TOWARDS LIBRARY SUPPORTED COLLABORATIVE

LEARNING

Toshiro Minami

Kyushu Institute of Information Sciences, 6-3-1 Saifu, Dazaifu, Fukuoka 818-0117, Japan

Kyushu University Library, 6-10-1 Hakozaki, Higashi, Fukuoka 812-8581, Japan

Keywords: Library Marketing, Digital Libraries for e-Learning, Collaborative Learning/Filtering, Data Mining.

Abstract: Due to the development of information and communication technology our information environment has

greatly changed. People’s requests to libraries have been changing along with it. As a result the library

materials are changing from printed ones to network media. Considering such a circumstance we anticipate

that learning assistance should be one of the major library services in the future. In this paper, we propose a

model of collaborative learning in which the library users, or patrons, are implicitly helping each other as

they learn. The basic idea of this approach to collaboration comes from the mechanism of collaborative

filtering. Not only the learners but also the librarians are supposed to help the learners with advising their

learning materials and managing their learning processes; which is considered to be a style of extended

reference service by libraries. We anticipate that by mixing up the traditional reference services and the ones

that support patrons’ learning processes the future libraries would be able to keep existing as reliable

organizations and librarians would be considered to be their reliable supporters.

1 INTRODUCTION

The aim of this paper is to propose a model of

library supported (implicit) collaborative learning

(CL) system and to demonstrate its importance.

The spreads of information and communication

technology (ICT) is one of the most influencial

changes for our society in these couple of decades.

Accessing to the Internet with mobile phones

becomes very popular now. We are able to access a

wide variety of information anytime, anywhere.

Libraries have been playing an important role in

our society as organizations that provide us with

services for reading materials (Ranganathan 1957).

Due to the development of ICT, the library materials

are changing from printed ones to digital media

privided via network. Then what sort of library

service is required in such an ICT age? Our answer

is that the services that support the library users, or

patrons, with their learning. People in these days are

eager to keep studying. Libraries have advantages

for playing such a role because they have good

experience in assisting patrons’ learning.

Our goal is to construct a system so that the

librarians support their patrons based on their

professional skills and the data that are collected as

the patrons learn. This model is a kind of CL in the

sense that the data are used for helping others. There

are two types of CL; explicit and implicit. Explicit

CL is the one that learners collaborate by explicitly

communicating each other by using a chat system,

for example (Ueno 2005). Implicit CL is the one that

learners do not communicate explicitly; they just

study. The data are automatically collected as they

learn, shared by them, and used for helping them.

2 COLLABORATIVE LEARNING

2.1 A Model of Collaborative Learning

System

Figure 1 illustrates an overall organization of the

collaborative learning system poposed in this paper.

The left part indicates the system users who learn a

subject. Patron 1 studies materials based on the

library’s recommendation. Patron 2 uses an

intelligent bookshelf (IBS) (Finkenzeller 2003)

(Minami 2008) (Zhang and Minami 2007) that is

connected to the home server. The learning server

detects what books are taken out and returned at

which times. Such timestamp and other data are used

431

Minami T. (2009).

TOWARDS LIBRARY SUPPORTED COLLABORATIVE LEARNING.

In Proceedings of the First International Conference on Computer Supported Education, pages 431-434

DOI: 10.5220/0001974704310434

 SciTePress

for supporting Patron 2 with his study.

The middle part is the library, which has the

central collaboration server. Each patron is

connected to this server. All, or some, of the IBSs

access the server. Further, the librarians also access

the server in order to carry out their jobs. Reference

librarians use it for collecting data about reference

services as well as use it for looking for the

information and knowledge provided by the server.

Collaboration librarians also use the server. The

system provides them with information obtained by

analysing the raw data. The collaboration librarians

can change and/or add extra knowledge or policies

that specify how to use the knowledge of the server.

Libraries are also working collaboratively. They

have already been working cooperatively such as in

inter library loan (ILL) service.

2.2 Implicit Collaborative Learning

Learning Assistance with SASS

SASS (Searching Assistant with Social Selection)

(Oda and Minami 2000) is a system which was

planned and developed as a keyword

recommendation system for information searchers

(Fgiure 2). The input area is in the upper pane. It

also contains the given keyword(s), the type of

recommendation among several candidates. One

type is based on the relatedness of keywords.

Another example one is to find keywords which are

used in combination with the given keyword and

also which are closely related to the keywords that

are used in combination with the given keyword.

It is a surprise for us when we found SASS can

be used as a learning assistance in a sense that we

can recognize our knowledge level with the

recommended keyword list from the system. They

are related to the original keywords that are given by

the user in some senses. So we try to find the reason

how the recommended keyword is related to the

original keyword and why other searchers used them.

If we are can guess the reason then we can think

ourselves that we know well about the field that is

represented by the keywords. If we can not explain,

then we may consider that we do not know the field

sufficiently well. Thus we will set a goal of studying

this field in learning the recommended keywords;

starting with learning what the term means and then

learning what it is related to other terms and

concepts in the target field.

Figure 2: A Screen Shot of SASS.

Figure 1: A Conceptual Model of Collaborative Learning System Supported by Libraries.

Intelligent Bookshelves

(IBSs)

IBS

Collaboration

Network of Libraires

Reference

Librarian

Collaboration

Librarian

Lib

rar

Collaboration

Server

Patron 1

Patron 2

Recommended

Keywords

Given Keyword(s)

and Additional

Information

Keyword

Log

CSEDU 2009 - International Conference on Computer Supported Education

432

Figure 3: A Screenshot of WebLEAP.

Learning Assistance with WebLEAP

WebLEAP (Web Language Expression Assistant

Program) (Yamanoue, Minami and Ruxton 2000) is

a system that helps users with writing articles in

English (Figure 3). The input field is in the topmost

part, where the user types an English sentence or an

expression as a list of words. Then the system

replies with occurrence numbers of combinations of

consecutive words (n-grams) in the expression. The

occurrence numbers come from a Web search engine

specified in the system. The users are supposed to

read and compare the occurrence numbers and try to

find out what they mean including if the expression

he/she has given is right or not, if it is appropriate, if

it is popularly used, and so on.

In order to put appropriate conclusions by

reading and comparing the numbers, we have to

think hard, which is very good for us in training

ourselves in our studying of English. This is another

type of system that is good for educating ourselves.

2.3 Learner Profile Estimation

Figure 4 illustrates an example set of learning

materials M1, M2, ..., M6 together with the arrows

for representing their dependencies. Suppose that

these materials are the ones a learner has to study in

order to master a subject field. Let us take the

material M1 for example. There are two arrows

toward the material M1, which means that in order

to study the material M1, the learner must have

studied both of the materials M2 and M3.

Material Construction

The original material structure comes from the

knowledge and decision from experts in the target

field. We can refer textbooks for this. A lot of

companies have already provided such coursewares.

In addition to logical dependencies, we may use

other types of dependencies. Suppose, for example,

the material M3 has no direct dependency with M4.

We also suppose that M3 and M4 both contain

proofs that use mathematical inductions. We further

suppose that the mathematical induction used in M4

is simple and easy to understand, while the one in

M3 is somewhat difficult to understand.

We suppose further that it becomes easier to

understand the proof in M3 if the learners have

already studied the proof in M4. In such a case we

may consider that M3 is dependant with M4 not in

logical sense but in the sense of learning procedure.

In such a case we can put another dependency arrow

from M4 to M3. It also could happen that such

hidden dependency relationship may be detected

from the log data of patrons’ learning procedures.

Let us take the similar supposition as the previous

paragraphs. Then the learners may feel difficulties if

they study M3 before studying M4 and they would

feel it easier to study M3 after studying M4. This

difference may appear in study time and/or

achievement test of M3 or M4.

Learner’s Profile

In order to give advice to the patrons as they learn,

the librarians need to recognize the learning status,

or profile, of the patrons. In this paper we take the

set of learning materials and the patron’s

achievement degrees as the first approximation for

patrons’ profile data.

The achievement degree is from 0 to 1, or 0% to

100%, where 0 means that the learner has not

studied the material yet, or even after learning he/she

has failed to solve all the questions in the

achievement test or in some other methods of

evaluation. Degree 1 means that he/she has solved

all the test and he/she can go forward to the next step.

For other methods of evaluating the achievement

degree, we can take one described in Section 2.2; for

example, the system or librarian put a list of terms

that are supposed to have learned in the study topic

Figure 4: An Example Structure of Material Dependencies

and a Path of Learning for it.

TOWARDS LIBRARY SUPPORTED COLLABORATIVE LEARNING

433

and ask the learner to explain what they mean, how

two terms are related each other.

Study time is also a good index for assessing the

learner’s achievement. If the learner spends a lot of

time in studying, he/she may be in difficulty in the

studying material. Suppose, for example, the

learner’s study time is 20% longer than the standard

study time of the material then his final achievement

degree for the material may be calculated as the raw

degree times 0.8 or in other method.

It would be good to combine some types of

achievement degrees and decide the learner’s final

degree for the material. It is not necessary to require

the degree of 1 to go forward to the next step. We

put some threshold value, say 0.8, and the degree is

more than this value, the learner can go forward to

the higher level.

2.4 Material Recommendation

A learning plan is recommended by the collaborative

learning system. Then the reference librarian checks

the plan and modifies it if necessary. The final plan

will be decided upon negotiating with the learner

himself/herself.

Due to the dependency constraint, the possible

study order is limited. For example, the set of study

material in Figure 4 has 16 possible study orders. An

example order [M4, M5, M2, M6, M3, M1] is

shown in the figure. How can the system evaluate

and choose a possible study order? A possible way is

to use importance of study order. Let us suppose the

importance is in the order of M1, M2, M3, M4, M5,

and M6. The possible first material to start with is

either one of M4, M5, or M6, because other

materials are depending on some other materials in

this set. From the importance order the material M4

is the most important, so the system chooses M4 as

the first study material. Then the next material to be

studied is either M5 or M6 and M5 is more

important to study than M6, thus M5 is the next. As

M4 and M5 have studied, the next candidates are

M2 and M6, and M2 is more important than M6, so

the system takes M2 as the next one. By repeating

such processes the recommended study order

becomes the one in Figure 5.

3 CONCLUDING REMARKS

In this paper, we proposed a new library service

model of implicit collaborative learning. A key

feature is that the data are automatically collected as

a patron learns with the system, stored, and are used

for assisting all the patrons. Another important

feature is that not only the system but also the

librarians are involved in assisting the patrons with

providing their expertise and make final dicisions on

the ways of assisting. Also we discussed about the

methods of recommending study materials,

including their study orders.

The CL system proposed in this paper is an

education system in two different aspects. One is

that for patrons, of course. This is the major aim of

the system. Another one is that for the librarians.

They can learn as they use the system and help the

patrons with their learning. Even though this aspect

is rather a sub-aim, it is very important for both sides.

One of the biggest aims of this paper is to

suggest a direction to future library service when

libraries are facing difficulties in finding the way to

keep being as reliable organizations for our society.

The next goal of this research is set to design the

CL system in detail, implement, and demonstrate its

usefulness through experiments.

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Minami, T., 2008. A Design for Library Marketing System

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Ranganathan, S. R., 1957. The Five Laws of Library

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Learning, The Annual Report of Educational

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Japanese)

Yamanoue, T., Minami, T., Ruxton, I., 2000. Using the

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Program) to Write English Compositions, in FLEAT

IV, The 4

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