undef), meaning the vertex does not have any content graph, then the collection is empty.

If g is a learning unit, the StartNodes() function of the chosen microstrategy will be used.

If g is a subtutorial, all vertices that do not have any hierarchical relationship associated with them will be returned.

$\mathrm{<span\; class="notranslate">\; NextNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&Exists;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ,</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \&cup;</span>\mathrm{<span\; class="notranslate">\; StartNode</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; contrntGraph</span>}<span\; class="notranslate">\; )</span>$

Connect them to all vertices of v by external directed relations, plus all vertices that are the start vertices of v's content graph.

The top-down operations can be defined as follows:

navigate(v):v.visited=true

Set the "visited" property of the vertex to true.

testDone(v, MaxScore, ActScore): v.mscore = MaxScore, v.ascore = ActScore

if

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; Done</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; true</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; LCOMP</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; LCOMP</span>}<span\; class="notranslate">\; \&cup;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; comp</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&ForAll;</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; :</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; visited</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; true</span>}\\ \mathrm{<span\; class="notranslate">\; Done</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; false</span>}<span\; class="notranslate">\; :</span><span\; class="notranslate">\; \&ForAll;</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; :</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; visited</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; false</span>}\end{array}\right.$

Set the highest quiz score and the actual achieved quiz score for the apex.

If the quiz passes, the learner's competencies will be enlarged to include the graph's competencies, and

Set all vertices of the graph to "visited".

If the test fails, reset all vertices of the graph to "not visited".

The sorting function sortNav(V) can be defined according to the order relationship on the vertex set <: V ₁ ×V ₂ →bool. This requires the following helper functions to be defined:

1. Define the order relationship for the vertices according to the vertex ID

< _id : V × V → bool

v ₁ < _id v ₂ :  v ₁ .id < v ₂ .id

2. Define a comparison relation for the vertices against the vertex ID.

＝：V×V→bool

v ₁ =v ₂ : v ₁ .id=v ₂ .id

3. Define sequence relationships based on test types and unit types.

< _test (TC×TST)×(TC×TST)→bool

(tst, pre) < (co, undef) < (lu, undef) < (tst, post)

4. Define a 3-based order relationship for the vertices for the test type and unit type.

< _test : V × V → bool

v ₁ < _test v ₂ (v _1. contentType, v _1. testType) < _test (v _2. contentType, v _2. testType)

5. Define comparison relationships for vertices against test types and unit types.

＝ _test : V×V→bool

v ₁ = _test v ₂ (v ₁ .cotentType, v ₁ .testType) = (v ₂ .contentType, v ₂ .testType)

6. According to one of the micro-strategies (see micro-strategies), define the order relationship about the knowledge type

< _micro : TK × TK → bool

7. Define a 6-based ordering relationship for vertices for micropolicies.

< _micro : V × V → bool

v1< _micro v ₂  v1.knowType< _micro v ₂ .knowType

8. For the knowledge type, define the comparative relationship with the vertex

＝ _micro : V×V→bool

v ₁ = _micro v ₂  v ₁ .knowType = v ₂ .knowType

Using these definitions, the function <: V × V → bool can be defined as follows:

Note that if g ₁ =g ₂ , then obviously V ₁ =V ₂ , E ₁ =E ₂ , t ₁ =t ₂ , and comp ₁ =comp ₂ . Also, in Case 3, a state is maintained in which there is no direct relationship between vertices, but there is a relationship with higher-order vertices. In this way, this ordering relationship also applies to all vertices in the content graph of this vertex. This is shown in Figure 28, where v is the vertex representing the unit of study and _v1 , _v2 are the vertices under investigation.

The function SortNav(V) sorts the set V according to the order relation <.

The following procedure is one way to implement the function sortNav(V):

1. V _preTest = {v∈V|v.contentType=tst∧v.testType=pre}: the set of all pre-tests.

2. V=VV _preTest : Remove all pretests from V.

3. V _postTest = {v∈V|v.contentType=tst∧v.testType=post}: set of all posttests.

4. V=VV _postTest : Remove all posttests from V.

5. $V_{\mathrm{preReq}}=\{v\&Element;V|\&Exists;(\stackrel{\&OverBar;}{v},v,\mathrm{tr})\&Element;\mathrm{E.}:\mathrm{tr}=\mathrm{prerequisites}\}:$ The set of all vertices that have a prerequisite relationship towards them.

6. V = VV _preReq : Remove from V all vertices in V _preReq .

7. L=V _preTest : Add all pretests to the sorted list.

8. L=L∪{v∈V|v.contentType=co}, V=V-L: Enlarge the sorted list to include all vertices with learning units, then remove these vertices from V.

9. L=L∪{v∈V|v.contentType=lu}, V=V-L: Enlarge the sorted list to include all vertices containing learning units, then remove these vertices from V.

10. L=L∪V: Enlarge the sorted list to include the remaining vertices in V.

11. Search all vertices in v ∈ V _preReq :

Vertex v ^* ∈L, where (v ^* , v, prerequisite)∈E∧dist(v ^* )=MAX (the vertex furthest backward in L and having a prerequisite relationship with v).

Add v to L after v ^* .

12. L=L∪V _postTest : Enlarge the sorted list to include all posttests.

13. As a result, the sorted list L is returned.

Sort the sub-sets themselves determined in steps 7-12 by order relation < _id .

Here is an example of how to implement a bottom-up (inductive) learning strategy.

The predicates for this policy can be the same as for the top-down macro policy. The function for bottom-up can be defined as follows:

$\mathrm{<span\; class="notranslate">\; startNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; undef</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; \&phi;</span>}\\ \mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; type</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; lu</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; mic</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; StartNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; type</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; sc</span>}<span\; class="notranslate">\; :</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&ForAll;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; v</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; \&NotEqual;</span>\mathrm{<span\; class="notranslate">\; hierarchical</span>}<span\; class="notranslate">\; \}</span>\end{array}\right.$

If g is undefined, the vertex has no content graph and the collection is empty.

If g is a learning unit, the StartNodes() function of the chosen microstrategy will be used.

If g is a subtutorial, all vertices that do not have any hierarchical relationships involving them will be returned.

$\mathrm{<span\; class="notranslate">\; NextNodes</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; \{</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>{\mathrm{<span\; class="notranslate">\; V</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}}<span\; class="notranslate">\; |</span><span\; class="notranslate">\; \&Exists;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ,</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; tr</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \&cup;</span>\mathrm{<span\; class="notranslate">\; StartNode</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; contrntGraph</span>}<span\; class="notranslate">\; )</span>$

Connect them to all vertices of v by external directed relations.

If the vertex contains a unit of study and one of the hierarchically dependent vertices has not been visited, then the set is enlarged to include the start vertex of the unit of study "for positioning only" using the micropolicy.

Otherwise the set is enlarged to include all vertices that are the start vertices of v's content graph.

The operation and ordering functions of the bottom-up strategy are similar to those of the top-down macro strategy and thus will not be repeated.

Linear macropolicies represent a special case of the macropolicies already described. In a linear macro strategy, elements of a sorted set of vertices for navigation are provided sequentially rather than simultaneously. This linearization can be applied to any combination of macro and micro strategies.

The following description includes examples of how to implement micropolicies. In this example, the micro-policies for positioning only are described.

The predicates for this micropolicy can be defined as follows:

Visited(v): v.visited

Set the "visited" property of a vertex

Suggested(v):( v, v, tr)∈E, where Tr=prerequisite, then: Done( v)=true

All prerequisites for the apex have been met.

CanNavigate(v): Suggested(v)

This can be used like Suggested.

Done(v):

(v.contentType≠tst∧v.visited=true)∨

(c.contentType=tst∧(v.asvcore*2)≥v.mscore)

A vertex is considered complete if:

It contains no quizzes and has already been visited.

It handles the quiz and has achieved at least half of the maximum grade. The function can be defined as follows:

StartNodes(g)＝{v∈V _E |v.knowType＝Orientation}∪

The set of all vertices with knowledge type orientation, summed with knowledge type orientation

Vertices All vertices that have a prerequisite relationship.

NextNodes(v)=Φ

For this micropolicy, this collection is always the empty collection. In other words, there are no successor vertices because all related vertices are included in the set of the starting vertex.

An operation can be defined as follows:

navigate(v):v.visited=true

Set the "visited" property of the vertex to true.

testDone(v, MaxScore, ActScore): v.mscore = MaxScore, v.ascore = ActScore

if

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; Done</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; true</span>}<span\; class="notranslate">\; :</span>\mathrm{<span\; class="notranslate">\; LCOMP</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; LCOMP</span>}<span\; class="notranslate">\; \&cup;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; comp</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&ForAll;</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; :</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; visited</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; true</span>}\\ \mathrm{<span\; class="notranslate">\; Done</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; false</span>}<span\; class="notranslate">\; :</span><span\; class="notranslate">\; \&ForAll;</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; graph</span>}<span\; class="notranslate">\; :</span>\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; .</span>\mathrm{<span\; class="notranslate">\; visited</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; false</span>}\end{array}\right.$

Set the highest test score and the actual achieved test score for this vertex.

If the test passes, the learner capabilities will be scaled up to include graph capabilities, and all graph vertices will be set to "visited".

If the test fails, reset all graph vertices to "unvisited".

A ranking function similar to that of the top-down macropolicy can be used only for positioning micropolicies, so it is not restated.

The following is an example of an implementation of the example-oriented policy. The predicates for this policy are the same as for the targeting-only micro-policy, so they are not restated.

The function can be defined as follows:

StartNodes (g) = V _g

All vertices contained in the unit of study.

NextNodes(v)＝ _Φg

For this micropolicy, this collection is always the empty collection. In other words, there are no successor vertices because all related vertices are included in the starting vertex set.

The actions for the "for example" micropolicy are the same as for the "for targeting only" micropolicy, so they are not repeated.

An example-oriented sort function can be defined as follows:

The steps to execute sortNav(V) are as follows:

所有包含示例的顶点的集合，加这些顶点的先决条件。1.V _example ＝{v∈V|v.knowType＝Example}∪

The set of all vertices containing examples, plus the prerequisites for those vertices.

2. V _remain = VV _example : the remaining vertices of V.

3. L _examp =TopDown.sortNav(V _examp ): sort the set of examples using the top-down strategy sorting algorithm.

4. L _remain =TopDown.sortNav(V _remain ): use the top-down strategy sorting algorithm to sort the set of remaining vertices.

5. L=L _exam ∪L _remain : form the union of two sorted lists.

6. As a result, the sorted list L is returned.

The predicates, functions, and operations for explanation-oriented micropolicies are the same as for example-oriented micropolicies, so they are not repeated. The ranking function for explanation-oriented micropolicies is similar to the ranking function for example-oriented micropolicies (the only difference is that explanations rather than examples are used to form the two sets).

The predicates, functions, and operations for behavior-oriented micropolicies are the same as for example-oriented micropolicies, so they are not repeated. The ranking function for behavior-oriented micropolicies is similar to the ranking function for example-oriented micropolicies (the only difference is that behaviors rather than examples are used to form the two sets).

Various implementations have been described. However, it should be appreciated that various modifications may be made. For example, if the various steps of the disclosed technology are performed in a different order, and/or if components in the disclosed system, architecture, device, or circuit are combined in a different manner, and/or other components are replaced or substituted for all Good results may be obtained by disclosing parts of a system, architecture, device, or circuit. Accordingly, other implementations are within the scope of the following claims.

Claims (60)

1. study course authoring system comprises:

The author station is used to create study course;

Content Management System is used to store the study course and the course structure that comprise content;

Learning management system is used for determining that according to the learning strategy that is applied to described content and structure learning content is to present to the learner;

Communication link is used for study course is sent to Content Management System from the author station.

2. according to the study course authoring system of claim 1, wherein said author station comprises the author tool that is configured to increase to study course structural elements.

3. according to the study course authoring system of claim 2, wherein said structural elements comprises one or more in study course, sub-study course, unit or the knowledge item.

4. according to the study course authoring system of claim 2, wherein said author tool is configured to structure is associated with structural elements.

5. according to the study course authoring system of claim 2, wherein said study course and structural elements thereof not mandatory learning person should make the order of the structural elements that is used for traveling through study course.

6. according to the study course authoring system of claim 2, wherein said author tool is configured to create the metadata that will be associated with structural elements.

7. according to the study course authoring system of claim 6, wherein said metadata comprises attribute, comprising one of knowledge type and ability.

8. according to the study course authoring system of claim 2, wherein said author station is configured to create the relation between the structural elements.

9. according to the study course authoring system of claim 8, wherein said relation is directed.

10. according to the study course authoring system of claim 1, wherein Content Management System comprises content player, and content player is configured to content Applied Learning strategy to determine that navigation content is to present to learner's path.

11. study course authoring system according to claim 10, wherein said content comprises the relation between structural elements and the structural elements, structural elements not the mandatory learning person should make the order of the structural elements that is used for traveling through study course, described content player be configured to study course Applied Learning strategy with produce to user's suggestion, with the order of structural elements associated content.

12. according to the study course authoring system of claim 11, wherein learning strategy is grand strategy.

13. according to the study course authoring system of claim 12, wherein grand strategy comprises one of the bottom-up strategy of the top-down strategy of using based target, based target and contents table strategy.

14. according to the study course authoring system of claim 11, wherein said learning strategy is little strategy.

15. according to the study course authoring system of claim 14, wherein said little strategy comprise use a positioning strategy, towards behavioral strategy, towards explanation strategy, towards one of positioning strategy and contents table strategy.

16. according to the study course authoring system of claim 2, wherein Content Management System comprises content repository, the metadata that it is configured to store course content and structural elements and is associated with structural elements.

17. study course authoring system according to claim 16, wherein said content player is configured to access course, structural elements and metadata from content repository, and to the structural elements application strategy of being visited to come according to institute's application strategy to learner's suggestion and structural elements associated content.

18. according to the study course authoring system of claim 1, wherein said author station comprises:

Input end is used to select learning strategy;

The flush type learning management system is used for creating guidance path according to selected learning strategy.

19. according to the study course authoring system of claim 18, wherein said learning strategy is grand strategy.

20. according to the study course authoring system of claim 19, wherein said grand strategy comprises one of the bottom-up strategy of the top-down strategy of using based target, based target and contents table strategy.

21. according to the study course authoring system of claim 18, wherein said learning strategy is little strategy.

22. according to the study course authoring system of claim 21, wherein said little strategy comprise use a positioning strategy, towards behavioral strategy, towards explanation strategy, towards one of positioning strategy and contents table strategy.

23. according to the study course authoring system of claim 18, wherein said author station is configured to according to the guidance path editor study course of being created to produce the guidance path based on the expectation of the study course of being edited.

24. according to the study course authoring system of claim 1, wherein also comprise the system of operation and management and communication link, be used for transmitting course information to the system of operation and management.

25. according to the study course authoring system of claim 24, wherein said course information comprises one of title, author, description, condition precedent, the ability that is obtained and needed ability.

26. according to the study course authoring system of claim 1, wherein said author station is configured to the preview study course.

27. according to the study course authoring system of claim 1, wherein said author station is configured to revise study course.

28. an author station comprises:

Author's entering apparatus is used for input command;

Author tool is used to receive the order of input and comes assemble structural elements in response to described order, and described author tool comprises and is used to present structural elements, is used for choice structure unit and is used for content and structure unit is associated to form the interface of study course.

29. the author station according to claim 28 also comprises communication interface, is used for transmitting study course to Content Management System.

30. according to the author of claim 28 station, wherein said structural elements comprises one or more in study course, sub-study course, unit or the knowledge item.

31. according to the author of claim 28 station, wherein said study course and structural elements thereof not mandatory learning person should make the order of the structural elements that is used for traveling through study course.

32. according to the author station of claim 28, wherein said author tool is created the metadata that will be associated with structural elements.

33. according to the author station of claim 28, wherein said metadata comprises attribute, comprising one of knowledge type or ability.

34. according to the author station of claim 28, wherein the author station is configured to create the relation between the structural elements.

35. according to the author station of claim 34, wherein said relation is directed.

36. the author station according to claim 28 also comprises the flush type learning management system, it is configured to study course Applied Learning strategy with the suggestion order of generation with the structural elements associated content.

37. according to the author station of claim 36, wherein learning strategy is grand strategy.

38. according to the author of claim 37 station, wherein said grand strategy comprises one of the bottom-up strategy of the top-down strategy of using based target, based target or contents table strategy.

39. according to the author station of claim 36, wherein said learning strategy is little strategy.

40. according to the author of claim 39 station, wherein little strategy comprise use a positioning strategy, towards behavioral strategy, towards explanation strategy, towards one of positioning strategy or contents table strategy.

41. according to the author station of claim 28, also draw together the flush type learning management system, wherein author tool is configured to present one or more learning strategies, described author's entering apparatus is configured to select described one or more learning strategy; And the flush type learning management system is configured to create guidance path according to selected learning strategy.

42. according to the author station of claim 41, wherein learning strategy is grand strategy.

43. according to the author of claim 42 station, wherein said grand strategy comprises one of the bottom-up strategy of the top-down strategy of using based target, based target and contents table strategy.

44. according to the author station of claim 41, wherein said learning strategy is little strategy.

45. according to the author of claim 44 station, wherein little strategy comprise use a positioning strategy, towards behavioral strategy, towards explanation strategy, towards one of positioning strategy and contents table strategy.

46. want 41 author station according to right, wherein said author station is configured to according to the guidance path editor study course of being created with the guidance path of generation based on the expectation of the study course of being edited.

47. the author station according to claim 28 also comprises communication interface, is used for transmitting course information to the system of operation and management.

48. according to the author station of claim 47, wherein said course information comprises one of title, author, description, condition precedent, the ability that is obtained and needed ability.

49. according to the author station of claim 28, wherein said author tool is configured to the preview study course.

50. according to the author station of claim 28, wherein said author tool is configured to revise study course.

51., wherein be that the author tool interface comprises course editor according to the author station of claim 28.

52. according to the author station of claim 51, wherein said course editor is configured to present the study course general view to show and to select the expression of the structural elements of study course.

53. according to the author station of claim 51, wherein said course editor is configured to present the structural elements of workspace with editor and demonstration study course.

54. according to the author station of claim 51, wherein said course editor is configured to present a dialog box to show corresponding to the data field in the structural elements shown in the operation window.

55. according to the author station of claim 51, wherein said course editor is configured to attribute is associated with structural elements.

56. according to the author station of claim 55, wherein said attribute comprises behavior knowledge.

57. according to the author station of claim 56, wherein said attribute comprises knowledge of orientation.

58. according to the author station of claim 57, wherein said attribute comprises quotes knowledge.

59. according to the author station of claim 58, wherein said attribute comprises example knowledge.

60. according to the author station of claim 28, wherein said author tool is configured to create test.

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