BUILDING A DECISION SUPPORT SYSTEM FOR STUDENTS

BY USING CONCEPT MAPS

Dumitru Dan Burdescu, Marian Cristian Mihaescu

Software Engineering Department, University of Craiova,Craiova, Romania

Bogdan Logofatu

CREDIS, University of Bucharest, Bucharest, Romania

Keywords: Concept mapping, e-learning, decision support system.

Abstract: Concept maps are an effective way of representing organized knowledge (concepts) in hierarchical fashion

regarding a person’s understanding of a domain of knowledge. Within our custom developed e-Learning

platform it was created a concept map for a chapter of a discipline. The obtained concept map has been used

for creation the test and exam questions such that knowledge regarding each concept is tested by a certain

number of quizzes. We present the architecture of a decision support system that assesses the accumulated

knowledge of students. The architecture’s business logic is based on a concept map of a chapter of a

discipline. A custom algorithm has been designed and implemented to measure the coverage of the

curriculum. The system may be generalized for entire discipline as long as for each chapter is set up a

concept map and all other necessary settings.

1 INTRODUCTION

Every e-Learning platform has implemented a

mechanism for assessing the quantity of

accumulated knowledge for a certain discipline. A

problem that frequently arises is that the system in

place may not be fair regarding the ordering of

learners according with accumulated knowledge.

Usually, there are situations when the distributions

of grades is not normal, such that many learners are

clustered although there are differences regarding

their accumulated knowledge.

The evaluation environment is represented by the

setup put in place within an e-Learning platform for

assessment of learners. The setup consists of course

materials and test quizzes that are set up by course

managers. Learner’s activities are obtained by

specific methods embedded in our e-Learning

platform, called Tesys (Burdescu et. al., 2006). The

main goal of the application is to give students the

possibility to download course materials, take tests

or sustain final examinations and communicate with

all involved parties. To accomplish this, four

different roles were defined for the platform:

sysadmin, secretary, professor and student.

Concept maps are a result of Novak and Gowin’s

(1984) research into human learning and knowledge

construction. Novak (1977) proposed that the

primary elements of knowledge are concepts and

relationships between concepts are propositions.

Novak (1998) defined concepts as “perceived

regularities in events or objects, or records of events

or objects, designated by a label”. Propositions

consist of two or more concept labels connected by a

linking relationship that forms a semantic unit.

Concept maps are a graphical two-dimensional

display of concepts (usually represented within

boxes or circles), connected by directed arcs

encoding brief relationships (linking phrases)

between pairs of concepts forming propositions. The

simplest concept map consists of two nodes

connected by an arc representing a simple sentence

such as ‘flower is red,’ but they can also become

quite intricate.

One of the powerful uses of concept maps is not

only as a learning tool but also as an evaluation tool,

thus encouraging students to use meaningful-mode

learning patterns (Mintzes et al., 2000; Novak, 1990;

Novak & Gowin, 1984). Concept maps are also

effective in identifying both valid and invalid ideas

130

Dan Burdescu D., Cristian Mihaescu M. and Logofatu B. (2008).

BUILDING A DECISION SUPPORT SYSTEM FOR STUDENTS BY USING CONCEPT MAPS.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 130-135

DOI: 10.5220/0001673301300135

 SciTePress

held by students, and this will be discussed further in

another section. They can be as effective as more

time-consuming clinical interviews for identifying

the relevant knowledge a learner possesses before or

after instruction (Edwards & Fraser, 1983).

Ausubel made the very important distinction

between rote learning and meaningful learning.

Meaningful learning requires three conditions: 1.

The material to be learned must be conceptually

clear and presented with language and examples

relatable to the learner’s prior knowledge. Concept

maps can be helpful to meet this condition, both by

identifying large general concepts held by the leaner

prior to instruction of more specific concepts, and by

assisting in the sequencing of learning tasks though

progressively more explicit knowledge that can be

anchored into developing conceptual frameworks; 2.

The learner must possess relevant prior knowledge.

This condition can be met after age 3 for virtually

any domain of subject matter, but it is necessary to

be careful and explicit in building concept

frameworks if one hopes to present detailed specific

knowledge in any field in subsequent lessons. We

see, therefore, that conditions (1) and (2) are

interrelated and both are important; 3. The learner

must choose to learn meaningfully. The one

condition over which the teacher or mentor has only

indirect control is the motivation of students to

choose to learn by attempting to incorporate new

meanings into their prior knowledge, rather than

simply memorizing concept definitions or

propositional statements or computational

procedures. The indirect control over this choice is

primarily in instructional strategies used and the

evaluation strategies used. Instructional strategies

that emphasize relating new knowledge to the

learner’s existing knowledge foster meaningful

learning. Evaluation strategies that encourage

learners to relate ideas they possess with new ideas

also encourage meaningful learning. Typical

objective tests seldom require more than rote

learning (Holden, 1992).

Knowledge modeling methods and languages

may be thought of as representation schemes that

augment traditional data modeling by adding

semantic content to the modeling language (Mineau

et al. 2000). Knowledge modeling approaches lie on

a continuum from informal (and potentially, easily

understood by humans) to formal (and therefore,

capable of being evaluated by machine). Chan and

Johnston (1996) describe two categories of

approaches to knowledge modeling: one group

based upon problem solving methods and another

based upon domain ontologies. These two

approaches have significant overlap in the sense

that, although problem solving methods are process

oriented and ontological accounts start with

characterizations of objects, at some point during

knowledge model construction, process models must

be created.

This paper presents a procedure for measuring

learner’s accumulated knowledge using concept

mapping. The most important thing is to construct a

good concept map. It is important to begin with a

domain of knowledge that is very familiar to the

person constructing the map. After concepts have

been enumerated the concept map is build. For each

concept there is created a pool of questions

regarding that concept. At any point in time the

concept map may be seen as a graph, such that a

coverage function may be used to compute the

learner’s accumulated knowledge.

2 METHODS AND MATERIALS

2.1 Tesys e-Learning Platform

The main goal of the platform is to give students the

possibility to download course materials, take tests

or sustain final examinations and communicate with

all involved parties. To accomplish this, four

different roles were defined for the platform:

sysadmin, secretary, professor and student.

The main task of sysadmin users is to manage

secretaries. A sysadmin user may add or delete

secretaries, or change their password. He may also

view the actions performed by all other users of the

platform. All actions performed by users are logged.

In this way the sysadmin may check the activity that

takes place on the application. The logging facility

has some benefits. An audit may be performed for

the application with the logs as witness. Security

breaches may also be discovered.

Secretary users manage sections, professors,

disciplines and students. On any of these a secretary

may perform actions like add, delete or update.

These actions will finally set up the application

such that professors and students may use it. As

conclusion, the secretary manages a list of sections,

a list of professors and a list of students. Each

discipline is assigned to a section and has as

attributes a name, a short name, the year of study

and semester when it is studied and the list of

professors that teach the discipline which may be

maximum three. A student may be enrolled to one or

more sections.

BUILDING A DECISION SUPPORT SYSTEM FOR STUDENTS BY USING CONCEPT MAPS

131

Tesys application offers students the possibility

to download course materials, take tests and exams

and communicate with other involved parties like

professors and secretaries.

Students may download only course materials for

the disciplines that belong to sections where they are

enrolled. They can take tests and exams with

constraints that were set up by the secretary through

the year structure facility.

2.2 Sample Concept Maps

Concept mapping may be used as a tool for

understanding, collaborating, validating, and

integrating curriculum content that is designed to

develop specific competencies. Concept mapping, a

tool originally developed to facilitate student

learning by organizing key and supporting concepts

into visual frameworks, can also facilitate

communication among faculty and administrators

about curricular structures, complex cognitive

frameworks, and competency-based learning

outcomes. To validate the relationships among the

competencies articulated by specialized accrediting

agencies, certification boards, and professional

associations, faculty may find the concept mapping

tool beneficial in illustrating relationships among,

approaches to, and compliance with competencies

(McDaniel et. al.).

According to this approach, the responsibility for

failure at school was to be attributed exclusively to

the innate (and, therefore, unalterable) intellectual

capacities of the pupil. The learning/ teaching

process was, then, looked upon in a simplistic, linear

way: the teacher transmits (and is the repository of)

knowledge, while the learner is required to comply

with the teacher and store the ideas being imparted.

(Vecchia, L, et. al.)

2.3 Knowledge Evaluation

Methodology

Knowledge evaluation is closely related with

cognitive processes performed by an individual.

After an initial step of goal setting a student has at

first to identify task-relevant knowledge and to

evaluate it with respect to his own knowledge

regarding that goal. Self-evaluation of individual

knowledge is a step that should be performed before

any learning process. For example, if the task is to

acquire expert knowledge, the structure of an

individuals' knowledge as represented in an

individual knowledge map may be compared with

the knowledge structure of an expert as represented

in an expert map. The potential of knowledge maps

as means for diagnosing individual structures of

knowledge has been shown in a variety of empirical

studies (a.o. Jonassen et al., 1997). In self-regulated

learning scenarios the particular contribution of

computer-based concept maps is that they may

support self-assessment (Shavelson, Lang, & Lewin,

1994; Kommers & Lanzing, 1997).

A concept map may be seen as an oriented graph

where vertexes are represented by concepts and

edges are represented by verbs. Within e-Learning

platform for each proposition from the concept map

may will be represented by an weighted edge and

will have associated a number of quiz questions.

Under these circumstances we have created an

algorithms for building the associated graph of a

concept map. The parameters of edges are

continuously updated as the student answers quizzes.

In the experimental part of the paper there will be

presented the obtained graph. Each edge in the graph

will have four parameters: the weight, the total

number of questions, the correctly answered

questions and the wrong answered questions.

Knowledge evaluation procedure takes into

account the parameters of edges from the associated

graph of concept map. The weight of an edge is set

by the domain knowledge expert from a scale from 1

to 10 where 1 means very simple proposition and 10

means very hard proposition. All other parameters

take different values according with learner’s

experience. In the experimental part there will be

presented the formulas that synthesize the

knowledge level of the learner.

The analysis of concept’s map associated graph

represents the core part of decision support system

that runs along the e-Learning platform. The

architecture of the decision support system is

presented in figure 1.

Figure 1: Functionality of Decision Support System.

Chapter A has associated a concept map build by

a domain expert. From the concept map a

transformation procedure creates the general graph

of the chapter. In this graph, each sentence becomes

an edge, weighted by the domain expert. Besides the

associated weight, each proposition has associated a

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set of quiz questions that are to be answered by

learners.

When the learner starts answering questions, the

Decision Support System starts building learner’s

associated graph. This graph represents the input

data for the calculus procedure that assesses the

knowledge of the students regarding chapter A.

Whenever a student logs in the Decision Support

System builds the learner’s associated graph such

that at request the knowledge status will be delivered

in the form of an annotated concept map.

3 EXPERIMENTAL RESULTS

The experimental results were obtained on Tesys e-

Learning platform (Burdescu and Mihaescu, 2006).

On this platform there was set an Algorithms and

Data Structures discipline. The tests were performed

for the chapter named Binary Search Trees.

The concept map for Binary Search Trees is

presented in figure 2. It contains 16 concepts, 11

linking phrases and 16 propositions.

The concepts are presented in table 1.

Table 1: List of Concepts.

Id Concept Id Concept

C1 BST C9 Right child

C2 Dynamic Structure C10 No child

C3 Node(s) C11 Root

C4 Traversed C12 Leaf

C5 Key C13 Preorder

C6 Parent C14 Inorder

C7 Child C15 Postorder

C8 Left child C16 Ascending order

The list of propositions with two concepts and

one linking phrase is presented in table 2. The list of

propositions with three concepts and two linking

phrases is presented in table 3.

Table 2: List of propositions with two concepts and one

linking phrase.

Id Concept Linking phrase Concept

P1 BST is Dynamic

Structure

P2 BST is made of Node(s)

P3 Node has key

P4 Node is Parent

P5 Node is Child

P6 Parent is greater than Left child

P7 Parent is smaller than Right child

P8 Node may have Left child

P9 Node may have Right child

P10 Node may have No child

There is one proposition with five concepts and

four linking phrases:

“BST” may be

“Traversed” in “Preorder”

determines

“Key” in “Ascending Order”. The

concepts are bolded and put between quotation

marks, while linking phrases are italic and

underlined.

Table 3: List of propositions with three concepts and two

linking phrase.

Id C LP C LP C

P11 Node without parent is root

P12 BST may be traversed in Preorder

P13 BST may be traversed in Inorder

P14 BST may be traversed in Postorder

Once the concept map has been built the general

graph of the chapter may be created. In this graph,

each proposition will become an edge that links the

first concept and the last concept. The domain

knowledge expert will assign a weight for each edge.

While the students answers questions the number of

correct and wrong answers will determine the

knowledge weight of that edge.

If:

W- is the weight of the edge

CA – is the number of correct answers

WA – is the number of wrong answers

N – the number of questions

Than

KW – is the knowledge weight of the edge

and

100

∗

−

WACA

Under these circumstances the knowledge

weight may also be negative. At any time there may

be estimated the overall knowledge level of the

learner as the ratio between overall knowledge

weight and overall weight.

Figure 2 presents the general graph associated

with the concept map.

The algorithm transforming the Concept Map

into General Graph is strait forward. Each

proposition becomes an edge with an weight

assigned by domain knowledge expert. In this way it

was obtained the Binary Search Tree General Graph.

Once the General Graph has been set up the

professor has to set up the quiz questions for the

chapter. For each edge in the graph it will

correspond a certain number of quiz questions.

There is no specification regarding the number of

quiz questions but a minimum (e.g. five) number is

still required. Once the quiz questions have been set

up, for each student there may be constructed

BUILDING A DECISION SUPPORT SYSTEM FOR STUDENTS BY USING CONCEPT MAPS

133

Figure 2: Binary Search Tree Concept Map.

Figure 3: Binary Search Tree General Graph.

the learner’s associated graph. This graph will have

associated with the edges the history of correct and

wrong answered questions. The Calculus engine will

reconstruct an Annotated Concept Map which will

present to the learner the current status of his

knowledge level at Concept level. In this way, the

learner will have an exact overview of his

knowledge level regarding that chapter.

The Annotated Concept Map may represent the

important information for learner in having a

decision regarding which part of the chapter needs

more study.

Table 4 presents a sample of the setup of the

Binary Search Trees chapter.

Table 4: Sample setup of BST chapter.

Proposition Weight No. of questions

P1 10 8

P2 4 7

P3 7 6

P4 3 5

P5 2 7

Table 5 presents a sample of the of the values of

the Learner’s Associated Graph corresponding to

BST chapter.

The values from table five are marked in an

Annotated Concept Map that is finally presented to

the learner. The Annotated Concept Map is the final

outcome of the Decision Support System and is

supposed to guide the learner regarding the

necessary future efforts.

Table 5: Sample values for Learner’s Associated Graph.

Proposition

(Weight)

No. of

questions

CA WA KW

(%)

P1 (10) 8 3 2 1.25

P2 (4) 7 4 2 7.14

P3 (7) 6 1 3 -4.76

P4 (3) 5 3 1 13.3

P5 (2) 7 2 4 -14.2

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4 CONCLUSIONS AND FUTURE

WORK

The paper presents the structure and functionality of

a Decision Support System that runs along Tesys e-

Learning platform.

Tesys e-Learning platform has been designed

such that on-line testing activities may me

performed as they were set up by course managers.

It has been created a Concept Map for a Binary

Search Trees chapter within Algorithms and Data

Structures course. The Concept map has been the

staring point in creating a set of quiz questions. Each

quiz question refers to a certain proposition from the

concept map.

For the designed Concept Map it has been

derived a general graph in which edges are

represented by the propositions from the Concept

Map. For each edge the domain knowledge expert

(i.e. course manager) assigned a specific weight.

After the setup has been put in place, the learners

started using the platform. At request, from the

general graph there was derived the learner’s

associated graph and on this one there may be

performed calculus such that the level of knowledge

regarding the chapter may be estimated at

proposition level. These calculus represent the

annotations in the original concept. The annotated

concept map represents what the learner finally

receives upon his request.

The calculus logic computes the knowledge of

the student regarding the chapter as a knowledge

weight. This weight is computed as a function of

proposition’s weight, number of questions assigned

to that proposition, the number of correct answered

questions and number of wrong answered questions.

This whole mechanism represents the

functionality of a decision support system that runs

along the Tesys e-Learning platform.

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