Describing learning objects for situation-oriented knowledge management applications

Abstract

From a knowledge management (KM) perspective, demand for learning is triggered directly from business processes and requires presentation of learning objects customized to the specific situation. Standardized descriptions of learning objects so far have not been designed with this aim in mind. This paper studies current meta-data specifications for learning objects with respect to their relationships to context dimensions describing situations in business processes in which employees switch to learning activities described in the concept of knowledge stance. Finally, the paper gives an outlook to an approach for on-demand course composition.

Full text

Describing Learning Objects for Situation-
Oriented Knowledge Management
Applications1
Ronald Maier
University of Innsbruck
ronald.maier@uibk.ac.at
Stefan Thalmann
University of Innsbruck
stefan.thalmann@uibk.ac.at
Abstract: From a knowledge management (KM) perspective, demand
for learning is triggered directly from business processes and
requires presentation of learning objects customized to the specific
situation. Standardized descriptions of learning objects so far have
not been designed with this aim in mind. This paper studies current
meta-data specifications for learning objects with respect to their
relationships to context dimensions describing situations in business
processes in which employees switch to learning activities described
in the concept of knowledge stance. Finally, the paper gives an
outlook to an approach for on-demand course composition.
1. Introduction
The fields of e-learning (EL) and knowledge management (KM) have been
around for more than a decade and despite numerous attempts to bridge the
gap between the two intuitively strongly related fields, they are still quite
separated in research and practice [LM+06]. In large organizations, EL and
KM are institutionalized in different organizational units, information
systems as well as attitudes towards handling knowledge. A more formal
training approach with pre-defined courses contrasts a more informal
approach “harvesting” knowledge in projects and directly handing it on to an
unspecified target group without much effort put into validating it,
didactically refining it or examining success of the learning processes.
Although learning objects (LOs) or Web-based trainings (WBTs) typically
used in EL and lessons learned or best practices typically used in KM are
knowledge elements of varying stages of maturity, it supposedly can be
treated similarly. Approaches for designing more user- or situation-centric
KM applications seem to be equally useful for handling LOs.
1 Maier, Ronald; Thalmann, Stefan: Describing Learning Objects for Situataion-Oriented
Knowledge Management Applications. In: Gronau, Norbert (Ed.): 4th Conference on
Professional Konwledge Management Experiences and Visions. Band 2, GITO-Verlag, Berlin
2007. (343-351)

WBTs can be characterized either as monolithic, static courses or as more or
less pre-defined compositions of LOs. The result is in both cases a static
WBT that should fit all learners in all situations, irrespective of their
personal attributes and that of their current context. If the learner’s context
and needs are not considered, motivation, success and acceptance may
decline [DWC05]. In order to solve this problem, it is necessary to consider
the learner’s concrete situation and to compose LOs during run-time into
adaptive WBTs. Therefore, a detailed and processable description of the
situation must be available. Building on the theory of task-technology fit
[GTh95], a better fit between the learner’s situation (task) and the proposed
WBT (technology) should improve learner’s performance.
EL research and practice have successfully developed and adopted numerous
meta-data specifications and some widely used standards for the description
of LOs. However, due to their complex and technical nature, it is difficult for
a user to understand them. Thus, they do not adequately support the search
for useful LOs. Consequently, an automatic (push) delivery of composed
LOs dependent on the current situation seems to be a suitable solution. This
requires revisiting meta-data specifications for LOs which is described in the
following.
2. Modelling Situation-Oriented Applications
In the last years, many organizations have applied concepts of business
process reengineering. Numerous methods and techniques to support
business process modeling have been proposed. Recently, a number of
authors have suggested extensions to business process modeling techniques
that model (some of the) specifics of KM. Examples are extensions to ARIS
[All98], GPO-WM [Hei02], KMDL [Gro03] and PROMOTE [HKT02],
[KWo03] as well as the design of tools for flexible workflow management
[GHe01]. Main extensions are additional object types, e.g., knowledge
object or skill, and additional model types, e.g., knowledge structure
diagram or communication diagram (see [Mai05] for a detailed analysis).
Even though the added concepts describe a portion of the context of
knowledge work, there remain challenges (1) to overcome the product- or
push-oriented metaphor inherently part of many applications and embrace
the user-centric metaphor typically part of newer learning approaches, (2) to
move towards on-demand presentation of learning material according to the
situation in which the user currently is in and (3) to link the resulting
learning activities to business processes. However, none of the methods
clearly focuses designing typical situations in which employees encounter an
opportunity to learn which is at the center of just-in-time KM [DGl02],

workplace learning [Ell02], [Ill03] or on-demand KM [SK+02]. On-demand
KM means here that knowledge services are triggered by a situation in
which the user switches to a learning-oriented action, in this case LOs are
selected, composed and delivered considering as much context as possible.
A knowledge stance is a recurring situation in knowledge work in which an
employee can, should or must switch from a business function to a
knowledge action [HMa04], [Mai04]. This concept connects the process-
oriented perspective and its focus on implementation, exploitation, and
accumulation of knowledge in the context of business processes [Dav93],
[HCh93] with activity theory and its focus on unstructured problems,
creative, dynamic, and communication-intensive tasks, membership in
communities, self-organizing teams and demand for learning. [Eng87],
[Bla95]. A knowledge stance is defined by (1) occasion, i.e. opportunity or
need that occurs in a business function requiring knowledge actions, (2)
context, i.e. all dimensions describing the employee’s actual work
environment, (3) mode, described by the four informing practices
monitoring, translating, expressing and networking [Sch00] and (4) actions
offered depending on occasion, context and mode. Categories for actions are
developed e.g., from information quality tasks [Epp03].
In order to support all types of knowledge stances, a comprehensive KM
system ([ALe01], [Mai04], [MHP05]) seems the appropriate solution. In this
paper, we concentrate on the informing practice of translating, i.e. adapting
knowledge to the current situation and the actions that can be supported by a
learning infrastructure. Table 1 presents the dimensions of context required
for this type of applications.
dimension description implication for LOs
process sequence of tasks determines learning intent and application of the
acquired knowledge, i.e. the endpoint of the translation
person individual that
engages in the
learning process
is identified with the help of the person’s roles as well
as manually and automatically established profiles that
include preferences on learning
group teams, work groups,
communities the
learner is engaged in
represents the collective in which the individual learner
performs learning tasks, provides the social context for
understanding LOs and for collaborative learning
product electronic resources
which the learner use is the content of electronic resources used in the
process as well as whilst learning determining the
knowledge domain(s)
location geographic location enables location-based contents and services as well as
the adaptation of learning contents to cultural context
time available time for
learning, current time determines number and currency of LOs, the
appropriate level of detail and contemplation
technology technical attributes of
devices / applications is used for adapting LO to applications and appliances
and to get attention metadata from diverse applications

Table 1: Dimensions of context of a knowledge stance
Knowledge stances help to frame context dimensions needed to support
selecting LOs, presenting and translating them for application in the business
process that the learner is engaged in.
3. Standards for Describing Learning Objects
Resources, i.e. LOs and the context have to be matched in order to realise a
system delivering LOs coordinated with the learner’s context. LO as the
central concept to handle learning content in EL is defined as (non-) digital
entity that may be used for learning, education or training [IEE02]. Thus, a
LO can be a whole course, a graphic, a table, even in non-digital form. For
EL applications, LOs are restricted to digital resources [Wil01] which can be
(re-) used in defined contexts. Reusability demands a comprehensive
description of LO characteristics, typically stored as meta-data to the LOs.
Deciding the appropriate level of granularity of the meta-data descriptions
involves a trade-off between costs for meta-data creation and benefits from
reusing the LOs [Wil01].
Standards support defining what meta-data to create and exchanging meta-
data between applications and organisations. Meta-data must be classified in
a standardized way [PLR04] and several organizations, e.g., Dublin Core2,
Aviation Industry CBT Committee (AICC)3, Global Learning Consortium4
or IEEE Learning Technology Standards Committee5 have developed meta-
data schemata with different focuses and priorities. Additionally, several
organizations like CanCore6 or CELTS7 derive their own proposals from
established meta-data schemata and in the end some companies like CISCO8
can also be added to the large and intransparent group of initiatives.
However, IEEE Learning Object Metadata (LOM) [IEE02] seems to be the
most prominent exponent in the bulk of standards and specifications for
describing LOs [Duv04]. Therefore, LOM elements are considered in the
following mapping to the context dimensions introduced in section 2.
As a framework for mapping context dimensions, the generic categorization
developed by Gilliland-Swetland seems to be a good starting point with the
five categories administration, description, preservation, technology and use
2 http://www.dublincore.org
3 http://www.e-teaching.org/glossar/aicc
4 http://www.imsproject.org
5 http://ltsc.ieee.org
6 http://www.cancore.ca/
7 http://mdlet.jtc1sc36.org/doc/SC36_WG4_N0059.pdf
8 http://www.cisco.com/warp/public/779/ibs/solutions/learning/whitepapers

for classifying meta-data [Gil05]. From the perspective of this paper, the
categories preservation and technology both describe technical details and
thus are merged. The category education was added to this generic scheme in
order to enable the description of didactic characteristics of LOs as are
typically part of e-learning-oriented standards such as LOM.
4. Mapping of Standards to Context Dimensions
The meta-data standard LOM and several specifications representative for
numerous approaches listed in section 3 have been mapped to the context
dimensions presented in Table 1. The authors have studied the relationships
between meta-data elements and appropriate context dimensions with the
help of examples for purposeful selections of LOs on the basis of
descriptions of situations and Los (see Table 2). Categories, mappings and
some examples of relationships will be discussed in the following.
meta-data catego-
ries and elements elements and cites LOM matched
context
dimension
administration primary for handling and organising LOs
identification • identifier A
•
identifier -
storage • location information A
•
location, catalo-
gue & entry product
versioning • version control A
•
version, status
& contribute -
meta description
•
meta-metadata product
description for describing and classifying the content of the LO
key words • finding aids A
•
keywords process,
product
summary • exhibit records A
•
description -
title • title A
•
title -
annotations • annotations
A
•
annotations group,
person
creation process • cataloguing records A
• role B
•
contribute -
relationships • relationships A
• has alternative C
• has component C
•
taxon path &
relations product
language settings
•
language person
duration
•
duration time
granularity
•
aggregation
level &
semantic density
product
structure
•
structure -
sector classification
•
coverage process,

product
technology for describing the LO’s embedding in a technical infrastructure
requirements • hard & software documenta-
tion A
• control flexibility C
•
requirements &
platform
requirements
technology
backup • metadata for recordkeeping
systems A -
file information • digitization information A
• hazard C
•
format
• size
technology
configuration • display transformability C
•
installation
remarks technology
education for educational requirements and characteristics
instructional design • instructional domain, context
& strategy B
• method D
•
interactivity type
& interactivity
level
person,
group,
technology
handling time
•
typical learning
time time
outcome • learning outcome type B
• educational objective B
• learning objective D
•
purpose process
level of difficulty • competency level B
•
difficulty person,
group
learning material • environment D
•
learning resource
type person,
technology
target group • role D
•
end user role,
typical age &
context
group
adaptability • educational adaptability B
• property object D
person
requirements • required training resources B person
evaluation • assessment type B process
use for describing terms and conditions of application
legal terms of use legal access requirements A
•
copyright process,
person,
group
costs
•
cost process
usage • user tracking & audit trails A person
access requirements • access mode C person,
technology
re-use • content re-use A -
security • authentication and security
dataA person,
technology
A: [Gil05]
B: [AIC06] with selective extensions to IEEE LOM
C: [Can06] with selective extensions to IEEE LOM
D: [Kop01] with selective extensions to IEEE LOM, part general meta-data
Table 2: Mapping of learning objects to situations

The meta-data elements of the category administration are primarily used to
manage LOs and therefore the relevance for the mapping of context
dimensions is marginal. Nevertheless, there are some mappings with context
dimension product for corresponding meta-data entries.
In the category descriptive, elements can be assigned to nearly all context
dimensions except technology and location. Matching meta-data entries
from documents currently used in a situation in which LOs are required
(product) to meta-data elements of LOs permits many opportunities,
especially in matching the elements keywords, relationships, sector
classifications or granularities. For example, keywords from documents in
use can be applied to search for LOs with corresponding keyword entries.
Technical context parameters match exclusively to technical meta-data
elements, because the requirements and descriptions of technical devices are
reflecting those meta-data elements.
Meta-data elements from category education are typically associated with
personal characteristics, so these elements predominantly match the
dimensions person and group. For instance, information about completed
LOs are stored in a learner profile and can be matched to the requirements
when selecting LOs.
Elements from category use can not be exclusively assigned to a single
context dimension. Terms of use and costs are directly influenced by process
parameters like process priority. Other meta-data elements like access
requirements or security are assigned to technical or personal context
dimensions. For example, a cost limit specified in a process description can
be matched with the cost information from the meta-data of LOs, especially
when using a distributed repository or multiple content providers.
Except for context dimension location, all dimensions can be assigned to
meta-data elements, because LO descriptions typically do not contain
geographical information. However, location information can be derived
from other meta-data, e.g., by relating keywords to geographical coordinates
(points of interest).
Figure 1 shows the process of using context descriptions of a situation for
the on-demand presentation of learning material that is described in the
following.

Figure 1: Using context of a situation for composing courses
Starting point for on-demand learning systems is the business process. More
specifically, the learning activities would be triggered by an opportunity in a
business process in which an employee with a certain learning profile can,
should or must de-routinise and learn in order to be able to complete the
present task properly. A learning system realising the delivery of LOs
according to the worker’s situation described by the context dimensions has
to perform the following steps.
Firstly, context data needs to be collected which can be implemented by
sensor services that provide context from basic systems such as repositories,
ERP or HR systems. This raw data must be filtered, aggregated and related
in a separate consolidation process. After preparing the input data, it is
necessary to select LOs with the help of automatic query generation. LOs are
selected from the return set and composed into courses. The size of courses
can vary from atomic LOs over dyads to entire WBTs. The decision is
placed by a personal software agent or by a rule-based approach based on a
fitness function as benchmark for evaluation [GHt95]. The last step in the
process is to transfer the LO identifiers to the delivery component which
retrieves LOs from the repository and presents them to the learner.
The endpoint of a so-defined learning activity would not be passing a test,
but completing the original task in the business process and possibly a
reflection step, i.e. documenting learning experiences and experiences
gained from applying the knowledge to overcome this hurdle. This is a
scenario that is not typical for EL which rather concentrates on
contemplative, general, long-lasting courses that end with an exam and thus
poses a number of changed requirements towards designing such solutions.
5. Conclusion
In order to support demand for learning triggered directly from business
processes, this paper has presented one potential solution for on-demand KM
combined with EL. Most modelling approaches do not explicitly consider
the requirements from on-demand KM. Based on activity theory, the concept

of knowledge stance seems to be a sufficient solution. Corresponding
context dimensions can be mapped to current meta-data standards and
specifications for LOs. The approach handles LOs in a similar way as other
knowledge elements, such as lessons learned, good or best practices as well
as orientation services for locating experts or communities of practice. This
provides the potential for bridging current gaps between EL and KM
applications. As next step, the concept will be implemented as automatic on-
demand course delivery system using descriptions of the situation in
business processes based on the concept of knowledge stance.
Literature
[AIC06] AICC: Aviation Industry Metadata Description, DRAFT Draft
version 1.6, January 2006, URL: http://aicc.org/review/meta/Metadata-
draft16.doc, last access 2006-09-28.
[ALe01] Alavi, M., Leidner, D. E.: Review: Knowledge Management and
Knowledge Management Systems: Conceptual Foundations and
Research Issues. MIS Quarterly, Volume 25 (2001) 1, pp. 107-136.
[All98] Allweyer, T.: Modellbasiertes Wissensmanagement. Information
Management, 13(1), pp. 37-45.
[Bla95] Blackler, F.: Knowledge, Knowledge Work and Organizations: An
Overview and Interpretation. Organization Studies, Volume 16 (1995),
Issue 6, pp. 1021-1046.
[Can06] CanCore: Access for All Guidelines, last updated: 2006, URL:
http://www.cancore.ca/guidelines/drd/, last access 2006-09-28.
[Dav93] Davenport, T.: Business Process Innovation. HBS Press, Boston
1993.
[DWC05] Dagger, D., Wade, V., Conlan, O.: Personalisation for All:
Making Adaptive Course Composition Easy. Journal of Educational
Technology & Society, Volume 8, Issue 3, pp. 9–25.
[DGl02] Davenport, T., Glaser, J.: Just-in-Time Delivery Comes to
Knowledge Management. Harvard Business Review, 80(7), pp. 5-9.
[Duv04] Duval, E.: Learning Technology Standardization: Making Sense of
it All. Computer Science and Information Systems (ComSIS), Volume 1,
No. 1, February 2004.
[Ell02] Ellström P. E.: Integrating Learning and Work: Problems and
Prospects. Human Resource Development Quarterly, Volume 12, Issue
4, pp. 421-435.

[Eng87] Engeström, Y.: Learning by Expanding: An Activity-theoretical
Approach to Developmental Research. Orienta-Konsultit Oy, Helsinki
1987.
[Epp03] Eppler, M. J.: Managing Information Quality: Increasing the Value
of Information in Knowledge-intensive Products and Processes. Springer,
Berlin et al. 2003.
[Gil05] Gilliland-Swetland, A. J.: Introduction into Metadata – Pathways to
Digital Information. Online Edition, Version 2.1., last change: 2005,
URL: http://www.getty.edu/research/conducting_
research/standards/intrometadata/index.html, last access 2006-09-28.
[GHe01] Goesmann, T., Herrmann, T.: Wissensmanagement und
Geschäftsprozessunterstützung - am Beispiel des Workflow Memory
Information System WoMIS. In Herrmann, T., Scheer, A.-W., Weber, H.
(Eds.), Verbesserung von Geschäftsprozessen mit flexiblen Workflow-
Management-Systemen , Physica-Verlag, Heidelberg 2001, pp. 83-101.
[Gro03] Gronau, N.: Modellierung von wissensintensiven
Geschäftsprozessen mit der Beschreibungssprache K-Modeler. in
Gronau, N. (Ed.), Wissensmanagement: Potenziale - Konzepte -
Werkzeuge, Proceedings of the 4th Oldenburg Conference on Knowledge
Management, June 2003, Berlin, pp. 3-29.
[GTh95] Goodhue, D. L., Thompson R. L.: Task-technology fit and
individual performance. MIS Quarterly 19(2): 213, 1995.
[HCh93] Hammer, M., Champy, J.: Reengineering the Cooperation. Harper
Business Publishers, New York 1993.
[HMa04] Hädrich, T., Maier, R.: Modeling Knowledge Work, in: Chamoni,
P. et al. (eds.): Multikonferenz Wirtschaftsinformatik (MKWI), March 9-
11, 2004, Vol. 2, Berlin 2004, pp. 189-203.
[Hei02] Heisig, P.: GPO-WM: Methoden und Werkzeuge zum
geschäftsprozessorientierten Wissensmanagement. In Abecker, A.,
Hinkelmann, K., Heiko, M. (eds.), Geschäftsprozessorientiertes
Wissensmanagement, Springer, Berlin 2002, pp. 47-64.
[HKT02] Hinkelmann, K., Karagiannis, D., Telesko, R.: PROMOTE -
Methodologie und Werkzeug für geschäftsprozessorientiertes
Wissensmanagement. In: Abecker, A., Hinkelmann, K., Heiko, M. (eds.):
Geschäftsprozessorientiertes Wissensmanagement. Springer, Berlin
2002, pp. 65-90.
[IEE02]IEEE: Draft Standard for Learning Object Metadata (LOM). New
York. 2002, URL:
http://ltsc.ieee.org/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf, last
access 2006-09-28.

[Ill03] Illeris, K.: Workplace Learning and Learning Theory. Journal of
Workplace Learning. Vol.15, No 4.
[KWo03] Karagiannis, D., Woitsch, R.: The PROMOTE Approach:
Modelling Knowledge Management Processes to Describe Knowledge
Management Systems. In Gronau, N. (Ed.), Wissensmanagement:
Potenziale - Konzepte - Werkzeuge, Proceedings of the 4th Oldenburg
Conference on Knowledge Management, University of Oldenburg, June
2003, Berlin, pp. 35-52.
[Kop01] Koper, R.: Modeling units of study from pedagogical perspective
the pedagogical meta-model behind EML, Open University of the
Netherlands, 2001, URL: http://eml.ou.nl/introduction/docs/ped-
metamodel.pdf, last access 2006-09-28.
[Mai04] Maier, R.: Knowledge Management Systems: Information And
Communication Technologies for Knowledge Management. 2nd edition,
Springer, Berlin 2004.
[Mai05] Maier, R.: Modeling Knowledge Work for the Design of
Knowledge Infrastructures. Journal of Universal Computer Science, Vol.
11, No. 4, 2005, pp. 429-451.
[MHP05] Maier, R., Hädrich, T., Peinl, R.: Enterprise Knowledge
Infrastructures. Springer, Berlin 2005.
[LM+06] Le, T., Moghrabi, C., Tivendell, J., Hachey, J., Roy, J.: From
Knowledge Management System to E-Learning Tool. In Miltiadis, D. L.,
Naeve, A.: Intelligent Learning Infrastructures for Knowledge intensive
Orgenisations, Informtion Science Publishing, 2006, pp.81-104.
[PLR04] Paramythis, A., Loidl-Reisinger: Adaptive Learning Environments
and e-Learning Standards. In: Electronic Journal on e-Learning, Volume
2, Issue 1 (February 2004), pp. 181-194. URL:
http://www.ejel.org/volume-2/vol2-issue1/issue1-art11-paramythis.pdf,
last access 2006-09-28.
[SK+02] Sampson, D., Karagiannidis, C., Schenone, A., Cardinali, F.:
Knowledge-on-Demand in e-Learning and e-Working Settings.
Educational Technology & Society Journal, Special Issue on Integrating
Technology into Learning and Working, vol. 5(2), 4/2002, pp. 107-112.
[Sch00] Schultze, U.: A Confessional Account of an Ethnography About
Knowledge Work. In: MIS Quarterly 24 (2000) 1, pp. 3-41.
[Wil01] Wiley D. A.: Connecting learning objects to instructional design
theory: A definition, a metaphor, and a taxonomy. URL:
http://www.elearning-reviews.org/topics/technology/learning-
objects/2001-wiley-learning-objects-instructional-design-theory.pdf, last
access 2006-09-28.