DEVELOPMENT OF A SUPPORT SYSTEM FOR UNIVERSITY

COURSE SELECTION USING SEMANTIC WEB TECHNOLOGY

Minoru Nakayama and Jun Hoshito

CRADLE (The Center for Research and Development of Educational Technolgy)

Tokyo Institute of Technology, O-okayama 2–12–1, Meguro-ku, Tokyo, 152–8552, Japan

Keywords:

Web system, Semantic web, Course selection, Learning support, University education.

Abstract:

The authors proposed a support system for university students to create their own course schedules using se-

mantic web technology. The system provides course information, such as syllabus, students’ assessment scores

and reviews, which are RDF based ontology, while participants create their own course schedules. A proto-

type system was developed for course selections of two departments, and its effectiveness was determined.

As a result, the number of courses selected increased signiﬁcantly, and participants’ subjective responses were

encouraged when they consulted the system.

1 INTRODUCTION

The university students have to have core courses, but

they can also choose some optional courses which are

basic or advanced. Students seek to learn several dis-

ciplines in response to the needs of society. There-

fore, students seek a variety of information about

courses. There are some restrictions however, such

as the number of optional courses which can be cho-

sen. As a result, the course selection problem can

be deﬁned as an optimization task where the effort

taken can beneﬁt each student at the beginning of ev-

ery academic term. Students are afraid to lose any

course credits despite their academic efforts, so they

exchange opinions about their experiences and write

their impressions of courses using anonymous web

sites (Syushoku, 2002). This suggests that students

have to browse a number of web sites which include

various sources of information.

Some supporting systems have already developed

and considered the validation of curricula (Melia and

Pahl, 2007; Baldoni et al., 2007). Also students’ as-

sessment results and reviews for courses are required

for their satisfaction with courses. For these opera-

tions, semantic web technology (Berners-Lee et al.,

2001; Ossenbruggen et al., 2002) can be a powerful

tool for gathering related documents and reproducing

referable data. Semantic web technology has been ap-

plied to numerous educational tools which are based

on speciﬁc ontology (Kasai et al., 2005b; Kasai et al.,

2005a).

In this paper, we propose a support system for uni-

versity students to create their own course schedules

each term using semantic web technology, where the

effectiveness of the system to promote course selec-

tion is examined while participants review their own

behavior. For this purpose, we have developed a pro-

totype of the system and conducted an evaluation ex-

periment.

2 SYSTEM DEVELOPMENT

2.1 Course Selection Support System

The proposed system for course selection support is

illustrated in Figure 1. The procedure for course se-

lection is displayed as a ﬂow chart on the right side of

the ﬁgure. In the ﬁrst step, students set their own plan-

ning policy for course registration, and they survey

course information. During this step, they need pre-

cise information and systematic support. Students re-

view their own course schedules and repeatedly revise

them using the system. To show the appropriate infor-

mation on a web browser, a semantic web system has

been developed. The system consists of the ontology

for university courses and some databases. Here, the

ontology deﬁnes the logical relationship between re-

quired data formats and databases (Mizoguchi, 2006).

The databases consist of following data:

• Formal course information (syllabus)

389

Nakayama M. and Hoshito J.

DEVELOPMENT OF A SUPPORT SYSTEM FOR UNIVERSITY COURSE SELECTION USING SEMANTIC WEB TECHNOLOGY.

DOI: 10.5220/0001816603890392

In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page

ISBN: 978-989-8111-81-4

Syllabus

Course assessment

Student's

comments

Course

assessment

scores

Course ontology

University

curriculum data

Web sites for

course information

Planning for

course registration

Course selection

Evaluation of

course schedule

Final course schedule

Information

presentation

Semantic Web

Figure 1: A diagram of the system.

Class period(1-8)

Tuesday

Monday

Control Engineering (3rd term)

Control Engineering (5th term)

Industrial Management (3rd term)

Industrial Management (5th term)

Course name

with assessment score

Color illustration

Red = <= 3.0

Yellow = >3.0 - <6.0

Blue = >6.0

Course Informtion

Student’ s comments

Syllabus

Figure 2: Screen shot of the system.

This is provided by university ofﬁces, and in-

cludes course names, information about teaching

staff, academic credit, course content, the require-

ments for students, and an assessment guideline.

Students can browse these on the university’s web

site.

• Course assessments

Assessments for each course are from anonymous

evaluations by students. These evaluations are

averaged to allow comparisons of usefulness be-

tween courses.

• Student’s reviews

Student’s comments about courses: Impressions,

recommendations and criticisms are noted.

The ﬁrst type of data can be obtained from the uni-

versity ofﬁce, and some universities provide these via

their web sites. As the rest of the databases are dis-

tributed across the Internet (Syushoku, 2002), a pro-

cedure for referring to other web sites is required.

Also, since their data formats are not uniﬁed, they are

transformed into resources for the semantic web us-

ing RDF (Resource Description Framework) schema

(Kanzaki, 2005). The target information is extracted

from the RDF ﬁles using a program with SPARQL

(Simple Protocol And RDF Query Language) (Kan-

zaki, 2005), and is converted to ﬁles with XML tags

to facilitate browsing.

2.2 Web Interface of the System

A screen shot of the web interface for a course se-

lection support system using semantic web technol-

ogy is displayed in Figure 2. The left vertical sub-

window shows course information such as the syl-

labus. The horizontal column on the top-right side

of the panel shows class periods (1-8 per day), the

mid-right side shows the names of course available on

Monday, and the bottom-right side of the panel shows

names of course available on Tuesday. Here, this im-

age shows courses for two departments: Control En-

gineering and Industrial Management. This informa-

tion is commonly displayed on systems both with and

without semantic web technology. When the system

uses semantic web technology, review information is

also displayed in the left side vertical window. The

WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies

390

mean value of students’ assessment are indicated next

to course names, and the color of the background of

the course name is illustrated using a color to indicate

the level of usefulness, according to students’ evalua-

tions. This mean value is represented by three colors:

red = less than 3.0, yellow = from 3.0 to 6.0, and blue

= higher than 6.0. These color designations, which are

based on peers evaluations, may attract participant’s

attention during the selection of courses. When users

click the right mouse button of a course name, the in-

terface sends a query message to the semantic web

system and displays information about the course.

The system was developed in an integrated devel-

opment environment (eclipse 3.3), using JAVA.

The semantic web function was developed using

a Jena framework. This can work on Windows and

Linux platforms. Therefore, the system can be ac-

cessed using any type of browser.

3 EXPERIMENT

3.1 Experimental Design

To determine the effectiveness of the semantic web

technology system, the performance of student’s

course schedule creation process is tested during an

experiment. Participants in this experiment were

asked to compare courses of two departments and

then create their own course schedules using the sys-

tem. The participants were 13 undergraduate level

students who were studying in the Control Engineer-

ing department. As seniors, they knew most of the

content of courses in their department. In this ex-

periment, the support system displayed all courses

in the spring term for the Control Engineering, and

Industrial Management departments. If the courses

were recommended for sophomores or junior stu-

dents, these recommendations were clearly displayed

as spring term courses (3rd and 5th terms) in Fig. 2.

Our hypothesis is that students select some In-

dustrial Management courses in addition to courses

in their own department (Control Engineering), since

most students would like to extend the range of their

education to various disciplines when the system

can provide sufﬁcient course information for them to

make informed decisions.

It was not easy to gather enough information about

all of the courses which were displayed on the system

in this experiment. Therefore, the review comments

for some courses surveyed for this experiment used

a reviewing system developed in advance. For the

courses in the Industrial Management department, 8

disparate senior students who were students in that

5.85

4.38

2.08

4.08

7.92

8.46

Control

Engineering

Industrial

Management

Total number

of courses

Without Sematic Web

With Semantic Web

0 2 4 6 8 10

Number of courses

p<0.01

Figure 3: Mean number of courses chosen.

department rated all of the courses. Their scores

and review comments were stored in an experimen-

tal database.

3.2 Procedure

All 13 participants were asked to create their own

course schedule twice, once using the system and

once using a system without a semantic web function.

The procedure was as follows:

1. Instruction of the experiment’s aim to partici-

pants.

2. Questionnaires about learning attitudes (1st)

3. Creation of a course schedule without a semantic

web function system

4. Break

5. Creation of course schedule with a semantic web

function system

6. Questionnaire about learning attitudes (2nd)

To evaluate system performance, the following met-

rics were gathered and analyzed.

• The number of courses selected

The number of courses of each department which

were selected (steps 3 and 5).

• Questionnaire about learning attitudes

The participants were asked to rate their own

learning attitudes twice using 6 questionnaires

with 4 point scales, before and after the experi-

ment (steps 2 and 6).

4 RESULTS

4.1 Effect on Course Selection

The number of courses chosen during each experi-

mental session is summarized in Figure 3. For the ﬁrst

session (without semantic web), most courses chosen

were related to Control Engineering, the participants’

DEVELOPMENT OF A SUPPORT SYSTEM FOR UNIVERSITY COURSE SELECTION USING SEMANTIC WEB

TECHNOLOGY

391

p<0.01

p<0.05

Before

After

Mean scale score

1 2 3 4

Interest in

the other department

Voluntary course

selection

Interest in

Industrial Management

courses

Easiness of planning

course schedules

Time devoted to

the other department

Strength of

perceived utility of

taking a course

Figure 4: Mean score for learning attitude questionnaires.

own department. The number of courses for Indus-

trial Management was less than 30% of total courses

chosen.

When participants could refer to course informa-

tion using a semantic web, the number of courses

chosen for both departments were comparable. This

suggests that the number of courses chosen for Con-

trol Engineering decreased signiﬁcantly (t(12)=3.4,

p < 0.01), while the number for Industrial Manage-

ment increased signiﬁcantly (t(12)=3.1, p < 0.01).

The total number of courses chosen were comparable

because there was a restriction for choosing courses.

This suggests that course selection shifts to an

other department’s courses when the semantic web

shows detailed course information.

4.2 Attitude Change of Participants

The response for learning attitude questionnaires is

summarized in Figure 4. At the beginning of the ex-

periment, the means of all responses were distributed

in the neutral range, between 2 and 3 on a 4 points

scale. At the end of the experiment, the means for re-

sponses of 4 out of 6 questionnaires responses were

higher than 3.0. The scores for 4 questionnaires were

signiﬁcantly higher than the previous scores. There-

fore, this system may encourageparticipants’ involve-

ment, such as is shown in ”Interest in the other depart-

ment” (t(12)=3.4, p < 0.01), ”Voluntary course selec-

tion” (t(12)=3.3, p < 0.01). For ”Interest in Industrial

Management department courses” (t(12)=2.6, p <

0.05) and ”Easiness of planning course schedules”

(t(12)=2.5, p < 0.05), the means for the responses

after the experiment using semantic web technology

were also signiﬁcantly higher than were the means for

the 1st responses.

This suggests that the system support may affect

student’s attitudes and actions regarding course selec-

tion. The participants had some interest in courses of

the other departments, however, further study of this

will be required.

5 CONCLUSIONS

To determine the effectiveness of a support system,

which can provide course information including stu-

dents’ assessments and reviews, to help university

students create their own course schedules using se-

mantic web technology, a prototype system was de-

veloped for course selection support for two depart-

ments: Control Engineering and Industrial Manage-

ment.

In the results, the number of courses in Industrial

Management which participants in the Control Engi-

neering department chose increased signiﬁcantly, and

subjectiveresponses about their attitudes and interests

were encouraged when they consulted a system which

had semantic web functions.

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