Learning objects as a uniform foundation for e ... - Semantic Scholar

Learning Objects as a Uniform Foundation for E- Learning Platforms∗ Gottfried Vossen University of Münster D-48149 Münster, Germany [email protected]

Abstract Learning objects are under discussion in the context of electronic and in particular Web-based learning. Intuitively, they represent reusable units of learning content that can be consumed or studied within a single learning session, and that can be sequenced into larger units such as classes and courses if necessary or desired. To achieve these goals, making learning objects amenable to the technical support of a database system appears appropriate. Once this view is in place, learning objects can be used as the central and uniform foundation of an e-learning platform that, for example, is capable of tracking users or of adapting content to users, and that can simultaneously handle a companys knowledge management. It is shown in this paper how the latter can be achieved through a common exploitation of (and interaction between) database objects and suitably designed processes.

1 Introduction Learning objects are under discussion in the context of electronic and in particular Web-based learning [1]. Intuitively, they represent reusable units of learning content that can be consumed or studied within a single learning session or within a predefined, finite amount of time, and that can be sequenced into larger units such as classes and courses if necessary or desired. These goals have previously been made precise by various people, including [5, 7] and others. In [16], we have described the initial version of an object model which makes learning objects amenable to the technical support of a database system. Beyond this, we will show in this paper that learning objects can be used as the central and uniform foundation of an e-learning platform. Indeed, they can form the basis, for example, of tracking ∗ This work was done while both authors were affiliated with PROMATIS in Germany.

Peter Jaeschke Credit Suisse Group CH-8070 Zurich, Switzerland

users or of adapting content to users, and they can simultaneously become a link into a companys knowledge management system. We show how this central role of learning objects can be accomplished by a suitably chosen interaction of database learning objects and processes that use and manipulate these objects. Various communities have recently entered into a discussion of learning objects, and an agreement on what a learning object is about seems in sight. Indeed, according to Eduworks (see www.eduworks.com/LOTT/tutorial/), “learning objects are the core concept in an approach to learning content in which content is broken down into bite size chunks. These chunks can be reused, independently created and maintained, and pulled apart and stuck together like so many legos.” Similar definitions are given by other people; see, for example, [2, 5, 7]. Moreover, researchers and developers of e-learning platforms seem to have come to a general agreement that learning objects are the piece of information that has to be handled by a learning management system (LMS). On the other hand, what exactly constitutes a learning object in practice is not clear at all; even standardization bodies such as IEEE, IMS, and SCORM remain vague on what really constitutes the inside of a learning object and delve more into general aspects such as who authored it or what are relevant keywords, and into the packaging of several such objects from potentially distinct sources into larger units of learning. We have tried to lay the foundations for a more technical view of learning objects in [16]. Our view is derived from the way objects are handled in a database. If learning objects are stored in relational tables or in object-oriented class extensions, they need a schema as well as attributes which have (simple or complex) types. A schema specifies the content structure of an object that is stored in the underlying database, and hence serves as a time-invariant conceptual description of what a database object is comprised of. Besides being mandatory from a storage point of view, viewing learning objects as database objects brings

Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS’03) 1098-8068/03 $17.00 © 2003 IEEE

along several advantages, among them that we can now exploit database techniques for handling learning objects; in particular, SQL can be used for querying, updating, and managing them. Second, it is easy to devise interfaces for authoring learning objects, since transporting data into and out of a (potentially even Web-based) form can be realized as database insertion or retrieval, resp. Third, databases nicely interact with other systems, an experience we have confirmed when developing our XLX e-learning system for doing exercises over the Web [10]. For example, data stored in a, say, relational database can easily be published in XML format, where the particular XML documents produced can follow a generic structuring (i.e., one driven by the table constituents in the database) or they can be created according to an XML Schema (that in turn may be derived from the underlying database schema). The relevance of this aspect stems from the fact that XML is generally accepted as a data exchange format these days, and XML is also employed by the standards bodies mentioned above for learning object exchanges. Once such a database-oriented notion of a learning object has been established, the various expectations an LMS developer or deployer may have about learning objects can be materialized. Indeed, learning objects taken from a database can easily form the units underlying a content authoring system. Moreover, as individual learning objects get combined into larger units such as classes, maps can be attached to the larger granules that enable content adaptation at the level of individual objects. Finally, user tracking (in a positive as well as in a negative sense) can be based on learning objects, thereby retaining an appropriate level of tracking detail. Other uses can be designed for learning objects, thereby making them the central construct underlying a learning platform. These ideas are worked out in the rest of this paper, which is organized as follows: In Section 2, we sketch the database model of learning objects we have developed and briefly describe other object model components (e.g., learners and their profiles) relevant for what follows; we also discuss the authoring of learning objects. In Section 3 we elaborate on the access to and management of learning objects, which is described using Petri nets as process specification formalism. In Section 4 we present some conclusions and directions for future work.

2 A Database Model for Learning Objects Learning objects are essentially units of content to be studied by people through an electronic platform. We assume that, in the context of such a platform, learners are the “customers” who consume the content, and that authors are those who produce the learning objects; other roles (such as teachers, trainers, or administrators) are not relevant

for the purposes of this paper. Regarding content, training and learning offerings are supposed to come in reusable granules that can be authored independently of a delivery medium and accessed dynamically, e.g., over the Web. To this end, it is crucial to design and develop learning objects in such a way that a certain validity period or lifetime as well as (re-) usability in a variety of contexts are achieved. This section presents our learning object and learner model. Overview The model of learning objects whose initial version we have described in [16] is intended to accommodate a variety of learning object instances whose content can vary considerably in size, volume, and presentation, i.e., from the short note that informs about a product update in written form, to the comprehensive lecture that introduces the learner to a, say, technical topic in a self-contained way. Our model is based on the following design decisions: 1. We derive structural aspects of a learning object (i.e., its attributes) from an analysis of typical classes or courses as well as from the content of our prime learning targets, and from the notion of an object commonly used in object-relational databases. 2. We make the resulting object model amenable to knowledge management (by introducing knowledge objects as generalizations of learning objects). 3. Besides this, administrative information is gathered in such a way that various learning object exploitations become possible (as described below). In addition to the above, learning objects should conform to relevant standards; we are using portions of the Learning Object Metadata (LOM) recommendation from IEEE as described in [11] as well as meta attributes as suggested in Dublin Core (see Figure 1; here the intention is that a learning object comes with either set of meta-attributes, not necessarily with both sets). Moreover, we have cast the result into a class diagram which is straightforwardly amenable to a database (or XML) schema representation. We emphasize that we are not looking into presentation aspects of a learning object here (i.e., questions such as which parts of a learning object get animated and what media are appropriate for that purpose), but only into structural aspects. The object model we have designed for learning objects is shown in Figure 1. A learning object has an orientation section in the beginning, followed by a learning section where the actual content is conveyed to the learners, and concluded by a resources section which comprises hints for further study. The center portion of this decomposition has again an inner structure: Learning some material or content


Figure 1. The Learning Object Class Lattice. is typically a combination of study, practice, and undergoing a review. In more detail, an orientation section comprises an executive summary, prerequisites stating what the learner is expected to know prior to “entering” this learning object, a description of the context in which this particular learning object occurs or should be used, learning goals that are associated with this learning object, passing requirements that state what the learner has to do to get credit for studying this learning object, and a table of contents. In a similar way, a study section has components such as introduction, definition, facts, example, diagram, explanation, or summary; a practice section can comprise a case study, simulation, or exercises; and a review section may contain a recapitulation, quiz, test, assessment, or next steps. Finally, a resources section contains links, documents, a glossary,

and an index. As shown in Figure 1, a decomposition like this can directly be transferred to a class diagram, where the various content pieces are represented as attributes. Technically, these attributes will have types such as text or image so that content can adequately be represented. Figure 1 also shows the LOM and the DCMI attributes that we consider part of the head of a learning object. Finally, a learning object may further be characterized by global information, such as prerequisites, i.e., a brief statement of is expected from the learner when studying this object, or an objective of what the learner is supposed to learn here. As a consequence of this way of modeling learning objects, there is considerable flexibility in their inner structure, which can be exploited to model a large variety of learning


items and learning situations, e.g., the object that resembles a lecture in class, consisting of definitions, examples, scenario explanations, and summarizing comments; the object that explains a certain process model (in one of several forms: through an animated slide show or a movie showing a simulation, through a textual explanation of the various activities and object stores occurring in the model); the situation where a sales person needs to read a short note or be instructed about a special offer of his company; the object comprising a strategy exposition for a company; the object containing a legal text or regulation that needs to be brought to an employees attention. Figure 1 also indicates how we are trying to come across an integration of knowledge management and learning: A learning object is perceived as a specialization of a knowledge object. The idea behind this is that any user of the learning system at large could easily create a knowledge object (and other users can contribute to a knowledge object already created); in order to motivate people to do this, creation should be vastly unrestricted (e.g., not confined to editing certain attributes). However, it could happen after a while that such an object has accumulated so much or so important content that it is turned (specialized) into a learning object. Therefore, a knowledge object references an “author,” i.e., the person responsible for its content; this person ideally retains this role even after a specialization has occurred. Clearly, this may be considered preliminary, yet it should be seen as an attempt to integrate knowledge management and learning at a technical level. Usage of Learning Objects Regarding the usage of learning objects, we imagine that they are consumed by learners (individually or in chunks called classes) and that they are created by authors, both under the supervision of some management system. When learning objects are grouped together and sequenced to form a class, this class will have an associated class map the learner can use as a navigation aid; in particular, a class map constitutes a partial order among the learning objects involved. Additionally, we use a person class as a generalization of the roles of a learner as well as of an author (and possibly others). A specific feature of a learner is the associated profile as well as the learning allowance. The profile, among other things, is intended to keep track of a learning history, i.e., what a learner has been doing. Finally, the learning allowance records the time a class has consumed and counts backwards in order to determine when a “time account” is used up; notice that a learning allowance resembles the (monetary or time) accounts under discussion for learning in various countries at the moment. We will return to this issue in Section 3 (cf. Figure 2). As mentioned, the map associated with a class can be

thought of as a partial order (or directed acyclic graph) of the learning objects making up that class; in particular, there is a starting point, i.e., a learning object that has to be accessed first, and thereafter there may be parallel branches within the class content for which the exact order in which a learner works through the respective objects is immaterial. Finally, there are no loops in a class map, so what a learner actually will have done in the end is to have consumed a topological sort of the class map, i.e., one of possibly several linear orders of learning objects derived from the class map. Clearly, the underlying system should allow a learner to redo individual learning objects on such a map if necessary. Class maps give rise to a straightforward navigation mechanism: Since individual learning objects may occur in more than one class a learner has to attend, we can avoid that he or she has to do the same object over and over by deriving an active class map from the generic one associated with a class; the active class map is personalized towards the learner and takes his learning history (recorded in the profile) into account. Now that we have established a basic technical understanding of learning objects as used in this paper, we can look at their various exploitations in the context of a learning system. We devote the remainder of this section to the “easy” part of authoring learning objects. Authoring Learning Objects Learning objects as introduced above will typically form the basis of a variety of learning assignments; for the latter, we see at least three relevant types: • Individual learning objects should be appropriate, for example, for individual topics that can be extracted from a larger context (e.g., basics of EntityRelationship modeling), and for which it can be specified precisely where their usage would be appropriate. • A class could be comprised of a collection or sequence of learning objects according to a class map, and would roughly correspond to a class in a university that extends over several sessions or class meetings (appropriate, for example, for the novice learner). • Finally, a course program comprised of several classes (such as one offered in a virtual university) would need a larger collection of learning objects, potentially from a variety of sources and grouped into several classes. Clearly, the task of authoring content is simplified if a certain structuring of the target learning object has been prescribed. To this end, a learning object structured as shown in Figure 1 is easily cast into a number of forms an author can fill out. Indeed, for a particular learning object under design, the first decision to be made is which attribute is


needed and which is not. For the ones that are left, content can be either created from scratch, imported from a foreign source, or even generated on the basis of another system. We mention that we are developing a prototypical system that makes use of the latter approach; our system is intended to support an easy creation of learning objects that teach process models (as well as various other subjects). We also mention that we consider learning objects as being versioned, i.e., updates to an existing learning object are not made in place, but lead to another version. In this way, the participation of a learning object (or different versions of it) in distinct classes becomes manageable. We are even considering to attach a lifetime to a learning object so that it can no longer be used once its lifetime has expired. Finally, we note again that, according to Figure 1, learning objects are specializations of knowledge objects in our approach. Since we let knowledge objects be simpler in structure than learning objects (actually freely chosen by its creator), it is easier to create a knowledge object than a learning object, a fact that should motivate users to participate in knowledge creation and conservation. Moreover, as more and more content gets accumulated in a knowledge object, a course designer may decide that it is now appropriate to derive a learning object from that particular knowledge object. In that case, that way to accomplish this would be to create a corresponding specialization and then enter into an authoring process.

3 Accessing and Managing Learning Objects In this section, we look at learning objects from two other perspectives, that of a learner who is the consumer of such objects and that of an administrator (or a teacher or even the underlying LMS) responsible for information derivation and management pertinent to learning objects. To this end, we particularly look into the aspect of user profiling as well as tracking, which we perceive as processes that are modeled using the Petri net formalism. Profiling Learners A user profile is typically established and maintained by an LMS for several purposes, including content adaptation, content presentation, and progress monitoring. Indeed, content to be worked on by a learner is ideally adapted by an LMS to the learners prior knowledge, his or her preferences, standing, learning style and speed, as well as to the overall study program he or she has been assigned or chosen [4, 6, 9]. For example, a learner may take several classes on a similar subject of increasing difficulty (e.g.,“Introduction to Process Modeling,” “Advanced Process Modeling,” etc.); it may then happen that initial parts of both classes overlap, so that the learner may get bored if the classes are taken

shortly after one another and the learner can indeed experience the redundancy involved. If the class is properly organized into learning objects, user adaptation could make sure that if such an object occurs in either class, the learner can skip over it when the object is encountered the second time, provided he or she has successfully mastered it in the first place. From the content perspective, we are able to provide this using class maps as described earlier. From the learner perspective, we take such considerations into account by associating a learner profile with every learner as shown in Figure 2. This figure shows an excerpt from the learner data model we have designed (and also references the learning object class explained above as a class specified elsewhere). A learner profile comprises goals, a learning history, information about a learner’s performance, and preferences that drive content selection and presentation. Importantly, a learning history is given by the learning objects he or she has successfully completed. Since learning objects may be repeated throughout various courses, it makes sense to associate them to a profile. The profile is continuously updated as a learner works through various assignments. The learner profile contains an attribute called Learning Allowance (already mentioned in Section 2) which represents an amount of hours a learner has available to spend freely on learning material. It has to be set to an initial value as part of the initialization of a learner profile, and it is updated whenever a class is completed. Updating means that the current allowance is reduced by the class duration. Three aspects are worth noting here: • The allowance is currently connected to classes only, not to course programs, and also not to individual learning objects. In a more refined scheme, this could be extended. • The current approach is to recalculate an allowance based on class durations only, which is an estimated time that simply adds up the durations of the learning objects comprising the class. Thus, a “slow” learner, taking more time than estimated, is treated fair by this approach, while the “fast” learner, taking less time than estimated, does not really benefit from it. This could also be changed in the future. • The learning allowance could ultimately be transformed into a “pricing scheme” where a learner is charged for the delivery of an individual learning object or an entire class content (or for taking an exam) and, beyond that, become part of a business model for e-learning. As a consequence, we consider awards a learner can earn for the successful completion of an assignment as having an


Learner Learner Profile Level Learning History Learner Performance Learner Preference Goals Learning Allowance

Learning History Date Learning Object Action

Personal Desktop

Start Date Status To Do List Learner Profile Personal Desktop Last Login

Learner Performance Date Item Grade

Links Belongs_To Notepad Desktop

Learner Preferences

Notepad

Presentation Method Style

What Notes

Learning Object

Figure 2. Excerpt from the Learner Data Model.

effect on a learners allowance, which is reflected, for example, in our approach to user tracking. The Learning History can technically be as simple as a table representing what a learner has been doing to a learning object, and when he or she has been doing it. From the values of the attributes included here it is possible to calculate the time a learner has needed to complete this class. The Learner Performance represents a possibility to record a learner’s performance, not in terms of what he or she has been doing (that appears in the Learner History), but how he or she has been doing it. Thus, entries here carry grading information. An extension would be to enable the calculation of GPA (grade point average) values reflecting an average taken over the results of several assignments, or to calculate degrees resulting from the successful completion of a course program in combination with a certain grade. Entries in the Learner Preferences class represent preferences a learner might have with respect to his or her learning style and the presentation of learning material (see above). For example, some learners prefer written text (to be read) over spoken text (to be listened to), or still images over animated ones. Clearly, an appropriate selection of attributes (and their allowed values) must be guided by expertise from

learning theories, which goes beyond the scope of this paper. Petri Nets For describing our next exploitations of learning objects, we need a model to describe processes. To this end, we use this modeling approach for the specification of relevant processes. For the sake of completeness, we briefly recall some Petri net basics next; further details can be found, for example, in [14]. Petri nets are a graphical formalism for the description of processes and allow for a specification of sequential or concurrent activities that may or may not exclude each other. The crucial feature is that only two graphical constructs are needed: Circles are used to represent static aspects, e.g., states, documents, object stores; rectangles are used to represent events or activities. Moreover, circles and rectangles are connected by arcs in such way that the resulting graph is bipartite, i.e., states and activities occur in a strictly alternating manner. More formally, a net is a triple N = (S, T, F ) s.t. S (“states”) and T (“transitions”) are non-empty, disjoint, and finite sets, and F ⊆ (S × T ) ∪ (T × S) repre-


s2

s5 t2 s3

s1

s6

t1 t3

s7

s4

Figure 3. A sample Petri net.

sents the flow relation (or the arcs). As a simple example, Figure 3 shows a Petri net N = (S, T, F ) where S = {s1 , s2 , s3 , s4 , s5 , s6 , s7 }, T = {t1 , t2 , t3 }, and F = {(s1 , t1 ), (t1 , s2 ), (t1 , s3 ), (s2 , t2 ), (t2 , s5 ), (s3 , t3 ), (s4 , t3 ), (t3 , s6 ), (t3 , s7 )}. Petri nets are widely used in a variety of applications, which is mainly due to their adaptability and flexibility as well as to the fact that they can be given a precise semantics which, for example, allows testing various properties such as deadlock-freedom or liveliness. Depending on the particular interpretation given to the circles (e.g., channel, condition, place, predicate), various net classes can be established with distinct expressive power. This aspect together with the fact that they can be refined in a top-down fashion (where rectangles are replaced by entire “sub-nets”) and that they can be described in a strictly mathematical fashion (using techniques from linear algebra) makes them amenable to process modeling, as has been described, for example, in [15, 13]. In the context of process modeling, Petri nets informally come with the interpretation that rectangles are activities occurring in a process, and circles are object stores that provide input to an activity, get manipulated by the activity, and also represent output created by an activity. Clearly, in order to make this amenable to the modeling of processes representing workflows, a variety of additional aspects need to be covered. For example, each activity needs to be associated with one or more roles that are played by available resources and beyond that maybe with costs, times, or ratings, while each object store has associated object types or classes representing the entities that are relevant in the particular context. To make the latter precise, an underlying object model is needed which captures, at a conceptual level, the ingredients and properties of objects that are relevant to the process being modeled. Using Petri Nets to Describe Learning Sessions We now give several examples of how to describe processes relevant to e-learning using Petri nets; various other exam-

ples can be found in [17]. One exploitation of what has been described above regarding learner profiles appears in our vision of a learning session as shown in Figure 4. There is a basic distinction between a novice learner and an advanced learner. Upon login, the novice learner is first tested regarding prior knowledge; the result of this test is recorded in the previously initialized learner profile. The advanced learner is a person with prior learning experience, and for which the initial test is not necessary. Both types of learners get learning assignments which consist of course programs, classes, or learning objects. Once the learner starts working on the assignment, he or she enters a cycle and within that cycle essentially the “learning” sub-process. When the learner considers the assignment finished, he or she has to provide feedback on the assignment itself. Finally, the learner profile is updated in order to record what has been done and what the result was. Notice that the result itself is obtained inside the learning sub-process (which is not shown here), as each learning object has a review section that may eventually contain a test. Notice that the object of study ultimately is the individual learning object, several of which could be accessed consecutively within one session. Moreover, the profile of a learner is updated upon completion of a learning assignment and the subsequent test, and keeps track of the learner’s abilities and knowledge, yet all this information is broken down to the granularity of the individual learning object. Tracking Learners When a new tracking record is created and sent to the LMS, the system tests whether a class or a learning assignment is now successfully finished; this and further details of learner tracking are shown in Figure 5. If no assignment is complete, the learner simply continues, and the system continues to monitor her or him. However, if the former is the case, the system will check the degree obtained for the assignment against a predefined skill map. The point is that a new tracking record is created every time a learner starts working on a new learning object, and that record is updated once the learner finishes the learning object. We mention that if the assignment under consideration has been a course program, the test just described will be made every time a class from that program is finished. If the learning assignment just completed has been a class, the first thing to do is update the learning allowance, i.e., to subtract the class duration from the learning allowance associated with a learner in his or her profile. The next goal is to automatically match the degree earned as a result of completing an assignment; after that it can be tested whether the degree obtained matches a skill. For the time being, this test is simple, as a class is considered as a sequence of learning objects, and it is easy to see from the tracking records col-


Learner

Determine learner type

Novice Learner

XOR

Reward Do initial test Advanced Learner

Get first/next

XOR

Result

work assignment

Decide on reward Update profile

no

Course Program

Continue session?

XOR Select class

Updated track record

Provide feedback

Feedback

Class

Select LO

Learning Object

Update tracking info

LO finished

Learning New track record

Initialize tracking info

Figure 4. A Learning Session.

lected whether or not that sequence is complete. Similarly, a course program is a sequence of classes, and again it is easily tested whether that sequence is now complete. Notice that the situation is more complicated (although computationally not much harder) when classes are partial orders of learning objects, and course programs are partial orders of classes.

We conclude this section by mentioning that content selection and presentation is also based on user profiles and on the fact that the core granularity is that of a learning object. Indeed, we plan to give users an ability to subscribe to certain content based on their personal interests. Moreover, presentations are created for individual learning objects and are driven by the preferences, styles and standing recorded in a learners profile.

Putting the Pieces Together We now put the various pieces we have designed together. To this end, Figure 6 summarizes the various aspects we have been considering and for which learning objects are the fundamental paradigm: • Content creation is done based on (knowledge objects and on) learning objects, where the former can be turned into the latter, and where the latter are essentially based on predefined “patterns” that drive their authoring. Learning objects can be sequenced into classes, where the ordering can be a total or a partial one. • Content consumption (learning) is done based on course programs consisting of classes, which in turn consist of learning objects, so the ultimate study item


Tracking record Assignment completed? XOR

yes

yes

Class completed?

Update learning allowance

XOR

no

Updated allowance

no

Retrieve skill info No match

Match skill vs. degree

Skill info retrieved

XOR

Determine performance Learner performance

Match found

Update skill

Learner profile

Decide on reward

No reward granted

XOR

Reward granted

Figure 5. Tracking Learners.

is the individual learning object. For the purposes of learning, objects are given a presentation tailored towards a learners preferences and abilities.

Authoring System Ext. Input

• User profiling and tracking is also attached to the individual learning object, as each such object carries all the information relevant to a user profile, including test results and learning times. Clearly, learning objects and the system that manages them also need to be interfaced with systems or sources from which it is possible to derive or import content.

4 Conclusions and Future Work In this paper we have tried to advocate the role of learning objects as a central and fundamental paradigm underlying an e-learning system. We have started out from a database perspective of learning objects, which materializes in a well-defined structure with mandatory and optional attributes that leads to a class-based, object-model representation of what has for a long time already been called a learning object. We have then indicated how to surround this object model with other database objects representing further LMS ingredients, in particular learners and their profiles. Finally, we have shown how various LMS activities, both of learning nature and of administrative nature, can be

Learning Environment

Authoring of Content

Learning Session

Sequencing, Presentation

Learning Object

User Profiling

User Tracking

Learning Management System (LMS)

Figure 6. Overview of Learning Object Exploitations.

founded upon this object-based approach. On the side, we have indicated how learning objects can also be related to knowledge objects, by considering the former a specialization of the latter. Future work could be pursued along a variety of lines, some of which are indicated next: Course evaluation: As is common nowadays for every course offering, be it related to learning or not, an evaluation cycle may be a reasonable issue to be added. Up


to now we foresee the collection of feedback from learners in our system, and the use of collected feedback when learning objects are updated, yet there is no special evaluation mechanism that looks, for example, for issues such as the following: How well was a particular learning object or class received? How well are the authors currently involved in content creation? What are good external sources? How does learner performance relate to the evaluation learners have given a course? Learner evaluation: Once a learner has finished an assignment, he or she could undergo a critical performance evaluation. For example, from the learning history the sequence of learning objects worked upon can be derived, and it could be tested whether the route taken through these objects is indeed conformant to the (active) class map for the class under consideration. Moreover, the learning speed could be calculated, and from route and speed a classification of the learner could be derived. Calendaring: The environment presented to a learner could integrate a calendaring system through which a learner can arrange his or her assigned learning objects over time, and then be reminded of an upcoming learning session. Personalization: A learning environment could be personalized to some extent, and the learner could be given some freedom in that respect. So far it has only been specified that a learner should be able to create and manipulate a personal desktop including a “notepad,” but clearly much more could be done in terms of personalization. To this end, user profiling techniques as used in Web site management for the personalization of content may be useful for a learning system [3, 8]. By the same token, recommender systems which select items that could be of interest to a particular user could be employed for learning situations; see [12] for an evaluation of corresponding algorithms. We mention that we are investigating in a separate project to what extend the processes and components of an e-learning system can be perceived and be made available as Web services; details can be found in [18].

References [1] Adelsberger, H.H., B. Collis, J.M. Pawlowski, eds. (2002). Handbook on Information Technologies for Education and Training. Springer-Verlag, Berlin. [2] Barritt, C. (2001). CISCO Systems Reusable Learning Object Strategy Designing Information and Learning Objects Through Concept, Fact, Procedure, Process, and Principle Templates, Version 4.0. White Paper, CISCO Systems, Inc., November 2001. [3] Bradley, K., Rafter, R., Smyth, B.: Case-Based User Profiling for Content Personalisation. Proc. International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, Trento, Italy, 2000.

[4] De Bra, P., Calvi, L.: AHA! An open Adaptive Hypermedia Architecture. The New Review of Hypermedia and Multimedia 4, Taylor Graham Publishers, 1998, pp. 115-119. [5] Downes, S.: Learning Objects: Resources for Distances Education Worldwide. International Review of Research in Open and Distance Learning 2 (1) 2000. [6] El Saddik, A., Fischer, S., Steinmetz, R.: Reusability and Adaptability of Interactive Resources in Web-Based Educational Systems. ACM Journal of Educational Resources in Computing 1 (1) 2001. [7] Fischer, S. (2001). Course and Exercise Sequencing Using Metadata in Adaptive Hypermedia Learning Systems. ACM Journal of Educational Resources in Computing, 1(1), Spring 2001. [8] Fink, J., Kobsa, A.: A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web. User Modeling and User-Adapted Interaction 10, 2000, pp. 209-249. [9] Henze, N., Nejdl, W.: Adaptivity in the KBS Hyperbook System. Proc. 2nd Workshop on Adaptive Systems and User Modeling on the WWW 1999. [10] Hüsemann, B., Lechtenbörger, J., Vossen, G., Westerkamp, P.: XLX - A Platform for Graduate-Level Exercises. Proc. International Conference on Computers in Education, Auckland, New Zealand, December 2002, pp. 1262-1266. [11] IEEE: Draft Standard for Learning Object Metadata. IEEE Standards Department Publication P1484.12.1/D6.4, March 2002. [12] Karypis, G.: Evaluation of Item-Based Top-N Recommendation Algorithms. Proc. 10th ACM International Conference on Information and Knowledge Management (CIKM) 2001. [13] Oberweis, A., Sander, P., Stucky, W.: Petri net based modelling of procedures in complex object database applications, Proc. 17th IEEE Annual International Computer Software and Applications Conference (COMPSAC), Phoenix/Arizona 1993, pp. 138-144. [14] Reisig, W.: Petri Nets, An Introduction. EATCS, Monographs on Theoretical Computer Science, W. Brauer, G. Rozenberg, A. Salomaa (Eds.), Springer Verlag, Berlin, 1985. [15] Van der Aalst, W., Desel, J., Oberweis, A., eds.: Business Process Management - Models, Techniques and Empirical Studies, Lecture Notes in Computer Science Vol. 1806, Springer-Verlag, Berlin, 2000. [16] Vossen, G., Jaeschke, P.: Towards a Uniform and Flexible Data Model for Learning Objects. Proc. 30th Annual Conference of the International Business School Computing Association (IBSCA), Savannah, Georgia, 2002, pp. 99-129. [17] Vossen, G., Jaeschke, P., Oberweis, A.: Flexible Workflow Management as a Central E-Learning Support Paradigm. Proc. 1st European Conference on E-Learning (ECEL), Uxbridge, UK, 2002, pp. 253-267. [18] Vossen, G., Westerkamp, P.: E-Learning as a Web Service (Extended Abstract). these proceedings.
