Classifications

• • G—PHYSICS
  • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
  • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
  • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
• • G—PHYSICS
  • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
  • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
  • G09B5/00—Electrically-operated educational appliances

Definitions

• the following description relates generally to e-learning and in particular to methods and systems for e-learning strategies.
• CBT computer-based training
• Newer methods for intelligent tutoring and CBT systems are based on special domain models that must be defined prior to creation of the course or content. Once a course is created, the material may not be easily adapted or changed for different learners' specific training needs or learning styles. As a result, the courses often fail to meet the needs of the trainee and/or trainer.
• a learning system and method apply learning strategies to course structure.
• the course structure includes a plurality of structural elements and one or more relations that indicate dependences between the structural elements.
• a learning strategy is selected and applied to the course structure.
• a sequence of structural elements is determined based on the applied learning strategy.
• Course content associated with the structural elements is suggested to be presented to the learner based on the determined sequence of structural elements.
• the learner may select the learning strategy.
• the learning strategy may be a macro-strategy or a micro-strategy.
• a macro- strategy may be applied to the course structure that includes a plurality of structural elements of sub-courses and learning units to determine the sequence of structural elements.
• the macro-strategy may be inductive, for example, a goal-based, top-down strategy.
• the goal-based, top down strategy ignores any of the relations that are not a hierarchical dependency.
• An inductive strategy suggests content from general knowledge to specific knowledge.
• the macro-strategy may deductive, for example, a goal-based, bottom-up strategy;
• the deductive strategy may suggest content from specific knowledge to general knowledge.
• the macro-strategy may be a table-of-contents strategy.
• the table-of-contents strategy ignores all relations when determining the sequence.
• the learning strategy may be a micro-strategy.
• the micro-strategy may be applied to a learning unit.
• the micro-strategy may be used to determine a sequence in which knowledge items within a learning unit are suggested.
• the sequence in which knowledge items are suggested may be determined based on attributes of the knowledge items.
• micro-strategies include orientation only, action orient, explanation oriented, orientation oriented.
• the micro-strategy of orientation only ignores all knowledge items that do not include knowledge of orientation, and may provide, for example, an overview of the course.
• the micro-strategy of action oriented selects knowledge items that include action knowledge before other knowledge items.
• the micro-strategy of explanation oriented selects knowledge items that include explanation knowledge before other knowledge items.
• the micro-strategy of orientation oriented selects knowledge items that include orientation knowledge before other knowledge items.
• Both a macro-strategy and a micro-strategy may be applied to the same course structure.
• the course structure does not provide a predetermined sequence of structural elements for presentation to the user.
• FIG. 1 is an exemplary content aggregation model.
• FIG. 2 is an example of an ontology of knowledge types.
• FIG. 3 is an example of a course graph for e-learning.
• FIG. 4 is an example of a sub-course graph for e-learning.
• FIG. 5 is an example of a learning unit graph for e-learning.
• FIGS. 6 and 7 are exemplary block diagrams of e-learning systems.
• FIG. 8 is an example showing v as the vertex that represents the learning unit LU : where v v 2 are the vertices.
• the e-leaming system and methodology structures content so that the content is reusable and flexible.
• the content structure allows the creator of a course to reuse existing content to create new or additional courses.
• the content structure provides flexible content delivery that may be adapted to the learning styles of different learners.
• E-learning content may be aggregated using a number of structural elements arranged at different aggregation levels. Each higher level structural element may refer to any instances of all structural elements of a lower level. At its lowest level, a structural element refers to content and may not be further divided. According to one implementation shown in Fig. 1, course material 100 may be divided into four structural elements: a course 110, a sub-course 120, a learning unit 130, and a knowledge item 140. Starting from the lowest level, knowledge items 140 are the basis for the other structural elements and are the building blocks of the course content structure. Each knowledge item 140 may include content that illustrates, explains, practices, or tests an aspect of a thematic area or topic. Knowledge items 140 typically are small in size (i.e., of short duration, e.g., approximately five minutes or less).
• a number of attributes may be used to describe a knowledge item 140, such as, for example, a name, a type of media, and a type of knowledge.
• the name may be used by a learning system to identify and locate the content associated with a knowledge item 140.
• the type of media describes the form of the content that is associated with the knowledge item 140.
• media types include a presentation type, a communication type, and an interactive type.
• a presentation media type may include a text, a table, an illustration, a graphic, an image, an animation, an audio clip, and a video clip.
• a communication media type may include a chat session, a group (e.g., a newsgroup, a team, a class, and a group of peers), an email, a short message service (SMS), and an instant message.
• An interactive media type may include a computer based training, a simulation, and a test.
• a knowledge item 140 also may be described by the attribute of knowledge type.
• knowledge types include knowledge of orientation, knowledge of action, knowledge of explanation, and knowledge of source/reference.
• Knowledge types may differ in learning goal and content.
• knowledge of orientation offers a point of reference to the learner, and, therefore, provides general information for a better understanding of the structure of interrelated structural elements.
• Knowledge items 140 may be generated using a wide range of technologies, however, a browser (including plug-in applications) should be able to interpret and display the appropriate file formats associated with each knowledge item.
• markup languages such as a Hypertext Markup language (HTML), a standard generalized markup language (SGML), a dynamic HTML (DHTML), or an extensible markup language (XML)), JavaScript (a client-side scripting language), and/or Flash may be used to create knowledge items 140.
• HTML may be used to describe the logical elements and presentation of a document, such as, for example, text, headings, paragraphs, lists, tables, or image references.
• Flash may be used as a file format for Flash movies and as a plug-in for playing Flash files in a browser.
• Flash movies using vector and bitmap graphics, animations, transparencies, transitions, MP3 audio files, input forms, and interactions may be used.
• Flash allows a pixel-precise positioning of graphical elements to generate impressive and interactive applications, for presentation of course material to a learner.
• Learning units 130 may be assembled using one or more knowledge items 140 to represent, for example, a distinct, thematically-coherent unit. Consequently, learning units 130 may be considered containers for knowledge items 140 of the same topic. Learning units 130 also may be considered relatively small in size (i.e., duration) though larger than a knowledge item 140.
• Sub-courses 120 may be assembled using other sub-courses 120, learning units
• the sub-course 120 may be used to split up an extensive course into several smaller subordinate courses.
• Sub-courses 120 may be used to build an arbitrarily deep nested structure by referring to other sub-courses 120.
• Courses may be assembled from all of the subordinate structural elements including sub-courses 120, learning units 130, and knowledge items 140. To foster maximum reuse, all structural elements should be self-contained and context free.
• Structural elements also may be tagged with metadata that is used to support adaptive delivery, reusability, and search/retrieval of content associated with the structural elements.
• learning object metadata defined by the IEEE "Learning Object Metadata Working Group” may be attached to individual course structure elements.
• the metadata may be used to indicate learner competencies associated with the structural elements.
• Other metadata may include a number of knowledge types (e.g., orientation, action, explanation, and resources) that may be used to categorize structural elements. As shown in Fig. 2, structural elements may be categorized using a didactical ontology 200 of knowledge types 201 that includes orientation knowledge 210, action knowledge 220, explanation knowledge 230, and reference knowledge 240.
• Orientation knowledge 210 helps a learner to find their way through a topic without being able to act in a topic-specific manner and may be referred to as "know what.”
• Action knowledge 220 helps a learner to acquire topic related skills and may be referred to as “know how.”
• Explanation knowledge 230 provides a learner with an explanation of why something is the way it is and may be referred to as “know why.”
• Reference knowledge 240 teaches a learner where to find additional information on a specific topic and may be referred to as "know where.”
• orientation knowledge 210 may refer to sub-types 250 that include a history, a scenario, a fact, an overview, and a summary.
• Action knowledge 220 may refer to sub-types 260 that include a strategy, a procedure, a rule, a principle, an order, a law, a comment on law, and a checklist.
• Explanation knowledge 230 may refer to sub-types 270 that include an example, a intention, a reflection, an explanation of why or what, and an argumentation.
• Resource knowledge 240 may refer to sub-types 280 that include a reference, a document reference, and an archival reference.
• Dependencies between structural elements may be described by relations when assembling the structural elements at one aggregation level.
• a relation may be used to describe the natural, subject-taxonomic relation between the structural elements.
• a relation may be directional or non-directional.
• a directional relation may be used to indicate that the relation between structural elements is true only in one direction.
• Directional relations should be followed. Relations may be divided into two categories: subject-taxonomic and non-subject taxonomic.
• Subject-taxonomic relations may be further divided into hierarchical relations and associative relations.
• Hierarchical relations may be used to express a relation between structural elements that have a relation of subordination or superordination. For example, a hierarchical relation between the knowledge items A and B exists if B is part of A.
• Hierarchical relations may be divided into two categories: the part/whole relation (i.e., "has part") and the abstraction relation (i.e., "generalizes”).
• the part/whole relation "A has part B” describes that B is part of A.
• the abstraction relation "A generalizes B” implies that B is a specific type of A (e.g., an aircraft generalizes a jet or a jet is a specific type of aircraft).
• Associative relations may be used refer to a kind of relation of relevancy between two structural elements. Associative relations may help a learner obtain a better understanding of facts associated with the structural elements. Associative relations describe a manifold relation between two structural elements and are mainly directional (i.e., the relation between structural elements is true only in one direction). Examples of associative relations include “determines,” “side-by-side,” “alternative to,” “opposite to,” “precedes,” “context of,” “process of,” “values,” “means of,” and “affinity.”
• the “determines” relation describes a deterministic correlation between A and B (e.g., B causally depends on A).
• the "side-by-side” relation may be viewed from a spatial, conceptual, theoretical, or ontological perspective (e.g., A side-by-side with B is valid if both knowledge objects are part of a superordinate whole).
• the side-by-side relation may be subdivided into relations, such as "similar to,” “alternative to,” and “analogous to.”
• the “opposite to” relation implies that two structural elements are opposite in reference to at least one quality.
• the "precedes” relation describes a temporal relationship of succession (e.g., A occurs in time before B (and not that A is a prerequisite of B)).
• the "context of relation” describes the factual and situational relationship on a basis of which one of the related structural elements may be derived.
• An "affinity" between structural elements suggests that there is a close functional correlation between the structural elements (e.g., there is an affinity between books and the act of reading because reading is the main function of books).
• Non Subject-Taxonomic relations may include the relations "prerequisite of and "belongs to.”
• the "prerequisite of and the "belongs to” relations do not refer to the subject-taxonomic interrelations of the knowledge to be imparted. Instead, these relations refer to the progression of the course in the learning environment (e.g., as the learner traverses the course).
• the "prerequisite of relation is directional whereas the "belongs to" relation is non-directional. Both relations may be used for knowledge items 140 that cannot be further subdivided. For example, if the size of the screen is too small to display the entire content on one page, the page displaying the content may be split into two pages that are connected by the relation "prerequisite of.”
• Competencies may be assigned to structural elements, such as, for example, a sub-course 120 or a learning unit 130.
• the competencies may be used to indicate and evaluate the performance of a learner as the learner traverse the course material.
• a competency may be classified as a cognitive skill, an emotional skill, an senso-motorical skill, or a social skill.
• the content structure associated with a course may be represented as a set of graphs.
• a structural element may be represented as a node in a graph.
• Node attributes are used to convey the metadata attached to the corresponding structural element (e.g., a name, a knowledge type, a competency, and/or a media type).
• a relation between two structural elements may be represented as an edge.
• Fig. 3 shows a graph 300 for a course.
• the course is divided into four structural elements or nodes (310, 320, 330, and 340): three sub-courses (e.g., knowledge structure, learning environment, and tools) and one learning unit (e.g., basic concepts).
• a node attribute 350 of each node is shown in brackets (e.g., the node labeled "Basic concepts" has an attribute that identifies it as a reference to a learning unit).
• an edge 380 expressing the relation "context of has been specified for the learning unit with respect to each of the sub- courses.
• Fig. 4 shows a graph 400 of the sub-course "Knowledge structure" 350 of Fig. 3.
• the sub-course "Knowledge structure" is further divided into three nodes (410, 420, and 430): a learning unit (e.g., on relations) and two sub-courses (e.g., covering the topics of methods and knowledge objects).
• a learning unit e.g., on relations
• two sub-courses e.g., covering the topics of methods and knowledge objects.
• the edge 440 expressing the relation "determines” has been provided between the structural elements (e.g., the sub- course “Methods” determines the sub-course "Knowledge objects” and the learning unit “Relations”.)
• the attributes 450 of each node is shown in brackets (e.g., nodes “Methods” and “Knowledge objects” have the attribute identifying them as references to other sub-courses; node “Relations” has the attribute of being a reference to a learning unit).
• Fig. 5 shows a graph 500 for the learning unit "Relations" 450 shown in Fig. 4.
• the learning unit includes six nodes (510, 515, 520, 525, 530, 535, 540, and 545): six knowledge items (i.e., "Associative relations (1)”, “Associative relations (2)”, “Test on relations”, “Hierarchical relations”, “Non subject-taxonomic relations”, and “The different relations”).
• An edge 547 expressing the relation "prerequisite” has been provided between the knowledge items "Associative relations (1)” and "Associative relations (2).”
• attributes 550 of each node are specified in brackets (e.g., the node “Hierarchical relations” includes the attributes "Example” and "Picture”).
• the above-described content aggregation and structure associated with a course does not automatically enforce any sequence that a learner may use to traverse the content associated with the course.
• different sequencing rules may be applied to the same course structure to provide different paths through the course.
• the sequencing rules applied to the knowledge structure of a course are learning strategies.
• the learning strategies may be used to pick specific structural elements to be suggested to the learner as the learner progresses through the course.
• the learner or supervisor e.g., a tutor
• a teacher determines the learning strategy that is used to learn course material.
• the learning progression may start with a course orientation, followed by an explanation (with examples), an action, and practice.
• a learner may choose between one or more learning strategies to determine which path to take through the course. As a result, the progression of learners through the course may differ.
• Learning strategies may be created using macro-strategies and micro-strategies.
• a learner may select from a number of different learning strategies when taking a course.
• the learning strategies are selected at run time of the presentation of course content to the learner (and not during the design of the knowledge structure of the course).
• course authors are relieved from the burden of determining a sequence or an order of presentation of the course material. Instead, course authors may focus on structuring and annotating the course material.
• authors are not required to apply complex rules or Boolean expressions to domain models thus minimizing the training necessary to use the system.
• the course material may be easily adapted and reused to edit and create new courses.
• Macro-strategies are used in learning strategies to refer to the coarse-grained structure of a course (i.e., the organization of sub-courses 120 and learning units 130).
• the macro-strategy determines the sequence that sub-courses 120 and learning units 130 of a course are presented to the learner.
• Basic macro-strategies include "inductive” and “deductive,” which allow the learner to work through the course from the general to the specific or 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 approach.
• the structural hierarchies are traversed from top to bottom. Relations within one structural element are ignored if the relation does not specify a hierarchical dependency.
• Goal-based bottom-up follows an inductive approach by doing a depth first traversal of the course material. The table of contents simply ignores all relations.
• Micro-strategies, implemented by the learning strategies target the learning progression within a learning unit. The micro-strategies determine the order that knowledge items of a learning unit are presented. Micro-strategies refer to the attributes describing the knowledge items. Examples of micro-strategies include "orientation only”, “action oriented”, “explanation-oriented", and "table of contents").
• the micro-strategy "orientation only” ignores all knowledge items that are not classified as orientation knowledge.
• the "orientation only” strategy may be best suited to implement an overview of the course.
• the micro-strategy "action oriented” first picks knowledge items that are classified as action knowledge. All other knowledge items are sorted in their natural order (i.e., as they appear in the knowledge structure of the learning unit).
• the micro-strategy "explanation oriented” is similar to action oriented and focuses on explanation knowledge. Orientation oriented is similar to action oriented and focuses on orientation knowledge.
• the micro-strategy "table of contents” operates like the macro-strategy table of contents (but on a learning unit level).
• an e-learning architecture 600 may include a learning station
• the learner may access course material using a learning station 610 (e.g., using a learning portal).
• the learning station 610 may be implemented using a work station, a computer, a portable computing device, or any intelligent device capable of executing instructions and connecting to a network.
• the learning station 610 may include any number of devices and/or peripherals (e.g., displays, memory/storage devices, input devices, interfaces, printers, communication cards, and speakers) that facilitate access to and use of course material.
• the learning station 610 may execute any number of software applications, including an application that is configured to access, interpret, and present courses and related information to a learner.
• the software may be implemented using a browser, such as, for example, Netscape communicator, Microsoft's Internet explorer, or any other software application that may be used to interpret and process a markup language, such as HTML, SGML, DHTML, or XML.
• the browser also may include software plug-in applications that allow the browser to interpret, process, and present different types of information.
• the browser may include any number of application tools, such as, for example, Java, Active X, JavaScript, and Flash.
• the browser may be used to implement a learning portal that allows a learner to access the learning system 620.
• a link 621 between the learning portal and the learning system 620 may be configured to send and receive signals (e.g., electrical, electromagnetic, or optical).
• the link may be a wireless link that uses electromagnetic signals (e.g., radio, infrared, to microwave) to convey information between the learning station and the learning system.
• the 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 administration management system 627. Each of these systems may be implemented using one or more servers, processors, or intelligent network devices.
• the administration system may be implemented using a server, such as, for example, the SAP R/3 4.6C + LSO Add-On.
• the administration system may include a database of learner accounts and course information.
• the learner account may include demographic data about the learner (e.g., a name, an age, a sex, an address, a company, a school, an account number, and a bill) and his/her progress through the course material (e.g., places visited, tests completed, skills gained, knowledge acquired, and competency using the material).
• the administration system also may provide additional information about courses, such as the courses offered, the author/instructor of a course, and the most popular courses.
• the content management system may include a learning content server.
• the learning content server may be implemented using a WebDAV server.
• the learning content server may include a content repository.
• the content repository may store course files and media files that are used to present a course to a learner at the learning station.
• the course files may include the structural elements that make up a course and may be stored as XML files.
• the media files may be used to store the content that is included in the course and assembled for presentation to the learner at the learning station.
• the learning management system may include a content player.
• the content player may be implemented using a server, such as, an SAP J2EE Engine.
• the content player is used to obtain course material from the content repository.
• the content player also applies the learning strategies to the obtained course material to generate a navigation tree for the learner.
• the navigation tree is used to suggest a route through the course material for the learner and to generate a presentation of course material to the learner based on the learning strategy selected by the learner.
• the learning management system also may include an interface for exchanging information with the administration system.
• the content player may update the learner account information as the learner progresses through the course material.
• the structure of a course is made up of a number of graphs of the structural elements included in the course.
• a navigation tree may be determined from the graphs by applying a selected learning strategy to the graphs. The navigation tree may be used to navigate a path through the course for the learner. Only parts of the navigation tree are displayed to the learner at the learning portal based on the position of the learner within the course.
• learning strategies are applied to the static course structure including the structural elements (nodes), metadata (attributes), and relations (edges). This data is created when the course structure is determined (e.g., by a course author).
• the course player processes the course structure using a strategy to present the material to the learner at the learning portal.
• the course player grants strategies access to the course data and the corresponding attributes.
• the strategy is used to prepare a record of predicates, functions, operations, and orders that are used to calculate navigation suggestions, which is explained in further detail below.
• the content player accesses files (e.g., XML files storing course graphs and associated media content) in the content repository and applies the learning strategies to the files to generate a path through the course.
• files e.g., XML files storing course graphs and associated media content
• the content player produces a set of course-related graphs (which is simply an ordered list of nodes) that are used to generate a navigation tree of nodes.
• the set of nodes may be sorted to generate an order list of nodes that may be used to present a path through the material for a learner.
• graphs and strategies may "interact" in the following ways:
• a strategy implements a set of Boolean predicates that can be applied to graph nodes. For example: isCompleted(node). 2.
• a strategy may be informed by an event that some sort of action has been performed on a graph node. For example: navigated(node).
• a strategy may provide functions that are used to compute new node sets for a given node. For example: NavigationNodes(node). 4. A strategy provides an ordering function that turns node sets computed number 3 into ordered lists.
• a strategy may decide to alter certain strategy-related node attributes. For example: ⁇ ode.setVisited(tr ⁇ e).
• nodes that may be used to generate a path through a course.
• One set of nodes are "navigation nodes.”
• Navigation nodes may include all nodes that the strategy identifies that may be immediately reached from the current node. In other words, the navigation nodes represent potential direct successors from a current node.
• Another set of nodes are "start nodes.” Start nodes are potential starting points when entering a new graph. The more starting points this set contains, the more choices a learner has when entering the unit.
• any strategy should implement at least two functions that can compute these sets and the ordering function that transforms those sets into ordered lists. The functions are described in further detail below using the following examples.
• C is the set of all courses.
• G is a set of graphs.
• V is a set of vertices (e.g., knowledge items, references to learning units, references to sub courses, and test) Vertices are used when talking about graphs in a mathematical sense (whereas nodes may used to refer to the resulting course structure)
• E is a set of edges (e.g., relations types as used in a mathematical sense).
• pretests and posttests are defined as tests; self-tests and exercises are content rather than tests.
• TK ⁇ ••• ⁇ is the set of all knowledge types (e.g., as described in the section E-learning content structure).
• TR ⁇ • • • ⁇ is the set of all relation types(e.g., as described in the section E- learning content structure).
• BOOL ⁇ true, false
• BOOL ⁇ true, false
• MIC ⁇ • • • ⁇ is the set of micro-strategies (e.g., as described in the section E-learning strategies).
• COMP ⁇ • •• ⁇ is the set of all competences.
• LCOMP c COMP is the set of a learner's competences.
• G c is the set of all sub-courses and learning units that are members of c ;
• g s is the start graph of course c , in particular g s e G ;
• mac e MAC is the macro-strategy that has been chosen for navigating the course;
• mic € MIC is the micro-strategy that has been chosen for navigating the course. Processing of the course begins with the start graph.
• E g ⁇ z V g x V g x TR is the set of all edges in g ; t g e TG is the graph type of g ; and comp g ⁇ z COMP is the competences of the graph.
• content graph is used to identify the subgraph to which a vertex refers, rather than a graph that includes the vertex. One can think of the vertex representing the "palceholder" of the sub-graph.
• Attributes are used to define and implement the learning strategies.
• v (vs v ,tc v ,gc c ,tk v ,tt v ,mscore v ,ascore v ) e V be a vertex with the following attributes: v.
• vs v is the visited status of vertex v (initially this value is false );
• v.contentType tc v is the content type of v ;
• vXestType - is the test type of ;
• Predicates are "dynamic attributes" of vertices.
• the strategy computes the dynamic attributes for an individual vertex when necessary.
• predicates ⁇
• Visited(v) the vertex v has already been visited; Suggested(v) : the vertex v is suggested; CanNavigate(v) :the vertex v can be navigated; and
• Done(v) the vertex v is done. If a vertex is within a learning unit (i.e., v.graphlype - lu ), then the micro- strategy is used to compute the predicates.
• the macro-strategy that is chosen is responsible for determining all other vertices.
• Functions are used to compute the navigation sets (vertices that are displayed). A function should return a set of vertices.
• the strategies implement the functions. For example, the following functions are:
• v is a starting vertex of g
• ⁇ is the set of all starting vertices of graph g .
• Starting vertices are the vertices of a graph from which navigation within the graph may be initiated in accordance with a chosen strategy.
• v is a successor of v ⁇ is the set of all successor vertices of vertex v .
• the chosen macro-strategy calls the functions as needed.
• the macro-strategy selects the appropriate (selected) micro- strategy.
• Operations provide information to the chosen strategy about particular events that occur during navigation of a course.
• the strategy may use them to change the attributes.
• the operations are: navigate(v) ;
• the runtime environment calls this operation as soon as the vertex v is navigated during the navigation of the course.
• testDone(v,MaxScore,ActScore) ;
• the macro-strategy is responsible for all other vertices.
• the runtime environment uses the sorting function to order the navigation sets that have been computed.
• the order determines the sequence in which the vertices are to be drawn.
• the "most important" vertex e.g., from the strategy's point of view
• the strategies implement these sorting functions and the runtime environment provides them.
• sortNav(V) is used to sort the set of navigation vertices.
• the sorting functions are called automatically as soon as the functions have returned sets of vertices to the strategy in question. It is consequently necessary that each macro and micro-strategy have a sorting function at its disposal.
• the predicates for the top-down strategy may be defined as follows:
• Visited (v) v. visited
• the vertex's "visited” attribute is set.
• CanNavigate(v) Suggested (v)
• the vertex v is considered done if at least one of the following conditions holds: It includes a learning unit or sub-course that has at its disposal a nonempty set of competences that the learner already possesses; It does not contain a test, is visited, and all of the content graph's starting vertices have been done; and/or
• g is a learning unit
• StartNodesQ function of the chosen micro- strategy will be used. If g is a sub-course, all vertices that do not have any hierarchical relations ; referring to them will be returned.
• the maximum test score and the test score actually attained for the vertex are both set. If the test is passed, the learner competences will be enlarged to include the competences of the graph, and all of the graph's vertices will be set to "visited.”
• sortNav(V) may be defined upon an order relation
• VV l SIT 'i ⁇ lu
• the function sortNav(V) is the sort of the set V in accordance with the order relation ⁇ .
• V V - V postTest : remove all posttests from V .
• V V- V preReq : remove all vertices in V preReq from V .
• L V preTesl : add all pretests into the sorted list. 8.
• L L J V :enlarge the sorted list to include the remaining vertices from V .
• the predicates for this strategy may be the same as those used for the macro- strategy, top-down.
• g is undefined, the vertex doesn't have a content graph and the set is empty. If g is a learning unit, then the StartNodesQ function of the chosen micro-strategy will be used. If g is a sub-course, then all vertices that do not have any hierarchical relations referring to them will be returned.
• the vertex contains a learning unit and one of the hierarchically subordinate vertices has not yet been visited, enlarge the set to include the learning unit's starting vertex using the micro-strategy "orientation only.” Otherwise, enlarge the set to include all vertices that are starting vertices of the content graph of v .
• the operations and sorting function for the bottom-up strategy are the similar to the macro-strategy top-down and therefore are not repeated.
• Linear macro-strategies represent a special case of the macro-strategies that have already been described.
• the elements of the sorted sets of vertices are offered for navigation sequentially, rather than simultaneously. This linearization may be applied to any combination of macro and micro-strategies.
• micro-strategy may be realized.
• orientation only micro-strategy is described.
• the predicates for the micro-strategies may be defined as follows:
• the vertex's "visited” attribute is set.
• This may be used like Suggested .
• the vertex v is considered done if:
• the maximum test score and the test score actually attained for the vertex are both set.
• the learner competences will be enlarged to include the competences of the graph, and all of the graph's vertices will be set to "visited.”
• micro-strategy orientation only may use a sorting function that is similar to sorting function for the macro-strategy top-down and, therefore is not repeated.
• the operations for the example-oriented micro-strategy are identical to those for the micro-strategy "orientation only,” and, therefore, are not repeated.
• the sorting function for example-oriented is defined as follows:
• L remam TopDown.sortNav(V remain ) : sort the set of remaining vertices using the sorting algorithm from the top-down strategy.
• micro-strategy explanation- oriented are identical to those for the micro-strategy example-oriented, and, therefore are not repeated.
• the sorting function for the explanation-oriented micro-strategy is similar to the sorting function of the micro-strategy example-oriented (the only difference being that explanations, rather than examples, are used to form the two sets).
• the predicates, functions, and operations for the micro-strategy action-oriented are identical to those for the micro-strategy example-oriented, and, therefore are not repeated.
• the sorting function for the action-oriented micro-strategy is similar to the sorting function of the micro-strategy example-oriented (the only difference being that actions, rather than examples, are used to form the two sets).

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• 2003
  • 2003-02-11 WO PCT/EP2003/001337 patent/WO2003069580A2/fr not_active Ceased
  • 2003-02-11 EP EP03702623A patent/EP1474791A1/fr not_active Ceased
  • 2003-02-11 CN CN 03808215 patent/CN1647129A/zh active Pending
  • 2003-02-11 AU AU2003205755A patent/AU2003205755A1/en not_active Abandoned

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