Subject: [IFETS] Pre-discussion paper: Learning Objects and Instruction Components
From: Kinshuk (kinshuk@ieee.org)
Date: Fri 11 Feb 2000 - 04:14:29 MET
From: "Kinshuk" <kinshuk@ieee.org> Subject: [IFETS] Pre-discussion paper: Learning Objects and Instruction Components Date: Fri, 11 Feb 2000 16:14:29 +1300
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Dear colleagues
Please find below the pre-discussion paper on the theme 'Learning Objects
and Instruction Components' by Dr Clark Quinn, KnowledgePlanet.com, USA,
our moderator for the discussion. The discussion will formally end on 25th
February 2000.
The discussion paper is also available at following URL:
http://ifets.ieee.org/discussions/discuss_feb2000.html
Please send your comments on the paper to IFETS list at
ifets-discuss@LISTSERV.READADP.COM
Regards.
Kinshuk.
IFETS Coordinator
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Pre-discussion paper:
* Introduction
A new concept in Educational Technology is the 'learning object'. Learning
objects, as defined by the IEEE's Learning Technology Standards Committee
(http://ltsc.ieee.org), are "any entity, digital or non-digital, which can
be used, re-used or referenced during technology supported learning."
In this paper I introduce the concept, review current work in the area, and
discuss ways in which our research is leading us to push the standard in a
particular way. I conclude with some questions that arise from this work.
* Learning Object Model
The learning object (LO) model is characterized by the belief that we can
create independent chunks of educational content that provide an educational
experience for some pedagogical purpose. Drawing on the object-oriented
programming (OOP) model, this approach asserts that these chunks are self-
contained, though they may contain references to other objects; and they may
be combined or sequenced to form longer educational interactions. These chunks
of educational content may be of any type—interactive, passive—and they may be
of any format or media type. A learning object is not necessarily a digital
object; however, the remainder of this paper will focus on learning objects
that are exclusively digital.
An associated requirement for learning objects is that of tagging or metadata.
For these objects to be used intelligently, they must be labeled as to what
they contain, what they teach, and what requirements exist for using them, and
thus exists the need for a reliable and valid scheme for tagging learning objects.
The LO model provides a framework for exchange of learning materials between
systems. If LOs are represented in an independent way, conforming instructional
systems can deliver and manage them. The learning object activities are a subset
of efforts to creating learning technology standards for such interoperable
instructional systems.
* Benefits
The first major benefit provided by the LO model is the one imported from OOP--
reuse. A learning object designed by one person is made available to other
instructors who can use them for different educational purposes. For example,
a learning object that discusses how autos behave differently with and without
anti-lock brakes might be used in several different educational domains: the
physics of friction, automotive design, or insurance liability.
One of the benefits of the LO model is that it has the potential to reward the
best educational content, by allowing objects to 'compete' in a market economy.
In this scheme, there are costs to the consumer for the object, costs that are
then delivered to the author as rewards. Rights to the objects are made clear,
as is the financial responsibility. The objects can be customized, aggregated
to produce courses, etc., as the Intellectual Property (IP) owner dictates.
Then, as different authors produce different versions of the same content, the
economy rewards those authors who produce the most effective objects. The
Educational Object Economy (http://www.eoe.org) has parts of this, though they
are limited to Java Applets.
Another benefit is to provide search operations for objects that meet a
particular category. Instead of doing a web search on "+Railroad +US +Western
+Expansion", for example, a teacher might do a search which specified a search
for educational material aimed at fourth graders which described the western
expansion of the railroads in the US, particularly which incorporated maps.
This same capability could be used by learners to aid in their own educational
processes. The richer the tag set, the higher the likelihood of being able to
craft a query that generates a precisely targeted set of candidates.
* Tagging
In any system that uses learning objects, the objects are manipulated by the
system independent of their content, at least until delivery to the learner.
Consequently, the objects must be tagged to indicate many things about the
content. Tags have a syntax that indicates the name of the field or domain of
the tag, and the value attached to that label. For example, the field might be
author, and the value for this article would be "Clark Quinn".
Some tags are necessary, independent of educational use. Such tags would include
technical issues of format, size, and delivery requirements. Other categories
are authorship, ownership, and might include information about who did the
tagging. Information might also track version number, status, and other issues
associated with a lifecycle of the object. It might also indicate if there's
been annotations or aggregations.
While tags like this are certainly useful, one can imagine a number of additional
tags that might be useful for educational purposes. For example, it would be
desirable to tag learning material as to the content. For objects at the level
of courses or books, we might consider using any established library scheme,
such as the Library of Congress subject headings. If our objects are smaller,
how do we address this? Any librarian can tell you (and you should talk to them,
they've been trying to solve this problem for years) that there is no overarching
ontology that accounts for all knowledge. So unless we aggregate individual
objects into larger buckets and label the buckets, we haven't solved the problem
of semantic content tagging. If we do aggregate, we limit the flexible reuse of
objects.
There are other tags to consider, as well. One, particularly for smaller objects,
is the instructional role of the object, as well as instructional characteristics.
Is it informational, or does it require activity on the part of the learner?
Other questions might include how focused it is, whether it has navigation
requirements, or whether and what the form of feedback is.
Others have supported the learning object approach, notably Merrill (1998), but
there is lack of agreement on what needs to be indicated. While theoretically it
might be valuable to err on the side of over-specification, pragmatically there
are reasons to limit the amount of detail. The tradeoff, of course, is that for
greater effort, you get greater power. The question is: where to draw the line?
* Current coverage
There are several activities in progress to develop a tagging scheme for LOs,
including the Dublin Core, the Instructional Management System (IMS) project,
and the Learning Technology Standards Committee (LTSC).
The Dublin Core initiative was an early effort to standardize on what the core
tags for any information object should be, and has been remarkably successful to
the stage that most standard efforts start with the Core. The Dublin Core is now
separately investigating the special case of educational objects (independently
of the other ongoing work).
The Instructional Management Systems project of EduCause has made a tagging
proposal that has achieved the level of a first specification
(http://www.imsproject.org/metadata/index.html). Their work has passed on to
the IEEE's LTSC, particularly working group 12, and is the basis for further
work in this area. The LTSC have a draft that is close to voting standard
(http://ltsc.ieee.org/doc/wg12/LOM-WD3.htm). Notably, the LTSC is having the
work forwarded to ISO to work towards an internationally accepted standard.
The bottom line is that there is considerable work going into object metadata
that the educational technology community needs to be aware of.
Currently, the LTSC proposal includes tag categories of: General, LifeCycle,
MetaMetaData, Technical, Educational, Rights, Relation, Annotation, and
Classification. Most of these are true of objects regardless of purpose, and
would be true of knowledge objects as well as learning objects. It is only
the 5th category, Education, that really concern us, though I will occasionally
point to some other issues.
I will here note that the Classification category allows the introduction of
other classifications for use in tagging. This allows people to propose and use
new sets, and it is an explicit goal of the current tagging exercises to leave
some difficult issues vague and allow actual use to drive further specification.
The educational category has several types of tags for objects. The first is
interactivity type, covering flow of information between resource and user,
with restricted values of active, expositive (passive), or mixed. Then comes
learning resource type, describing the specific kind of resource (which can
be a list, prioritized), and allows any terminology but recommended values are
exercise, simulation, questionnaire, diagram, figure, graph, index, slide,
table, narrative text, exam, or experiment. Next comes interactivity level,
defining the degree of interactivity, and ranges from very low, through low,
medium, high, to very high. Semantic density has the same values, and is meant
to define a subjective measure of a resource's usefulness relative to size or
duration. There are categories for intended end users (teacher, author, learner,
manager), context of use (an open vocabulary, but examples include primary
education, secondary, higher ed, different university levels, tech schools,
etc.), typical age range, difficulty (again, a range from very low to very
high), typical learning time. Also included are a space for a text description
of the resource, and a language choice from the international standard codes.
* Issues
Not surprisingly, a number of issues arise. These issues naturally divide into
issues about the characteristics of the objects and characteristics of the
tagging of the objects. Under object issues is the issue of level of granularity.
Under tagging issues is the problem of vocabulary.
* Granularity
Currently, people tend to develop instruction where a complete course is the
smallest independent level of learning object. Certainly, that's the easy way.
Can we find value in pursuing a finer level of granularity?
Several arguments can be made for a finer level of granularity. First, with
smaller granularity, there's greater potential for reuse of objects. If the
anti-lock brakes example discussed above had incorporated several problems
specific to the insurance domain, for example, its reusability in the engineering
domain would be limited. By keeping objects smaller, they are more likely to be
able to be reused in different contexts.
Second, there's the opportunity to allow flexibility on the part of the learner,
or even to support intelligent processing. If the objects are small enough, and instructional experiences are composed of these
objects, then different learners
can have different instructional experiences.
While developing an online course, I was trying to move beyond traditional
instructional design to consider principles that might support people's choices
in sequencing. Perusing different instructional design theories, I was struck
that 'problem-based learning' (e.g. Barrows, 1986) provides problems first,
before conceptual material, while Laurillard (1993) suggests conceptual material
first. It seemed clear that one way I could support learners in determining
their preferred learning path was to break material up along the lines of the
role in the instructional process, and allow learners flexibility (while
preserving a lifeline of a default path that followed a safe and standard
approach). That led me to propose that instruction is composed the following
components: Introduction, Concept, Example, Practice, and Reflection.
Introduction is material that motivates, activates relevant knowledge, and
lists objectives. Concept is a presentation of the relevant abstraction.
Examples are applications of the concept to problems. Practice is opportunity
for the learner to practice the skill, including feedback. Reflection (as I
use it here) is material that cements the learning and prepares the learner to
transition beyond the learning experience. This includes reviewing concepts,
pointing to further directions for exploration, suggesting ways to practice
and keep the knowledge active, and a graceful segue from the learning experience.
The smaller granularity provided greater opportunities for learner control.
Granularity is independent of object use, and the tagging standards have
granularity (called Aggregation Level), under the General category. They talk
about atomic units (raw media data or fragments), collections of atoms
(molecules?), collections of collections, and full courses. Here, I am
suggesting that granularity at the collection level is the one in which
instructionally different individual choices would be made.
* Vocabulary
With many tags proposed for learning objects, one stumbling block is whether
to determine a fixed and controlled vocabulary for the tag, or to allow authors
to extend labeling to meet their own needs (called "open vocabulary with best
practice"). Although this is not an easy goal, I argue for a robust fixed
vocabulary instead of the alternative, a lack of interoperability. We need
categories designed so that authors or 'taggers' (a new job category that's
part editor, part administrative) can easily discriminate how a potential
object should be labeled and so that the objects are labeled consistently.
I argue for a robust fixed , because the alternative is a lack of
interoperability. This is not an easy goal. As an example that illustrates
the difficulty of a issues related to fixed vocabulary, consider the description
of 'interactivity level'. We might have objects that are interactive, and we'd
like to categorize this. However, I see several problems with using the
interactivity level tag as it is now defined. First, it is hard difficult to
imagine anyone using the 'low' category without guidance. If someone creates
an interactive object, they are hardly likely to consider it only minimally
interactive.
Second, it is not clear what distinguishes a ‘high’ interactivity object from
a ‘medium’ one. Interactivity can come from several sources, whether navigation,
or type of response, or quality and speed of feedback; and any of these sources
can vary independently, and be more or less important than the others.
Ideally, we would have conceptual distinctions in a fixed vocabulary, but the
definition of interactivity is currently unsolved. In the next best case, we
would have categorical, demonstrated examples; and, here, I would argue, you
can get traction (like pornography, you know interactivity when you see it).
I'll argue that we can create rough examples for such categories. For
interactivity level, this might be: no interactivity, page turning/linear
progression, multi-dimensional navigation like web pages or multiple choice
questions, or rich interaction such as SimCity or Doom/Quake with rich (or
seemingly limitless) choice interaction possibilities and rapid feedback.
While I am not committed to this particular set of distinctions, I believe
this is an achievable and desirable intermediate stage on the path to a
fixed vocabulary.
It's not easy to determine categories, nor to attempt to apply them to the
myriad types of potential objects, but the guidelines for accomplishing the
task can be by example as well as by theoretical principle. In places where
the theory is still controversial, we'll need to do it by example.
I recognize that what I propose is not an easy task, but if we do not control
the vocabulary, we ensure that systems cannot operate on the data. One
important future use of learning object tagging is for intelligent systems,
which will only be possible if the tagging is through a predictable vocabulary.
Just briefly, let me extend my interactivity level examples to two other
categories—semantic density and difficulty level--to indicate that this is a
generalizable approach. For semantic density, we could indicate something to
the effect of: concept material implicit but not explicit, or buried in
additional detail, as in a story; narrative and illustrated content; direct
representations such as expositive text, charts, tables, or graphs. For
difficulty, we could consider: introductory material; initial application or
overview material; scaffolded practice or detailed example; and full
application or for expert only.
* Discussion
The sum total of what I'm proposing is a fixed vocabulary for a finer
granularity and the discriminating feature (in addition to technical and
IP properties) being the instructional role of the object. I'd like to stop
here and suggest some questions for discussion.
- What about a new instructional design? This suggests a different approach to
instructional design, where the components of the instructional process are
designed separately and designed to stand alone. Is that a good direction, and
why or why not?
- What about granularity? This level of granularity provides greater
individualization of learning, but at an overhead for authoring. Is it worth
it, and why or why not?
- What about vocabulary? The powers of a controlled vocabulary are greater
automatic processing. The costs are significant debate and perhaps premature
limitations. Is the goal obtainable, and why or why not? Is it worthwhile, and
why or why not?
- What questions haven't we asked? What tradeoffs have I missed, and what are
their pros and cons?
* Acknowledgement
While I wrote the first draft, important revisions have been made by Brendon
Towle, Cindy Mazow, Edwin Bos, and Dan Christinaz. They substantially improved
it; all remaining errors, of course, are mine. It is hoped that they will
participate in the discussion as well.
* References
Barrows, H. S. (1986). A taxonomy of problem-based learning methods. Medical
Education, 20 (6), 481-486.
Laurillard, D. (1993). Rethinking university teaching: a framework for effective
use of educational technology, London: Routledge.
Merrill, M. D. (1998). Knowledge Analysis for Effective Instruction. CBT Solutions,
Mar/Apr, 1-11.
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