E-learning course structure

E-LEARNING COURSE STRUCTURE

TECHNICAL FIELD

0003The following description relates generally to e-learning and in particular to e- learning course structure.

BACKGROUND

0005Systems and applications for delivering computer-based training (CBT) have existed for many years. However, CBT systems historically have not gained wide acceptance. A problem hindering the reception of CBTs as a means of training workers and learners is the compatibility between systems. A CBT system works as a stand-alone system that is unable to use content designed for use with other CBT systems.

0006Early CBTs also were based on hypermedia systems that statically linked content. User guidance was given by annotating the hyperlinks with descriptive information. The trainee could proceed through learning material by traversing the links embedded in the material. The structure associated with the material was very rigid, and the material could not be easily written, edited, or reused to create additional or new learning material.

0007Newer 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.

0008The special domain models also have many complex rules that must be understood prior to designing a course. As a result, a course is too difficult for most authors to create who have not undergone extensive training in the use of the system. Even authors who receive sufficient training may find the system difficult and frustrating to use. In addition, the resulting courses may be incomprehensible due to incorrect use of the domain model by the authors creating the course. Therefore, for the above and other reasons, new methods and technology are needed to supplement traditional computer based training and instruction. SUMMARY

0009In one general aspect, content associated with an e-learning course may be divided into two or more structural elements that may be interpreted by a browser. The structural elements may be aggregated into two or more levels, where a higher level may refer to any instance of a lower level. The structural elements may be assembled into a course.

0010The lowest level structural element is a knowledge item. The knowledge item illustrates or tests an aspect of a topic. The knowledge item may include one or more of a test, a table, an illustration, a graphic, an animation, an audio clip, and a video clip.

0011Another structural element is a learning unit, which may refer to one or more knowledge items to represent a thematically-coherent unit of information.

0012Another structural element is a sub-course, which may refer to one or more of a knowledge item, a learning unit, and a sub-course.

0013Another structural element is a course, which may refer to one or more of a knowledge item, a learning unit, and a sub-course. The structural elements may be tagged with metadata to search and assemble the structural elements. The metadata may be knowledge of orientation, knowledge of explanation, knowledge of reference, and knowledge of action. The metadata also may be a competency to indicate the performance of a learner that traverses the course.

0014A relation may be used to indicate dependencies between structural elements. The relation may be directional (i.e., true in one direction only) or non-directional. The relation may be subject taxonomic or non-subject taxonomic.

0015The subject taxonomic relation may be hierarchical or associative. The hierarchical relation may be a part/whole relation or an abstraction relation. The associative relation may be one of determines, side-by-side, alternative to, opposite to, precedes, context of, process of, values, means of, and affinity.

0016The non-subject taxonomic relation may be prerequisite of or belongs to.

0017The course and its structural elements do not enforce any sequence of structural elements that the learner may use to traverse the course. In addition, the structural elements may be reused and reassembled to form different courses. A content repository may be configured to store course data and its structural elements. A content player may access the stored course data and assemble the structural elements.

0018Other features and advantages will be apparent from the description, the drawings, and the claims.

0019? DESCRIPTION OF DRAWINGS

0020FIG. 1 is an exemplary content aggregation model.

0021FIG 2 is an example of an ontology of knowledge types.

0022FIG 3 is an example of a course graph for e-learning. FIG. 4 is an example of a sub-course graph for e-learning.

0023FIG. 5 is an example of a learning unit graph for e-learning.

0024FIGS. 6 and 7 are exemplary block diagrams of e-learning systems.

0025FIG. 8 is an example showing v as the vertex that represents the learning unit LU » where v,,v₂ are the vertices. Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

0027E-learning Content Structure

0028The e-learning system and methodology structures content so that the content is reusable and flexible. For example, the content structure allows the creator of a course to reuse existing content to create new or additional courses. In addition, the content structure provides flexible content delivery that may be adapted to the learning styles of different learners.

0029E-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.

0030Starting 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).

0031A 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

0032140. The type of media describes the form of the content that is associated with the knowledge item 140. For example, 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.

0033A knowledge item 140 also may be described by the attribute of knowledge type. For example, 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. For example, 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. Each of the knowledge types are described in further detail below. 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. For example, 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.

0034HTML 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

0035Flash files in a browser. For example, Flash movies using vector and bitmap graphics, animations, transparencies, transitions, MP3 audio files, input forms, and interactions may be used. In addition, Flash allows a pixel-precise positioning of graphical elements to generate impressive and interactive applications for presentation of course material to a learner.

0036Learning 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.

0037Sub-courses 120 may be assembled using other sub-courses 120, learning units 130, and/or knowledge items 140. 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.

0038Courses 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. For example, learning object metadata (LOM) 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.

0039As 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."

0040The four knowledge types (orientation, action, explanation, and reference) may be further divided into a fine grained ontology as shown in Fig. 2. For example, 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.

0041Dependencies 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"). For example, 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).

0042Associative 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."

0043The "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

0044"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."

0045Another type of metadata is competencies. 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.

0046The 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. For example, 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). In addition, an edge 380 expressing the relation "context of has been specified for the learning unit with respect to each of the sub- courses. As a result, the basic concepts explained in the learning unit provide the context for the concepts covered in the three sub-courses.

0047Fig. 4 shows a graph 400 of the sub-course "Knowledge structure" 350 of Fig. 3. In this example, 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). 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".) In addition, 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).

0048Fig. 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)." In addition, attributes 550 of each node are specified in brackets (e.g., the node "Hierarchical relations" includes the attributes "Example" and "Picture").

0049E-learning Strategies

0050The 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. As a result, 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) may select from a number of different learning strategies while taking a course. In turn, the selected learning strategy considers both the requirements of the course structure and the preferences of the learner.

0051In the classical classroom, a teacher determines the learning strategy that is used to leam course material. For example, in this context the learning progression may start with a course orientation, followed by an explanation (with examples), an action, and practice. Using the e-learning system and methods, 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). As result, 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. In addition, authors are not required to apply complex rules or Boolean expressions to domain models thus minimizing the training necessary to use the system. Furthermore, the course material may be easily adapted and reused to edit and create new courses.

0052Macro-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."

0053Goal-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.

0054Micro-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").

0055The 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).

0056In one implementation, no dependencies between macro-strategies and micro- strategies exist. Therefore, any combination of macro and micro-strategies may be used when taking a course. Application of learning strategies to the knowledge structure of a course is described in further detail below.

0057E-learning System

0058As shown in Fig. 6 an e-learning architecture 600 may include a learning station 610 and a learning system 620. 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.

0059The 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.

0060The 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.

0061The 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). In addition, 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.

0062The 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.

0063The 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. For example, 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.

0064The 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.

0065The 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.

0066The learning management system also may include an interface for exchanging information with the administration system. For example, the content player may update the learner account information as the learner progresses through the course material. Course Navigation

0067The 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.

0068As described above, 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).

0069Once the course structure is created, the course player processes the course structure using a strategy to present the material to the learner at the learning portal.

0070To process courses, 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.

0071The 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. By applying the learning strategies 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. In general 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).

00722. A strategy may be informed by an event that some sort of action has been performed on a graph node. For example: navigated(node).

00733. A strategy may provide functions that are used to compute new node sets for a given node. For example: NavigationNodes(node).

00744. A strategy provides an ordering function that turns node sets computed number 3 into ordered lists.

00755. A strategy may decide to alter certain strategy-related node attributes. For example: node. set Visited(true). Note that the last point is used because a strategy does not keep any internal state. Instead, any strategy-related information is stored in graph nodes' attributes allowing strategies to be changed "on the fly" during graph traversal.

0076As described there are sets of 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. As a consequence, 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.

0077In the following examples, these definitions are used: C is the set of all courses.

0078G is a set of graphs.

0079V 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)

0080E is a set of edges (e.g., relations types as used in a mathematical sense). TG = {sc,lu} is the set of graph types such that: sc= sub-course; and lu = learning unit.

0081TC = {sc,lu,co,tst} is the set of content types such that: sc- sub-course; lu = learning unit; co - content; and tst ~ test.

0082(With respect to assigning competences to a learner when passing a test, only 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).

0083TR = {• ■ ■} is the set of all relation types(e.g., as described in the section E- learning content structure). BOOL = {true, false] is the Boolean set with the values true and false .

0084MAC = {• ••} is the set of macro-strategies (e.g., as described in the section E-learning strategies).

0085MIC = {• • •} is the set of micro-strategies (e.g., as described in the section E-learning strategies). COMP = {•••} is the set of all competences.

0086LCOMP c COMP is the set of a learner's competences. TST = {pre, post] is the set of test types, such that: pre = pretest; and post = posttest. A course c = (G_c , g_s , mac, mic) e C may be defined such that:

0087G_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; and mic e MIC is the micro-strategy that has been chosen for navigating the course. Processing of the course begins with the start graph. A graph g = (V , E , t comp. ) e G may be defined such that:

0088V_g is the set of all vertices in g ; E_g c V_g x V_g x 77? is the set of all edges in g ; t_g e TG is the graph type of g ; and comp_g c COMP is the competences of the graph.

0089In the following description the term 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. A vertex v = (vs_v , tc_v , gc_c , tk_v , tt_v , mscore_v , ascore_v ) e V is defined such that: vs_v <= BOOL is the visited status of v ; tc_v e TC is the content type of ; gc_v € G is the content graph of v ; tk_v e TK is the knowledge type of v ; tt_v ε TST is the test type of v ; mscore_v is the maximum possible test score of v ; and ascore_v is the test score actually attained for . An edge or relation type e = (v_s , v_E , tr_e ) e E may be defined such that: v_s e V is the starting vertex of e ; v_ε 6 V is the end vertex of e ; and tr_e e 77? is the relation type of e .

0090A predicate is a mapping p : V -> BOOL that assigns a value b_p e BOOL to each vertex v e V . Therefore: b_p = p(y) .

0091An order is a mapping ørrf : V x F - BOOL that assigns a value 6_ord e BOOL to a pair of vertices v, ,v₂ e J⁷ . Therefore:

0092K_d = rd(v ,v₂) . The mapping sort : V" ,ord → V" is a sorting function from a set of vertices V" to a set of vertices (v, , ^■ • • , v_n ) = V with the order ord , provided that:

0093(^v_{ι >}" *_>^v_« ) ⁼ sort(V" ,ord) such that V v, ,v e V" ord (v_t ,v ) = true for

0094i ≤ j -

0095The following description explains the use of attributes. Attributes are used to define and implement the learning strategies. Let g = {V_g ,E_g ,t_g , comp_g ) e G be a graph with the following attributes: g. nodes = V is the vertices of g ; gtype = t_g is the type of g ; and g.comp = comp_g is the graph's competences. Let 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. visited - vs_v is the visited status of vertex v (initially this value is false ); v. graph = [g = (V_g , E_g , t_g ) e G \ v e V_g } is the graph that contains v ; v.contentType = tc_v is the content type of ; ϊg'e G : tc_v = sc v tc_v = lu v.contentGraph = is the content graph of ;

0096[undef : otherwise v.knowType = tk_vis the knowledge type of ;

0097vlestType = <img file="WO03069578A2_D0001.tif" /> is the test type of v ;

0098[undef : otherwise v.mscore = mscore_v is the maximum possible test score of v (initially this value is 0); v.ascore - ascore_v is the actual test score attained for v (initially this value is -1 Let e = (v_s , v_E , tr_e ) e E be an edge with the following attributes: cstart = v_s is the starting vertex of e ; e.end = v_E is the end point of e ; etype = tr_e is the relation type of e ;

0099An edge's logical direction does not necessarily have to agree with the direction indicated by the course player, because the course player displays an edge in the "read direction." This applies to the following edge, for example, e - (v_s ,v_ε,"is a subset of") .

0100The following explanation refers to the logical direction, in other words, the direction of the edge in the above-described cases is considered to be "rotated." In the following, undirected edges are treated as two edges in opposite directions.

0101Predicates are "dynamic attributes" of vertices. The strategy computes the dynamic attributes for an individual vertex when necessary. The following are examples of predicates:

0102Visited (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.graphtype = lu ), then the micro- strategy is used to compute the predicates. The macro-strategy that is chosen is responsible for determining all other vertices.

0103Functions 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: -

0104V = StartNodes(g) = jy | 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.

0105V = NextNodes(v) = jv | v is a successor of v } is the set of all successor vertices of vertex v .

0106For micro-strategies, the chosen macro-strategy calls the functions as needed. When entering a learning unit the macro-strategy selects the appropriate (selected) micro- strategy.

0107Operations 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 runtime environment calls this operation if the vertex v is a test (v.contentType = tst ) that has been done. MaxScore contains the maximum possible score, ActScore the score actually attained. If a vertex is in a learning unit, which means that v.graphtype = lu , then the micro-strategy computes these operations. The macro-strategy is responsible for all other vertices.

0108The 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) is placed at the start of the list (as the next vertex suggested). The strategies implement these sorting functions and the runtime environment provides them. The following examples of sorting functions may be defined: sortNav(V) is used to sort the set of navigation vertices.

0109The 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.

0110The following description explains the predicates, operations, functions, and sorting functions associated with macro-strategies..^'

0111The following is an example of how a top-down (deductive) learning strategy may be realized.

0112The predicates for the top-down strategy may be defined as follows:

0113Visited (v) : v.visited

0114The vertex's "visited" attribute is set.

0115Suggested (v) : V(v,v,tr) e E such that tr = prerequisite we have: Done(v) = true

0116All of the vertex's prerequisites are satisfied.

0117CanNavigate(v) : Suggested (v)

0118Is used in this example like Suggested .

0119Done(v) : (v.contentType e {sc,lu} A v.contentGraph.comp ≠ 0 c LCOMP) v

0120(v.contentType ≠ tst A v.visited = true A (VV e StartNodes(v.contentGraph) : Done(v) - tn

0121(c.contentType = tst A (v.ascore * 2) ≥ v.mscore) 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

0122It deals with a test and at least half of the maximum score has been attained.

0123The functions for the top-down strategy may be defined as follows: g = undef : 0

0124StartNodes(g) = g.type - lu : c.mic.StartNodes(g) g.type = sc : <img file="WO03069578A2_D0002.tif" /> V(v^* , v, tr) e E : tr ≠ hierarchical)

0125If g is undefined, which means that vertex does not have any content graphs, then the set is empty.

0126If g is a learning unit, the StartNodesQ function of the chosen micro- strategy will be used.

0127If g is a sub-course, all vertices that^'do not have any hierarchical relations referring to them will be returned.

0128NextNodes(v) - jv &lt;= V_{v graph} | 3(v,v,tr) } StartNodes(v.contentGraph)

0129All vertices connected to v by an externally directed relation, plus all vertices that are starting vertices of the content graph of .

0130The operations for top-down may be defined as follows: navigate(v) : v.visited - true

0131The vertex's "visited" attribute is set to true . testDone(v,MaxScore, ActScore) : vmscore - MaxScore,v.ascore = ActScore

0132if

0133[ Done(v) = true : LCOMP = LCOMP v.graph.comp, Vv e v. graph : v.visited = true [Done(v) = false : Vv e v.graph : v.visited = false

0134The maximum test score and the test score actually attained for the vertex are both set.

0135If 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

0136"visited."

0137If the test is not passed, all of the graph's vertices are reset to "not visited." The sorting function sortNav(V) may be defined upon an order relation

0138<: V χ V₂ -» boot on a set of vertices. This requires that the following auxiliary functions be defined: 1. An order relation for vertices with respect to the vertex ID <_ld:VxV →bool ^vι ^<ιd ^v2 ^{:< > v}ι-^z^ < ^V2 ^

01392. A comparison relation for vertices with respect to the vertex ID

0140=:VxV →bool, v, = v₂ :=> v, .id = v₂.id 3. An order relation on the test types and unit types

0141&lt;_lesl : (TC x TST) x (TC x TST) → 600

0142(tst, pre) &lt; (co,undef) &lt; (lu,undef) &lt; (tst, post)

01434. An order relation based on 3. for vertices with respect to the test types and unit types.

0144<_lest-VxV →bool v, <_lest v₂ :<=> (v_vcontentType,v_ytestType) <_tat (v₂.contentType,v₂.testType) 5. A comparison relation for vertices with respect to the test types and unit types

0145=_lest:VxV →bool v, =_tat v₂ :<=> (v_vcontentType,v_vtestType) = (v₂.contentType,v₂.testType)

01466. An order relation on the knowledge types based on one of the micro-strategies (see micro-strategies) &lt;_mιcro:TKxTK →bool 7. An order relation based on 6. on the vertices with respect to the micro- strategies. <img file="WO03069578A2_D0003.tif" />^vι &lt;_m,cr₀^vi <img file="WO03069578A2_D0004.tif" /> v₂.knowType

01478. A comparison relation to the vertices in regard to the knowledge types <img file="WO03069578A2_D0005.tif" />^vι =_m,_cro^v₂^: ^ v_x.knowType = v₂.knowType Using these definitions the function &lt;: V_x x V₂ → bool may be defined as follows: VContentTpe ≠ tst Λ

01483vε<img file="WO03069578A2_D0006.tif" /> £, Λv.contentlype≠ tst ΛV, &lt; VΛ V&lt;V₂]

0149<img file="WO03069578A2_D0007.tif" />

0150V_yContentTjpe ≠ tst Λ Λ V v, < v, :<=> 5v e V4^v_vv,prereq)s. £, v.contentType ≠ tst A V, < v Λ < v₂]

0151^VV1 =,«, ^V2^ΛVl &lt;, _OT^V2

0152'l -ua ^V2 ^{Λ V}l ⁼™&lt;M ^V2 ' "1 "2 if &amp; = Si, t_t = i

0153Ξv = (v5,/₁,g,,tA,tt,»2i:,αs) e F₂ : (v,v₂,fr) e£₂Λfrε {prereq, hierarchical) jf g, ≠ g₂, /, = /«, e₂ ≠ /a false otherwise

0154Note, if g_}=g₂, then it is obvious thatFJ =V₂,E = E₂, t_x =t₂ and comp_λ = comp₂. In addition, in case 3, a situation is maintained in which no direct relation between the vertices exists, but there does exist a relation to the higher-order vertex. The order relation will then also apply to all of the vertices in this vertex's content graph. This situation is depicted in Fig.8, where v is the vertex that represents the learning unit and v, , v₂ are the vertices under consideration.

0155The function sortNav(V) is the sort of the set V in accordance with the order relation &lt;.

0156The following process is one method of implementing the function sortNav(V) :

01571. V_preTesl = {v € V I v.contentType = tst A v.testType = pre] : the set of all pretests.

01582. v = V-V p.reTest : remove all pretests from V .

01593. V_postTesl = {v e V I v.contentType = tst Λ vlestType = post): the set of all posttests.

01604. V = V - V_poslTesl : remove all posttests from V .

01615. V_preReq = jv &lt;= V I Ξ(v,v,tr) e E :tr = prerequisite! : the set of all vertices that have a prerequisite relation directed toward them.

01626. V = V- V_preKeq : remove all vertices in V_preKeq from V .

01637. L = V p,reTest : add all pretests into the sorted list. 8. L = L {v e V \ v.contentType = cό), V = V - L .-enlarge the sorted list to include all vertices that have a learning unit and then remove these vertices from V .

01649. L = L {v € V I v.contentType = lu}, V - V - L :enlarge the sorted list to include all vertices that contain a learning unit and then remove these vertices from V .

016510. L = L J V : enlarge the sorted list to include the remaining vertices from V .

016611. Search for all vertices in v e V _λXtq : ^' the vertex v' e i such that (v ,v, prerequisite) e E A dist(v^*) = MAX (the vertex that is located farthest back in L and that possesses a prerequisite relation to v). Add v into L behind v^* .

016712. L = L J V_postTest :enlarge the sorted list to include all posttests.

016813. Return the sorted list L as the result. The subsets determined in steps 7 - 12 are themselves sorted by the order relation

0169&lt;* ^•

0170The following is an example of how a bottom-up (Inductive) learning strategy may be implemented.

0171The predicates for this strategy may be the same as those used for the macro- strategy, top-down. The functions for bottom-up may be defined as follows: g = undef : 0

0172StartNodes(g) = g.type = lu : cmic.StartNodes(g) g.type = sc : jv e V_gs | V(v^*,v,tr) &lt;= E : tr ≠ hierarchical

0173If 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.

0174NextNodes(v) = (v e V_{v graph} \ a(v,v,tr)j <img file="WO03069578A2_D0008.tif" />

0175All vertices that are connected to v by an externally directed relation.

0176If 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 .

0177The operations and sorting function for the bottom-up strategy are the similar to the macro-strategy top-down and therefore are not repeated.

0178Linear macro-strategies represent a special case of the macro-strategies that have already been described. In linear macro-strategies, 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.

0179The following description includes examples of how a micro-strategy may be realized. In this example, an orientation only micro-strategy is described. The predicates for the micro-strategies may be defined as follows:

0180Visited (v) : v.visited

0181The vertex's "visited" attribute is set.

0182Suggested(v) : V(v,v,tr) e E such that tr = prerequisite we have: Doneiy) = true All of the vertex's prerequisites are already satisfied.

0183CanNavigate(v) : Suggested (v) This may be used like Suggested . Done(v) :

0184(v.contentType ≠ tst A v.visited = true) v (ccontentType = tst A (v.ascore * 2) ≥ v.mscore)

0185The vertex v is considered done if:

0186It does not contain a test and has already been visited. It deals with a test and at least half of the maximum score has been attained. The functions may be defined as follows: StartNodes(g) ~ e V_g \ vknowType = <img file="WO03069578A2_D0009.tif" />

0187(v e V_g I Ξ(v, v, tr) e E : tr = prereq A vknowType = Orientation

0188The set of all vertices with knowledge type orientation, plus all vertices that have a prerequisite relation to a vertex with knowledge type orientation.

0189NextNodes(v) = 0

0190For this micro-strategy, this is always the empty set. In other words, no successor vertices exist because all relevant vertices are contained in the set of starting vertices. The operations may be defined as follows: navigate(v) : v.visited = true The vertex's "visited" attribute is set to true . testDone(v,MaxScore, ActScore) : vmscore = MaxScore,v.ascore = ActScore if

0191(Done(v) = true : LCOMP = LCOMP v.graph.comp, /v e v. graph : v.visited = true Done(v) = false : Vv e v.graph : v.visited = false

0192The maximum test score and the test score actually attained for the vertex are both set.

0193If 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."

0194If the test is not passed, all of the graph's vertices are reset to "not visited."

0195The 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.

0196The following is an example of the implementation of an example oriented micro- strategy. The predicates for this strategy are identical to those for the micro-strategy orientation only and are not repeated.

0197The functions may be defined as follows:

0198StartNodes(g) = V_g

0199All vertices that are contained in the learning unit. NextNodes(v) = 0

0200For this micro-strategy, this is always the empty set. In other words, no successor vertices exist because all relevant vertices are contained in the set of starting vertices.

0201The 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:

0202le

0203<img file="WO03069578A2_D0010.tif" />

0204Steps for executing sortNav(V) :

02051.. V_a = {v € V I v. knowType - Example) u jv G V I 3(v,v,tr) € E : tr = prereq A vknowType = Example): the set of all vertices that contain examples, plus the prerequisites of these vertices. ²- K_emcn = ^v ~ ^v_amp '■ ^the remaining vertices from V .

02063. L^ = TopDown.sortNav(V_examp) : sort the set of examples using the sorting algorithm from the top-down strategy.

02074. L_remaιn = TopDown.sortNav(V_remam) : sort the set of remaining vertices using the sorting algorithm from the top-down strategy.

02085. L = L_aamp L_rema : form the union of the two sorted lists.

02096. Return the sorted list Z, as the result.

0210The predicates, functions, and operations for the 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).

0211The 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).

0212A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, advantageous results may be achieved if the steps of the disclosed techniques are performed in a different order and/or if components in a disclosed system, architecture, device, or circuit are combined in a different manner and/or replaced or supplemented by other components. Accordingly, other implementations are within the scope of the following claims.