Situated Models and Metadata for Learning Management1
Heidrun Allert
(Learning Lab Lower Saxony, University of Hanover, Germany
allert@learninglab.de)
Christoph Richter
(Learning Lab Lower Saxony, University of Hanover, Germany
richter@learninglab.de)
Wolfgang Nejdl (Learning Lab Lower Saxony, University of Hanover,
Germany
nejdl@learninglab.de)
Abstract: This paper depicts the interrelation between situated
learning and learning management from an organizational and personal perspective.
Based on this introduction we show how educational metadata can be used
for approaches of situated learning and how we can take care of contexts
using context specific role-based metadata.
Keywords: eLearning, Metadata, Situated Learning
Category: K.3.0
1 Situated Approaches in Educational Management
In an environment that is flexible and changes rapidly, knowledge management
and educational management, form an integral part of strategic planning.
This holds for the educational manager within an organization as well as
for a self-organized life long learner. Educational management not only
has to deliver knowledge-assets, but also to enable learners to create,
communicate, and share knowledge, to develop competences, meta-cognitive
skills, and capabilities to support the co-construction of shared innovative
knowledge. Furthermore, educational management can be based on a large
repertoire of pedagogical concepts ranging from models of instructional
design to situated and humanistic approaches. In the following we will
refer to pedagogical approaches which are based on information processing
theory as instructional design (ID) (e.g. instruction, well-structured
problem solving), and to pedagogical approaches which are based on theories
of situated cognition as situated approaches (e.g. ill-structured
problem solving, Communities of Practice). Educational management faces
diverse goals and needs. Learning on demand which concentrates on just
in time access to information is only one aspect within a more comprehensive
vision.
1 A short version of this article was
presented at the I-KNOW '03 (Graz, Austria, July 2-4, 2003).
Education within an organizational context has to facilitate procedural
and pragmatic knowledge in addition to domain specific and predominantly
declarative knowledge. Members of an organization are engaged in transformation
processes and complex processes of change by creating innovative and strategic
knowledge.
Current standards and concepts for educational metadata focus on content-centered
approaches and models of instructional design rather than situated approaches.
Situated approaches engage learners in processes of creating and (co-)
constructing knowledge. These processes are dynamic and ill-structured.
Metadata for situated approaches could broaden the view and add visions
which are closer to the ideas of the semantic web outlined in a scenario
by Tim Berners-Lee et al. [Berners-Lee et al. 2001].
Actors in a scenario about learning services on the semantic web will be
self-organized learners and educational managers [Allert
et al. 2002].
We draft a metadata-concept which is relevant for situated approaches
as well as instructional design and which will facilitate learners and
teachers to organize learning processes. It is also relevant for exchanging
and comparing units-of-studies.
2 Valuable Diversity
There are diverse models, theories, principles and paradigms of learning
and teaching. There is common agreement that this diversity is of great
value and that standardization in the field of learning has to address
all of these models. IMS Learning Design is based on EML, which forms a
pedagogical meta-model. LOM aims at being neutral with regard to learning
theories and models. Often different models and theories of learning are
referred to as cognitivist, constructivist or behaviorist view of learning.
Here we distinguish different metaphors of learning and knowledge as
this gives a more vivid view on different epistemological foundations of
learning. [Sfard 1998] distinguishes the acquisition
metaphor from the participation metaphor of learning. The acquisition
metaphor refers to learning which is "a matter of individual construction,
acquisition, and such outcomes, which are realized in the process of transfer;
it consists in a person's capability to use and apply knowledge in new
situations. Knowledge is a property and possession of an individual mind"
[Paavola 2002]. The participation metaphor of learning
refers to learning as a process of participation in shared learning activities
and social processes of knowledge construction. "Cognition and knowing
are distributed over both individuals and their environments, and learning
is 'located' in these relations ad networks of distributed activities of
participation." [Paavola 2002]. This view is
based on the concept of situated learning [Lave and Wenger
1991] and on [Vygotsky 1978]. Paavola extends
the participation metaphor of learning and refers to it as knowledge-creation
metaphor of learning, which means that "learning is seen as analogous
to processes of inquiry, especially to innovative processes of inquiry
where something new is created and the initial knowledge is either substantially
enriched or significantly transformed during the process" [Paavola
2003]. The knowledge-creation metaphor of learning is seen as epistemological
foundation of CSCL and knowledge communities. Stahl refers to learning
as meaning making and grounds its collaborative character in the philosophical
tradition of Heidegger and in Vygotsky's concept of mediated cognition
which show how meaning is socially produced and situationally interpreted
[Stahl 2003].
Figure 1 arranges approaches of instruction and learning on a continuum
of contextualization.

Figure 1: Views of learning on the continuum of contextualization
3 Metadata for Instructional Design and Situated Approaches
The vision underlying existing metadata approaches is well expressed
by [LOM 2002]: to enable Computer agents to automatically
and dynamically compose personalized lessons for an individual learner.
Therefore a main goal is to compose consistency within a course or instructional
unit. This is consistent with the guiding principle of instructional design.
According to Reimann-Rothmann and Mandl [Reimann-Rothmeier
and Mandl 2001], goal and result of ID models are plans of instruction
which tell instructors which strategy of instruction and method of teaching
to choose according to given preconditions and prerequisites. Therefore
instruction can be formalized and automated. Learning objects are decontextualized.
Guiding principles and intended use of metadata for situated approaches
are different: Learning processes of situated learning are ill-structured.
Context plays a crucial role in situated learning. Metadata for situated
approaches have to refer to the specific learning context and go beyond
the mere creation of courses by authors etc. Metadata of situated approaches
stress the aspect of interaction, communication and cooperation and have
to support tasks like the following:
- A learner search for a Community of Practice with a specific strategic
intent within the Educational Semantic Web.
- A Community of Practice creates a shared understanding by annotating
knowledge-assets with "lessons learned" or "best practice".
- Learners search for project presentations of peers.
- A learner searches for a peer to perform peer-tutoring with, a coach,
etc.
- A mediating agent matches user profiles to support group formation.
The guiding principle is to enable interactive and cooperative processes.
We assume that scenarios like these have not been intended by LOM, SCORM
and other existing metadata approaches; but they are in the scope of many
learning theories. We state that metadata should meet both visions, and
thus situated approaches as well as models of instructional design should
be addressed.
The context-specific use of learning resources requires context-specific
metadata. General and objective annotation is obsolete in this concept.
Two consequences become relevant: The types relevant in educational settings
are not restricted to knowledge-assets but also comprise persons, technology,
activities, arrangements (figure 2).

Figure 2: Types and subtypes relevant in learning (examples)
Within existing metadata approaches, learning objects are equal with
the information object (e.g. a knowledge asset) itself. One of the major
problems with this equation is that there is no significant and explicit
distinction between an educational resource and a resource as any resource
can be used in education (e.g. the poem "The Road Not Taken"
by Robert Frost was not mainly intended to be an educational resource but
can be used in educational settings). The concept of context specific metadata
explicitly makes this distinction. A knowledge asset (e.g. person, technology,
activity, arrangement) which can fill a role within a certain learning
context is a learning resource. This notion is based on the concept of
autopoietic social systems [Luhmann 2001]. Learning
resources are characterized and constituted by context and relations.
4 Modeling Consistent Social Systems
The concept of Learning Roles explicitly models different views of learning.
The underlying assumption is that mature life long learners not only know
what they want to learn but also how. Therefore we do not model an integrative
theory but focus on expressiveness and significance within the Educational
Semantic web.
As LOM aims at consistent sequences, Learning Roles aim at coherent
social systems. The concept refers to the theory of Social Systems (the
functional-structural system theory) by [Luhmann 1995].
Systems reduce complexity - activities of persons are significantly related
within a system. E.g. when a speaker speaks, the audience listens. According
to Luhmann, persons do not belong to a system but to its environment.
This means persons do only belong to a system filling a specific role.
Within different systems they fill different roles.

Figure 3: A person (type) filling roles within different
systems

Figure 4: A picture (type) filling roles within different
systems
The legal system serves as an example here. There is no legal system
without a fundament. Legal systems are either based on codified law
(e.g. the German legal system) or on case law (as in Anglo-Saxon
countries). Here only the codified law is modeled. Roles within the system
are related. E.g. person-roles: there is no accused without a complainant,
no father without son (or daughter). Also the activities of the accused,
complainant, attestor, and the judge are related. Within systems expectations
are tied to roles.
Within this legal system a picture does not exist but only a picture
which fills the role indication. This means: as soon as someone
hands in a picture the judge will bring it into the system as indication
- or eventually refuses to do so. Only filling the role indication the
picture is part of the system.
What does system-oriented modeling mean for metadata in the field of
learning? Two examples will demonstrate this view:
- A Community of Practice (CoP) or a knowledge building community comprises
the roles core member, active member, peripheral member,
coordinator, expert [Wenger 2002], but not
the roles learner and teacher. Information assets fill roles such as innovative
knowledge, best practice, lessons learned. Persons filling
roles within a CoP-meeting have specific expectations concerning learning
process, learning culture etc.
- A session of instruction (expository learning and teaching, receptive
learning) comprises the roles learner and teacher. Information assets have
a function within the learning process and can fill the role orientation,
progressive differentiation, practice, or integration [cp.
Ausubel 1968]. A person filling the role learner within
this session will have specific expectations.
Peter is a person and might fill the role coordinator in the CoP arctic
biologists and at the same time he fills the role learner in an instructional
(expository) scenario. An information asset also may fill different roles
within different concepts of learning.
4.1 The Concept Role
To model social systems a corresponding concept taken from formal languages
is needed. The concept of Roles we use is taken from the field of semantics
and formal languages, see [Steimann 2000a, b].
Steimann recommends introducing the concept of Roles into object-oriented
modeling in order to make possible dynamic modeling approaches. He distinguishes
natural types and class-types from roles-types (table 1). Roles are not
semantically rigid but founded [Guarino 1992]. Instances
of natural types can fill, adopt and leave a role without loosing their
identity. Roles are defined by context and relation (interaction).
Natural-Type/Class-Type |
Role-Type |
|
- Dynamic (Dynamic classifying)
|
- An instance of a class once and forever belongs to that class. It cannot
change it without loosing its identity
|
- Founded (has context and relations) *Not semantically rigid - does
not lose its identity when leaving the role [Guarino 1992]
|
Table 1: Distinguishing natural-types and class-types from
role-types.
5 Role-based Metadata
It is necessary to distinguish between static attributes (such as DC
and vCard attributes) which are based on the type of a learning object
or learning resource, and context- or role-dependent attributes which are
based on the roles a learning resource can fill. Every educational resource
can have one or more associated roles. Learning objects, persons, and other
educational resources have some context-independent attributes; in the
case of information-assets, these are mainly the attributes from Dublin
Core and some further LOM attributes, like dc:title, dc:creator, etc..
Persons are annotated with vCard attributes like vcard:FN (full name) and
vcard:EMAIL. Furthermore, context-specific, role-based attributes are attached
to educational resources.
5.1 Learning Roles
To model diversity we introduce the concept of Learning Roles.
We call roles in the context of learning Learning Roles. Learning
Roles are meta-roles (meta-types in M2 in figure 5) which specify roles,
interaction between roles, and qualities/properties of roles.
Each Learning Role reflects a specific concept of learning, learning
theory or pedagogical approach (both instructional design and situated
approaches). Learning resources can fill roles temporarily which are specified
by Learning Roles and therefore dynamically adopt properties from diverse
Learning Roles. In a previous paper we proposed the concept of Learning
Roles to specify educational attributes [Allert et al.
2003]: a learning service may fill different roles in different instructional/learning
contexts. Similarly to how ontologies are often agreed on by a community
of knowledge such as ACM or IEEE we suggest deciding on relevant roles
within communities (such as scientists, practitioners, consultants on educational
management). Comparable with ontologies Learning Roles can be seen as shared
conceptualization: Communities (e.g. the community of CoP) have to agree
on a shared understanding of learning (concept of learning) and on relevant
characteristics of specific models and specify appropriate metadata.

Figure 5: Model Community of Practice (CoP) - examples of
types and roles
5.2 Identifying Relevant Types and Roles
Each learning theory is constituted by characteristic elements and is
based on an epistemological foundation. From these characteristic elements
one can identify relevant types and roles. Relevant learning services can
be identified by asking: What is useful to be provided and offered on the
(semantic) web. What do users search for in the context of learning? Then
we infer conceptual models. Here we outline two models. The model Communities
of Practice and the model Problem-Based Learning (solving ill-structured
problems). Within the diagram a rectangle indicates a natural-type
and a cycle indicates a role-type. (Fig. 5, 6)
Whatever entity is to be annotated one can ask which type it is (person,
knowledge-asset, technology, activity, and arrangement) and can annotate
this type with suitable metadata (vCard for persons, Dublin Core or reduced
LOM for knowledge assets e.g.). Then we can ask what role it fills or can
fill.
Additional educational metadata is then derived from different Learning
Roles. Any entity will be annotated with static attributes and context-specific
role-based (dynamic) attributes.

Figure 6: Model PBL ill-structured - examples of types and
roles
A system is referred to as a type. This means: if an instructional-unit
is integrated in a session of Problem-Based Learning, the instructional
unit is a type, filling a role within the learning process of PBL (integrating
a unit-of-study in unit-of-study).
Another example: there might be the Meta-Type "Brainstorming",
comprising specific roles. In a CSCL session Brainstorming is a type (behaviour),
which fills the role "knowledge externalization" (activity).
Activity roles represent the function behaviour (type) has within
a learning process. An activity is defined as "goal-directed".
Behaviour (such as "group discussion", "brainstorming"
etc.) has a goal within a learning process. Therefore behaviour represents
an activity in a learning process. Someone who plans a learning process
asks: what function does the "group discussion" have within the
learning process. Or vice versa: how can we induce "knowledge externalization"?
Then the type "group discussion" fills the role "knowledge
externalization" within the CSCL session.
6 Practical Implications
Human activity is predominantly shaped by schemata and scripts. These
schemata and scripts are relevant only within specific contexts. The script
about behaving in a first class restaurant is quite different from a fast-food
restaurant script. The script comprises expectations about activity sequences,
the behaviour within the assigned roles, etc. Similarly different learning
theories demand for activating different scripts and schemata. Activating
inappropriate schemata or scripts causes problems within educational settings.
The following scenario illustrates this:
Dr. Holm is a well known expert and consultant in the field of strategic
management. This morning she starts a new course within an Executive MBA
program. She is quite motivated as she prepared something special: She
plans to present problem situations. Students are supposed to solve these
problems cooperatively. All problems she selected are ill-structured: There
is no one-best solution but any solution will have pros and cons: 'Like
in real businesses. Learning does take place in cooperative construction
of arguments, in correcting wrong conclusions, as well as in reflecting
the learning processes in the end. She never prepared something like this
before - it was hard work and took much more time than she expected. Normally
she is giving a lecture telling about her experiences. But as she herself
is motivated she expects her students to be motivated as well.
But when she finished presenting the problem situations students
only expressed their dissatisfaction. They expected her to present her
knowledge in which they are so interested in. Why should novices solve
problems when all the knowledge they need is missing and when experts already
have well-prepared solutions? This situation rather quickly was somehow
deadlocked and Dr. Holm was only able to cope with this unexpected situation
in giving a fairly unprepared lecture.
Role-based modeling facilitates the orientation within a given context
and allows comparing contexts instead of generalizing and homogenizing
across divers contexts [Allert et al. 2003]. Additionally
a common activity model can describe interactivity within an educational
setting. The concept of context-specific metadata shares a system-centered
view to describe complex interaction processes [cp. System Theory Luhmann
2001]. To reduce complexity in modeling the context, we model specific
learning theories. Each learning theory is based on an epistemologically
founded term of learning, specific actor roles, perspectives and so on.
An object potentially fills different Learning Roles. For example a
person can hold the role Community Coordinator within a specific
Community of Practice while he fills the role Problem Solver in
a problem solving team. The attributes and tasks assigned to this person
vary with respect to the role. In the same way a knowledge asset might
fill the role Best Practice in a CoP while it is used as an Example-Integrating
Knowledge in an instructional learning arrangement.
7 Further Work
In a first step we will propose a complete set of metadata (including
types and roles) for a specific Learning Role. We then test the implications
towards CSCL user and group profiles to facilitate cooperative learning.
Based on this we will elaborate the requirements for the design of mediating
agents which support group formation e.g.
Metadata in the field of learning often address technical and organizational
requirements. In this work we focus on the educational aspects and on the
learner.
References
[Allert et al. 2003] Allert, H.; Richter, C.; Nejdl,
W.: Extending the Scope of the Current Discussion on Metadata towards Situated
Models. 5th International CSCL conference, Bergen, Norway, June, 14-18,
2003.
[Allert et al. 2002] Allert, H.; Richter, C. Nejdl,W.:
Learning Objects on the Semantic Web - Explicitly Modelling Instructional
Theories and Paradigms. In: AACE, E-Learn, World Conference on E-Learning
in Corporate, Healthcare, & Higher Education. Montreal, 2002.
[Ausubel 1968] Ausubel, D.P. Eudcational psychology
- A cognitive view. NewYork, 1968.
[Berners-Lee et al. 2001] Berners-Lee, T.; Hendler,
J.; Lassila, O.: The Semantic Web. A new form of Web content that is meaningful
to computers will unleash a revolution of new possibilities. Scientific
American: Feature Article: The Semantic Web: May 2001.
[Guarino 1992] Guarino, N.: Concepts, Attributes
and Arbitrary Relations: Some Linguistic and Ontological Criteria for Structuring
Knowledge Bases. Data & Knowledge Engeneering, 8, 1992; pp. 249-261.
[Lave and Wenger 1991] Lave, J.; Wenger, E.: Situated
Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University
Press, 1991.
[LOM 2002] Learning Object Metadata Working Group.
Draft Standard for Learning Object Metadata. IEEE 1484.12.1-2002.
[Luhmann 2001] Luhmann, N.: Soziale Systeme, Grundriss
einer allgemeinen Theorie. Frankfurt, 2001.
[Luhmann 1995] Luhmann, N.: Social Systems. Stanford
University Press, 1995.
[Paavola 2002] Paavola, S; Lipponen, L.; Hakkarainen,
K.: Epistemological Foundation for CSCL: A Comparison of Three Models of
Innovative Knowledge Communities. Retrieved from: http://newmedia.colorado.edu/cscl/228.html)
[Reimann-Rothmeier and Mandl 2001] Reimann-Rothmeier,
G.; Mandl, H.: Unterrichten und Lernumgebungen gestalten. In: A. Krapp;
B. Weidenmann (Hrsg). Pädagogische Psychologie: Ein Lehrbuch. Weinheim,
Beltz, 2001; S. 601-646.
[Sfard 1998] Sfard, A.: On two metaphors for learning
and the danger of choosing just one. Educational Researcher, 27, 1998,
4-37.
[Stahl 2003] Stahl, G. Meaning and Interpretation
in Collaboration. In: B. Wasson, S. Ludwigsen, & U. Hoppe (eds.), Designing
for Change. Kluwer Academic Publishers, 2003, 523 - 532.
[Steimann 2000a] Steimann, F.: Modellierung mit
Rollen. Habilitationsschrift, University of Hanover, Germany, 2000.
[Steimann 2000b] Steimann, F.: On the Representation
of Roles in Object-Oriented and Conceptual Modelling. Data & Knowledge
Engineering 35:1, 2000; 83-106.
[Vygotsky 1978] Vygotsky, L. S.: Mind and society:
The development of higher psychological processes. Cambridge, MA: Harvard
University Press, 1978.
[Wenger 2002] Wenger, E., McDermott, R., & Snyder,
W.M.: Cultivating Communities of Practice. A Guide to Managing Knowledge.
Boston: Harvard Business School Press, 2002.
|