KNOWLEDGE REPRESENTATION FRAMEWORK

FOR CURRICULUM DEVELOPMENT

Milan Segedinac, Goran Savić and Zora Konjović

Faculty of Technical Sciences, University of Novi Sad, Fruskogorska 11, Novi Sad, Serbia

Keywords: Knowledge representation, Curriculum development, e-learning, Learning objectives, Instructional design.

Abstract: This paper presents the formal representation of knowledge used in curriculum development process. Four

curriculum components are represented separately: learning objectives, learning experiences, the

organization of learning experiences and evaluation of learning outcomes. Learning objectives are formally

represented using ontologies. Learning experiences consist of learning objects and achievements assessment

instruments (tests) and they are specified using IMS Content Packaging standard. Learning experiences are

mapped to the learning objectives ontology using XML. For describing instructional design, we proposed a

special-purpose language implemented using XML notation. The achievement of a learning objective is

assessed using test items linked to this particular objective. Such an approach allows more flexible

management of the curriculum as a whole and easier modification of the particular components than in

classical approach.

1 INTRODUCTION

For the proper management of a curriculum

development process, a formal specification of all

curriculum components is essential. This formal

representation should be used for automation of the

curriculum creation process. Besides, it enables

automation of curriculum. We start by analyzing

Tyler’s rationale for curriculum development.

Tyler’s rationale states that in any educational

setting, when developing curriculum, four

fundamental questions must be considered (Tyler,

1949):

1. What educational purposes should the

school seek to attain?

2. What educational experiences can be

provided that are likely to attain these

purposes?

3. How can educational experiences be

effectively organized?

4. How can we determine whether these

purposes are being attained?

These questions can be reformulated in a more

familiar way as forming the process of curriculum

and instruction development consisting of: the

selection of learning objectives, the selection of

learning experiences, the organization of learning

experiences, and the evaluation of learning outcomes

(Tyrell, 1974).

In this paper it is shown how the knowledge used

in the steps of the process of curriculum and

instruction development can be represented. The

relationships among the steps are stressed as well.

Each step is represented as an independent

component, because such approach is more flexible

and it provides easier modification of the

curriculum.

2 LEARNING OBJECTIVES

The formulation, classification and organization of

learning objectives play very important roles in

Tyler’s rationale since all other steps proceed from

them. In this paper, a learning objective is

understood as “an explicit formulation of the way in

which students are expected to be changed by the

educational process”, and these changes can be

represented using taxonomies (Bloom, Engelhart,

Furst, Hill and Krathwohl 1956).

When the content of learning objectives is

represented, the number of the taxonomies to be

represented equals to the number of courses. The

explicit representation of the content facilitates

327

Segedinac M., Savi

c G. and Konjovi

c Z..

KNOWLEDGE REPRESENTATION FRAMEWORK FOR CURRICULUM DEVELOPMENT .

DOI: 10.5220/0003052303270330

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2010), pages 327-330

ISBN: 978-989-8425-29-4

 2010 SCITEPRESS (Science and Technology Publications, Lda.)

establishing the relationships among the learning

objectives.

When representing learning objectives, students’

behaviours involved in these objectives should also

somehow be represented. For that purpose Bloom’s

taxonomy, taxonomy of students’ behaviours which

represent the expected learning outcomes had been

designed (Bloom et al., 1956).

Three levels of specificity of learning objectives

are identified in (Anderson, Krathwohl, Airasian,

Cruikshank, Mayer, Pintrich, Raths and Wittrock,

2001.): global, educational, and instructional.

Although ontologies have wider range of use,

they are natural tool for representing taxonomies.

Therefore we have decided to use OWL full to

represent learning objectives. Learning objectives

ontology conjoins domain knowledge taxonomy,

Bloom’s taxonomy, and learning objective

specificity taxonomy. In Figure 1 an example of

representation of a concrete learning objective is

shown.

h a s i n sta nc e

lo:hasDomainKnowledge

lo:hasSpecificityLevel

lo:hasKnowledgeCategory

lo:LearningObjectiveSpecificityLevel

lo:LearningObjective

lo:DomainKnowledge

lo:InstructionalObjective

IntroductionToHTML

bloom:KnowledgeCategory

bloom:Knowledge

IntroductionToHTMLKnowledge

Figure 1: Representation of a learning objective

IntroductionToHTMLKnowledge.

While learning, a student must achieve less

complex learning objectives before he or she can

proceed to more complex ones. Accordingly, the

feasible (consistent) combinations of the learning

objectives have to be established. Knowledge space

theory facilitates representing feasible individual

knowledge structures with respect to the set of

problems that a student should be able to solve

(Doignon and Falmagne, 1999). As achieving a

learning objective means being able to solve a

problem, the set of problems can be mapped to the

set of learning objectives (Segedinac, Savic and

Slivka. 2010).

When representing learning objectives, we

confine to learning spaces with the quasi ordinal set

of learning states. These knowledge spaces can be

represented using surmise relation introduced in the

set of learning objectives. Learning objective q

surmises learning objective q

if, from knowing that

a student has achieved q

we can infer that he or she

has achieved q

(Doignon and Falmagne, 1999).

Organizing learning objectives in a knowledge space

facilitates knowledge assessment and allows

chaining of learning objectives.

According to Bloom’s taxonomy (Bloom et al,

1956), when learning objectives refer to the same

domain knowledge, the objectives which require

lower cognitive processes are prerequisites for those

which require higher cognitive processes. This rule

was built in our model using SWRL rules. Besides

that, user can specify surmise relation among

learning objectives freely.

lo:nextLearningObjective

test items and learning objects

mapping on learning

objectives ontology

lo:surmisesLearningObjective

IntroductionToHTMLKnowledge

IntroductionToHTMLComprehension

SpecialCharactersInHTMLKnowledgeOfConvetnions

BasicHTMLTagsKnowledgeOfConvetions

BasicHTMLTagsComprehension

BasicHTMLTagsApplication

TestItem1

TestItem2

LearningObject1

LearningObject2

Figure 2: The mapping of learning objects and test items

to learning objectives chain with surmise relation.

After knowledge space was built on the set of

learning objectives, a chain of learning objectives

should be defined. For that purpose we introduced

relation next (a relation of total ordering) in the set

of learning objectives, forming a chain of learning

objectives. This relation allows us to suggest the

order of achieving learning objectives and is useful

when organizing learning experiences. Relation next

was introduced with respect to surmise relation,

specifying new SWRL rules. In Figure 2, an

example of learning objective chain with surmise

relation and learning objects and corresponding test

items is presented.

3 LEARNING EXPERIENCES

A student gets most of the learning experiences by

consuming specific learning resources. Thus, our

component for representing learning experience

should formally describe learning resources. For this

purpose, we suggest IMS Content Packaging

specification, which is globally accepted.

Although we define the learning objectives and

learning material as separate components, there is

KEOD 2010 - International Conference on Knowledge Engineering and Ontology Development

328

still strong relationship between these components.

Students use learning objects to achieve learning

objectives and teachers use tests to evaluate

students’ knowledge. Thus, learning resource is

always related to one or more learning objectives.

To describe this relationship, we have created an

intermediate component that defines mapping

between learning resources and learning objectives.

We use a particular XML document to define this

mapping. The relationship between learning

objectives and material is shown in Figure 2.

It can be noticed that in our approach ontology

of learning objectives has a role to define relations

among learning resources. Thus, the relation is not

defined as a part of the resource, but it is implicitly

defined through ontology. By this, it is easier to

change relation between two resources, i.e. in order

to change relation between two resources; one

should only map the resource to another objective.

Likewise, when adding a new resource, its

relationships with other resources are indirectly

defined by its learning objectives.

4 ORGANIZATION OF

LEARNING EXPERIENCES

In the previous chapter we assumed that learning

experience is closely related to a specific learning

resource. Thus, the organization of learning

experiences is actually defined by two components -

selecting and organizing learning resources. These

two components define instructional design used in a

course. Although there are numerous different

instructional strategies (Ryder, 2010), there is no

formal language aimed at specifying instructional

design. Formal specification of instructional design

should enable computer-aided reasoning about

instructional design. For that purpose, we have

created an XML-based language formally describing

instructional design used in the course. This

language should provide ability for a teacher to

define the order of learning activities and criterion

for selecting learning resources (number of

resources, their type, and priority). For these

reasons, our instructional design language defines

the path through the learning objectives (objectives

are represented as nodes in the ontology, see Figure

2). For each objective, the language specifies an

ordered subset of the resources mapped to the

objective.

In Figure 3. the UML model of our proposal for

instructional design specification is presented.

Root element in the model is instructional design

element representing the course. Structure element is

a generic learning element in organization. There are

two different types of elements – sequence and

learning object. Sequence is a chain of other

elements. Learning object is a unit of learning on the

lowest hierarchical level. It actually represents a

concrete learning resource. For each sequence we

can define a specific strategy for selecting learning

resources. This strategy is defined in selection rule

element. Selection rule aggregates two lists of

Object selection elements. First list contains objects

that are included in the course. The second one is for

excluded objects. Object selection element specifies

learning objects for including or excluding. For

included learning objects, we set an integer value

called priority, which defines the order of learning

objects in the sequence.

Besides the course structure, sometimes it is

necessary to define relationships among learning

elements. For example, in mastery learning, a

student can’t proceed to the next learning objective

until he or she has completed the previous one. So,

we need to define the relationship between two

learning objectives. These relationships are specified

using element relation element. This element has

references to the source and destination learning

elements, respectively.

Condition specifies when two elements are in the

relationship. If the condition is satisfied, a specific

action (defined in the then action element) is done.

Otherwise, an action defined in the else action

element is executed.

1 0..*

0..1

0..*

source

0..*

destination

0..*

0..1

0..*

include

0..1

0..*

exclude

0..1

0..*

10..*

instructional design

structure element

sequence

learning object

element relation

condition

selection rule

object selection

then action

else action

Figure 3: The UML 4model of proposed instructional

design specification.

On the basis of the described UML model, we

created an XML schema. Listing 1 presents a part of

an XML document, created according to the schema.

The document formally describes instructional

design used in the “Web programming” course held

at Faculty of Technical Sciences Novi Sad in 2009.

KNOWLEDGE REPRESENTATION FRAMEWORK FOR CURRICULUM DEVELOPMENT

329

5 EVALUATION OF LEARNING

OUTCOMES

The precise identification of the set of achieved

learning objectives plays the key role in successive

learning, because it leads further learning process

(Mager, 1984).

Knowledge space formed on the set of learning

objectives proposed in this paper allows us to use

techniques enabling the explicit specification of

achieved learning objectives. Each test item is

mapped to specific learning objective(s) and

multiple test items can be mapped to the same

learning objective (Figure 2). These techniques

explicitly identify the set of learning objectives that

student has achieved, and can be used in interactive

assessment (Degreef, Doignon, Ducamp and

Falmagne, 1986; Falmagne and Doignon, 1988), as

well as in classical educational settings (Segedinac

et. al. 2010).

Listing 1: Instructional design example.

6 CONCLUSIONS

In this paper a formal knowledge representation

model for curriculum development process

automation is proposed. The model consists of four

components: learning objectives, learning

experiences, the organization of learning

experiences, and the evaluation of learning

outcomes. Classical approach to modelling

curriculum development process often uses the

monolithic representation of the process resulting in

situation where small changes cause alteration of the

whole structure. In our approach, each component is

modelled separately which allows managing

curriculum in a more flexible manner and altering

components more easily than in a classical approach.

Future works will include extending one of the

existing open-source e-learning systems with

proposed curriculum development module. Such an

e-learning system would allow further pedagogical

research related to optimization and evaluation of

the educational process.

ACKNOWLEDGEMENTS

This paper was supported by the Ministry of science

and technological development, Republic of Serbia.

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<instructional-design root =”course”>

<uol-structure>

<selection-rule>

</selection-rule>

</sequence>

<learning-object type="exercise"/>

</sequence>

<selection-rule>

</selection-rule>

</sequence>

<learning-object label = "final_test"/>

</uol-structure>

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