BUILDING RECOMMENDATION LEARNING ON E-LEARNING SYSTEM BY WEB MINING

Mu-Hsing Kuo & Chun-Hsien Chuang

Department of Information Management, Choayang University of Technology,

168, Jifong East Road, Wufong Township, Taichung County 41349, Taiwan ROC

mhk@cyut.edu.tw & s9314644@cyut.edu.tw

ABSTRACT

Today the amount of e-Learning information is incredible. To find out the

learning information from searching is an annoying and tedious task. Moreover, if

only applying chasing hyperlinks to find the relevant information of learning profile,

it may be daunting. In order to overcome such a problem, the recommendation

learning system is required. The user’s learning route is given and then provides the

relevant learners useful messages through dynamically searching for the appropriate

learning profile. The recommendation learning system, which is based on e-Learning

system, provides an effective learning profile for learners via the Agent. The learner

can study effectively instead of lavishing time on so many curricula. The Agent

recommends the learner ideal courses. The recommendation system has been used in

Electronic Commerce for analyzing the customers’ track record in order to increase

the purchasing power of the websites through introducing the commodities that the

customer may buy again. E-Learning system is applied on the basis of the method.

This paper recommends learners the studying activities or learning profile through the

technology of Web Mining with the purpose of helping them adopt a proper learning

profile. Furthermore, in order to enhance the performance of e-learning system, we

describe how to design the guiding system of recommendation e-learning web pages.

Therefore, we refer the Agents’ idea and combine with their ideal description to

accelerate the learning speed in the recommendation e-learning system.

Keywords: e-Learning, Web Mining, Agent, Recommendation system, Learning

profile.

1.INTRODUCTION

In the recent years, e-learning system has become more and more popular.

Sharable Content Object Reference Model (SCORM) has become the most popular

international standard. SCORM standard lets courses have compatibility so courses

are shared extensively on e-Learning System. With the development of e-Learning,

massive amounts of learning courses are available on the e-Learning system. When

entering e-Learning System, the learners are unable to know where to begin to learn

with various courses. Therefore, learners waste a lot of time on e-Learning System,

but don’t get the effective learning result.It is very difficult and time consuming for

educators to thoroughly track and assess all the activities performed by all learners on

all these tools. Moreover, it is hard to evaluate the structure of the course contents and

its effectiveness on the learning process. Resource providers do their best to structure

the content assuming its efficacy (Za¨ıane, O. R.2001.).

In order to overcome such a problem, the recommendation learning system is

required. The user’s learning route is given and then provides the relevant learners

useful messages through dynamically searching for the appropriate learning profile.

The recommendation system has been used in Electronic Commerce for analyzing the

customers’ track record in order to increase the purchasing power of the websites

through introducing the commodities that the customer may buy again. E-Learning

system is applied on the basis of the method. This paper recommends learners the

studying activities or learning profile through the technology of Web Mining with the

purpose of helping them adopt a proper learning profile.

In course of e-Learning at present, there are two kinds of study: Flow and

Choice. As show in table 1.

Flow ：This element enables the learner to go in sequence, the learner can’t

skip any chapters, and the learner can go only one step front or back. Flow

is like a general student's study in the classroom, one chapter continues with

the previous study chapter and s/he can't skip chapters to learn other

courses.

Choice：This element enables the learner to select any chapters randomly.

This kind of study is like ‘‘tree’’ structure, the learner can see the catalogue

of complete course, s/he studies according to his/her own demand, this kind

of study is a learning way of skip, the learner only needs to study unfamiliar

courses, courses already learnt can be omitted, so s/he needn't waste time on

courses already learnt to save time.

This e-Learning System provides a layout of the Run-Time Environment,as show

in Figure1, which conforms to the Sharable Content Object Reference Model

(SCORM) standard formulated by ADL. It shows the complete course menu on the

left, learners can select the title of the course according to their own demand. This

kind of study is called ‘‘choice’’.

Figure1. The ADL of SCORM Run-Time Environment

Table 1. Two kinds of study: Flow and Choice.

Comparative Study the way

Flow studying Choice studying

Information

Procedure

introduction Study with the way of

next step

Freely choose the study

contents or skip to study

Degree of freedom low high

spend time long Shorter, faster

Degree of

absorption

Complete Some

Learner Beginner The advanced

Kinds of studyFirst time or complete Review, strengthen,

complete

Besides Flow, there is another way of study now—Choice. The way of Choice is

more changeable thus can't make Association Rule with general mining technology.

Therefore, the research refers to ICI algorithm (Hung, Jen-Peng & Lin, Shiau-Wei.

2004.)and revises it as the mining, which is adequate for e-Learning System to

analyze the study routes of learners.It would be very useful for learners if the system

could automatically guide the learners to study and intelligently recommend them to

support and to improve on-line activities or resources.

This paper describes the design and recommendation of e-learning system. We refer to

the idea of the Agent and combine his/her way of describing the models to improve

the functions of learning system. Hope it can enhance the learning speed on the

recommendation learning system.

In this paper, we consider to construct a learning environment that can

automatically recommend on line. The resources are from study history and use data

mining technology namely association rule mining. The following section reviews

related work in the context of e-learning. Section 3 discusses the recommendation

system and Association Rule mining how to be built as a recommendation system.

Finally, Section 4 presents some conclusion.

2. RELATED WORK

There is LMS(Learning Management System, LMS) in the present e-learning

platform so that LMS will record the learner's Learning Profile, which can master the

learner's study schedule.

The route where the learner browses through the web pages will be noted down in

Web log, carries on the technology of Web mining through Learning Profile and Web

log, and analyzes from the materials related to Association rule. It can be found the

best learning profile from this information. These learning profiles combine with the

Agent and put them on the learning website. Furthermore, the Agent recommends the

function of learning profiles on learning website. Therefore, the learner will acquire a

better learning profile.This chapter briefly illustrates the relevant contents including:

e-Learning, Learning Profile, Agent, Data mining and Association rule.

2.1 E-LEARNING

e-Learning : The online delivery of information for purposes of education,

training, or knowledge management.

In the Information age skills and knowledge need to be continually updated and

refreshed to keep up with today’s fast-paced study environment. e-Learning is also

growing as a delivery method for information in the education field and is becoming a

major learning activity. It is a Web-enabled system that makes knowledge accessible

to those who need it. They can learn anytime and anywhere. e-Learning can be useful

both as an environment for facilitating learning at schools and as an environment for

efficient and effective corporate training, as shown in the Cisco case. Liaw and Hung

(Liaw, S. & Hung ,H.2002.) describe how Web technologies can facilitate learning.

E-learning can save money , reduce travel time , increase access to experts , enable

large number of students to take classes simultaneously , provide on-demand

education , and enable self-paced learning . (Delahoussaye, M.& Zemke ,R. 2001.)

2.2 LEARNING PROFILE

Learning Profile help you to keep a record of your current knowledge and

understanding of e-learning and e-learning activities.

This amalgam of on-line hyper linked material could form a complex structure

that is difficult to navigate. However, learners could follow different paths generating

a variety of sequences of learning activities. Often this sequence is not the optimum

sequence, and probably not the sequence intended by the designer. It is very difficult

to assess the on-line learning activities in a web-based system.Learning Profile writes

down the learner's study situation. We can find out hidden information and explore the

study course concerned from these records.

2.3 DATA MINING

Data mining : The process of searching a large database to discover previously

unknown patterns; automates the process of finding predictive information.

We find out the useful information from the skills of mining among various Web log

data.Data mining tools identify previously hidden patterns in one step.An example of

pattern discovery is the analysis of retail sales data to identify seemingly unrelated

products that are often purchased together such as baby diapers and beer.

What distinguish the data that results from web-based activities in general and e-

learning activities in particular are the sheer complexity of the information and the

vast size of the data collected, as well as the fact that simple information extraction is

not possible. Information must be deduced by interactive data mining and associated

visualization techniques. Visualization can be used either to visualize the patterns

discovered by data mining, and thus is for evaluation and interpretation of the

discoveries, or for the visualization of data itself. In this latter case visualization

becomes part of data mining as an interactive process (Fayyad,U., Grinstein,G. &

Wierse , A.2001.).

Data mining is the step in which, using advanced techniques, interesting and

potentially useful patterns are extracted from a set of large, already cleansed data

sources (Han, J. & Kamber ,M.2001). These advanced techniques include automatic

classification of data, clustering, the discovery of associations and correlation between

data, characterization and summarization of data, discovery of discrimination features,

identification of outliers, etc.

Applying data mining techniques on web logs to discover useful navigation

patterns or deduce hypothesis that can be used to improve web applications is the

main idea behind web usage mining. Web usage mining can be used for many

different purposes and applications such as user profiling and web page

personalization, server performance enhancement, web site structure improvement,

pre-fetching, etc. (Srivastava, J. , Cooley,R., Deshpande,M. & Tan, P. 2000.).Find out

the useful and relevant study information in the route through data mining.

2.4.1 WEB MINING

Web mining: The application of data mining techniques to discover meaningful

patterns , profiles , and trends from both the content and usage of Web sites.

To analyze a large amount of data on the Web, one needs somewhat different mining

tools. The term Web mining is used to describe two different types of information

mining. Firstly, Web content mining is the process of discovering information from

millions of web documents. Secondly, Web usage mining, is the process of analyzing

what customers are doing on the Web - that is, analyzing click stream data.In Web

mining, the data are click stream data, usually stored in a special click stream data

warehouse (Sweiger, M.2002.)or in a data mart.

Web usage mining performs mining on web data, particularly data stored in logs

managed by the web servers. The web log provides a raw trace of the learners’

navigation and activities on the site. In order to process these log entries and extract

valuable patterns that could be used to enhance the learning system or help in the

learning evaluation, a significant cleaning and transformation phase needs to take

place so as to prepare the information for data mining algorithms (Za¨ıane, O.

R.2001.). A new web usage mining system dedicated for e-learning is being

developed to allow educators to assess on-line learning activities (Za¨ıane ,O. R. &

Luo ,J.2001.).Web server log files of current common web servers contain insufficient

data upon which to base thorough analysis. The data we use to construct our

recommended system is based on association rules.

Some new experimental tools use data mining techniques to extract hidden

patterns from the large web logs. Systems such as WebSIFT (Cooley,R. ,

Mobasher,B. & Srivastava,J.1997) and WebLogMiner (Za¨ıane, O. R. , Xin,M. & Han

,J.1998.)are sets of comprehensive web usage tools that are able to perform many data

mining tasks and discover a variety of patterns from web logs. One attractive example

is an adaptive web site that makes use of a user’s access history to personalize page

layouts and web site structure automatically (Spiliopoulou,M. , Faulstich L. C. &

Winkler ,K.1999.). Find out the relevant study information in the route through web

mining.

2.4.2 ASSOCIATION RULE

Association Rule mining techniques (Agrawal ,R. & Srikant ,R. 1994.) discover

unordered correlations between items found in a database of transactions.Association

rules are one of the typical rule patterns that data mining tools aim at discovering.

They are very useful in many application domains, but are mainly applied in the

business world as in market-basket analysis. In a transactional database where each

transaction is a set of items bought together, association rules are rules associating

items that are frequently bought together. A rule consists of an antecedent (left-hand

side) and a consequent (right-hand side).Example: I1,I2,I3,….In= Iα,Iβ,……IγThe

intersection between the antecedent and the consequent is empty. If items in the

antecedent are bought then there is a probability that the items in the consequent

would be bought as well at the same time. An efficient algorithm to discover these

association rules was first introduced in (Agrawal,R. , Imieliski,T. & Swami,

A.1993.).

Association rules to understand the behaviour of web users accessing on-line

resources, visualization is paramount. It is not clear how to analyze and visualize web

usage data involving long sequences of on-line activities without loosing the big

picture(Berendt ,B.2002.). There are specific tools for visualizing web data such as

interconnections of web resources (Munzner, T. & Burchard,P.1995.), tools for

visualizing web navigation patterns (Cadez,I., Heckerman, D. & Meek, C.2000.), and

tools to visualize the changes in web sites or web usage over time(Chi,E., Pitkow,J.,

Mackinlay, J., Pirolli,P. & Konstan, J.1998.). Through these ways, finding out what

the meaningful and related study routes. The data we use to construct our

recommender system is based on association rules.Count the learners’ surfing records,

learning way and testing marks and finding out the connection between courses with

Association rule to calculate the learning profiles of the coming learners.

The data we use to construct our recommendation system based on association

rules. In this article, it will not utilize Apriori but Association rule; hence, it is the

shortcoming of Apriori because it takes a lot of time and wastes the memory space.

We need to offer information to learners immediately. In addition, we refer ICI to

perform the algorithm (Za¨ıane, O. R.2001.), and revise into mining technology on e-

Learning system appropriately. As a result, the recommendation system becomes

instant message to offer users the information.

2.5 AGENT

Agents are used as a recommendation action that products and other items are

applied together. In the field of electronic commerce, given the lucrative prospects, a

significant research has been made to devise elaborate methods to take advantage of

customers’ accesses and purchase behaviors in order to enhance the purchasing

experience and customer satisfaction by user profiling and smart recommendations,

and thus increase profit. Recommences are used to boost sales by displaying products

or services a consumer is likely to be interested in.

Integrated web mining is a recommendation system that suggests actions or

resources to a user, often called recommender agent. This software agent ”learns”

from past activities of one user or a group of users, and predicts activities or pages

that a given user might be interested in before suggesting them to the user. Some have

also suggested hyperlink shortcuts by shortening frequent web access sequences

discovered in the web log (Zheng,R. G. T. & Niu, Y.2002.).

For example, systems for recommendation such as Amazon.com that suggests

books or other products to purchase related to a current purchase, based on preference

information and other users purchases. The techniques are very simple and not always

accurate or even effective. Basically, the program compares the set of items purchased

by the current customer with the set of items purchased by other customers, selects the

customers with the bigger item overlap with the current customer’s item set, then

finally picks some items not yet bought by the customer but present in the baskets of

customers with high overlap and presents them as a recommendation list to the current

customer. More sophisticated methods take into account ratings of products given by

customers and select products for recommendation from customers that rate items the

same way or level as the current customer. This technique also used in information

retrieval for retrieving text documents that are similar is called collaborative filtering

(Chee,S., Han,J. & Wang, K.2001.).

A recommended system suggests possible actions or web resources based on its

understanding of the user’s access. To do so we have to translate the entries in the

web log into either known actions (i.e. learning activities such as accessing a course

notes module, doing a test, trying a simulation, etc.) or URLs of a web resource. This

mapping is a significant processing phase that in itself presents a considerable

challenge (Za¨ıane, O. R. Za¨ıane & Luo ,J. 2001. and Za¨ıane,O. R., Xin,M. and

Han ,J.1998.). Moreover, these identified actions and URLs are grouped into sessions

which is yet another difficult and delicate task (Za¨ıane, O. R.2001.). These sessions

are then modeled into transactions as sets of actions and URLs. The association rule

mining technique is applied on such transactions to discover associations between

actions, associations between URLs and associations between actions and URLs, as

well as associations between sequences of actions and/or URLs. This process usually

leads to a very large number of association rules even after filtering out those that do

not satisfy the requirement of minimum support (Agrawal,R. , Imielinski,T. & Swami,

A.1993.). We use other specific filtering approaches to eliminate such discovered

rules that associate two URLs that are directly linked from each other. We give higher

weights to rules that have as a consequent a URL or a set of URLs that are frequently

towards the end of a session.

When the recommender agent is activated by a triggering event, the association

rules are consulted to check for matches between the triggering event, or sequence of

events, with the rule antecedents. When a match is found, the consequent of the rule is

suggested. If more matches are found, the suggestions are ranked and only a small set

(highest ranked) is displayed.A recommendation system is a program that sees what a

user is doing and tries to recommend the learning profile that is beneficial to the user.

3.RECOMMENDATION E-LEARNING SYSTEM

This section is to illustrage the procedure of the data first then to detail the data

transfer, web mining technology and association rule of Web loge. Finally, to explain

how let the Agent offers the recommendation system on e-Learning.

3.1  THE PROCEDURE OF THE DATA IS EXPLAINED

Figure 2. The procedure of the data is explained

Web Agent

Course Database

Recommend Database

Learner n+1

Learning Profile evaluation

Learner n

Web

Web log

Course Database

Recommend Database

Analysis

Web Miningprocessor

Agent System

The beginning learner, that is to say the earliest one, will study in the web (e-

Learning teaching platform). The course materials of Web studying system come from

the course database. The data of learner’s learning profiles may be recorded in the

Web log. Then next step is to find out the best learning profile from the proceeded

data of Web log through web mining to proceed with Association rule. These learning

profiles need to be classified—every field has relevant courses and better learning

profiles.The Agent will offer the learning profile of relevant fields when learners

study the courses. These learning processions offer the Agent the result through

classification and the Agent can provide the good learning profile.

With the above information and learning profiles, when the future learners study

in Web, Agent offers related learning profiles according to the field. However, these

learning profiles may not be suitable for all learners. Therefore, after finishing

recommendation every time, there are systems of assessing. The user (n +1) evaluates

the learning profiles that are recommended. Because the profiles analized by system

may not be perfect, if there are adjustments of evaluation would make the

recommendation conform to learner’s asks more. As show in figure 2.

These suggestions can help learners navigate better relevant resources and fast

recommend the on-line materials, which help learners to select pertinent learning

activities to improve their performance based on on-line behavior of successful

learners.

3.2 WEB LOG

All accesses to a web site or a web-based application are tracked by the web

server in a log containing chronologically ordered transactions indicating that a given

URL was requested at a given time from a given machine using a given web client

(i.e. browser). Web server log files customarily contain: the domain name (or IP

address) of the request; the user name of the user who generated the request (if

applicable); the date and time of the request; the method of the request (GET or

POST); the name of the file requested; the result of the request (success, failure, error,

etc.); the size of the data sent back; the URL of the referring page; the identification of

the client agent; and a cookie, a sting of data generated by an application and

exchanged between the client and the server. A log entry is automatically added each

time a request for a resource reaches the web server. These log entries are not in a

format that is usable by mining applications and require to be reformatted and

cleansed in order to identify real session information, path completion, etc. (Cooley,R.

, Mobasher,B. & Srivastava, J.1999.). There exist some statistical tools that give

rudimentary analysis of the web logs and provide reports on the most popular pages,

the most active visitors, etc. in given time periods.And then this research will deal

with these materials and transfer them into useful and relative materials. Steps are as

follows:

Step1: Find out the Web log information of the relevant learner’s IPs on the same day.

As show in figure 3.That is to say the web page record that the learner passed is noted

down. Because Web log will write down all users’ records, we elect the single user,

that is to say the using route of the same IP.

Figure 3. An example of Web log

Step2：web page parameter

P={A,B,D,E,F,G,H…..} Each English word represents a webpage. The webpage route

materials that are put in order are as follows:

{ABEFGIJKPRUZ}

Step3：Time parameter

T={t1,t2,t3,t4}，t1 is 0~30 seconds, t2 is 31 seconds ~1 minutes, t3 is 1~3 minutes,

t4 is more than 3 minutes. Materials after change come out like these::

{At1Bt2Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4}

In the assessment of time, t1 to t4 presents the learners’ interest and attention degree

of courses. T is mainly used for analysing the useful learning profiles. t1 to t4 is to

analyze the valuable time. We may do deeper research for ' time ' in the future, and

find out learners’ useful statistics of studying time.

Step4：Join marks and appraise

After finishing the courses each time, we will offer the small test to prove whether the

learning profile is good. Furthermore, we will note down the data materials of

learning profiles each time for the source of materials recommendation.

{At1Bt2Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-78}

The value ' 78 ' is the test score after each study. It is stored in the database.

3.3 WEB MINING INFORMATION SCREENING

Web Usage Mining is the application of data mining techniques to usage logs of

large web data repositories in order to produce results that can be used in the design

task mentioned above.Find out the related recommendation route in materials put in

order by web mining , the steps are as follows:

Step1: Generally speaking, the useful materials should be t3 and t4. They take more

time to surf. It represents that learners read web pages. t1 and t2 may represent the

action of login the webpages or the materials that roughly browsed. They are not

suitable for our research so are not in consider. We leave the useful ' time ' data.

{At1Bt2Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-78}

↓

{Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-78}

(Delete t1 and t2)

Step2: Find out the better ' mark ' in learning profiles, for example: Pick and fetch the

data above 60 points. We regard 60 points as pass in general test.

{Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-78}ˇPass

{Ft4Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-80} ˇPass

{Ft3Ht3It4Jt4Kt4Lt3Zt4-55} 〤 Fail

{Dt4Ft3Lt4Mt4Nt3Yt3Zt4-35} 〤 Fail

{Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4-92}ˇPass

Delete Fail, leave fine learning profile that is above 60, take these fine learning

profiles and analyze them again. The result of learning profiles can be more correct

too. It will increase learning effect for the coming learner (n +1). Here

use「ˇ」represents pass,「〤 」represents fail ,the system will pick「ˇ」routes for

assessment’s standard.

Step3: Analyze useful route materials and all possible order situation of one route.

If learning profils is {ABC }. After analysis, the data will be

{A },{B },{C },{AB },{BC },{ABC }.

If lerning profile is {Et3Ft3Gt3It4Jt4Kt4Pt3Rt4Ut4Zt4}.Afther analysis, the data will

be {Et3 },{Ft3 },{Gt3 },{It4 },{Jt4 },{Kt4 },{Pt3 },{Rt4 },{Ut4 },{Zt4 },{Et3Ft3 },

{Ft3Gt3 },{Gt3It4 },{It4Jt4 },{Jt4Kt4 },{Kt4Pt3 },{Pt3Rt4 },{Rt4Ut4 },{Ut4Zt4 },

{Et3Ft3Gt3 },{Ft3Gt3It4 },{Gt3It4Jt4 },{It4Jt4Kt4 },{Jt4Kt4Pt3 },{Kt4Pt3Rt4 },

{Pt3Rt4Ut4 },{Rt4Ut4Zt4 },{Et3Ft3Gt3It4 },{Ft3Gt3It4Jt4 },{Gt3It4Jt4Kt4 },

{It4Jt4Kt4Pt3 },{Jt4Kt4Pt3Rt4 },{Kt4Pt3Rt4Ut4 },{Pt3Rt4Ut4Zt4 } .

These learning profiles are in order after analysis. If the set is {AB } that

representatives: study A course first and then B, but not how high is the probability

that study AB at the same time. It is different from the probability of the “Data

mining” appearing together in general statistics. The learning profiles in e-learing

researches are to let learners study better and faster, but not the final effect after

studying together. There will also see {ABA }: After studying A first, studying B, then

studying back to A, such a state will appear in e-Learning learning profiles, too.

Step4: Count the number of appearing times in all sets.

Table 2 Finishes the first statistics of learning profiles

X=1 X=2 X=3

{Et3}

{Ft3}

{Gt3}

：

1

1

1

：

{Et3Ft3}

{Ft3Gt3}

{Gt3It4}

：

1

1

1

：

{Et3Ft3Gt3}

{Ft3Gt3It4}

{Gt3It4Jt4}

：

1

1

1

：X=4 X=5

{Et3Ft3Gt3It4}

{Ft3Gt3It4Jt4}

{Gt3It4Jt4Kt4}

：

1

1

1

：

{Et3Ft3Gt3It4Jt4}

{Ft3Gt3It4Jt4Kt4}

{Gt3It4Jt4Kt4Pt3}

：

1

1

1

：

This is the first learner’s learning profile that put all sets into a form after

analysis. ' X ' represents the size of the set on the form. Below of the form, on the left

is an combination of a set; frequency statistic is on the right so when the future learner

increases, the number of times will increase, too. In this X =1 there is no connection

thus it wouldn’t be considered in this research. As show in table2.

Step5: To use frequent item set as the basis for recommendation.

After counting, the numbers that appear most frequently are taken out for the

frequent item set. Moreover, the sets appearing most frequently can be the better

learning profile after screening with the marks. We take these learning profiles as the

basis for recommendation.

3.4 RECOMMENDATION LEARNING OF THE AGENT OFFERS

The learning profiles through the screening need to give different learning

recommendation routes for different fields, for example in studying mathematics

courses, if learners study ' commercial mathematics ' first, the system will recommend

they can learn ' economics ', ' accounting ', and ' statistics ' later. Because according to

previous learning profile, after general users finish studying commercial mathematics,

then study “economics”, “accounting”, and “statistics,” the marks will obviously well.

However, if after finishing commercial mathematics, then they study the ' linear

algebra ', ' dispersed mathematics ', ' calculus,' the scores will be low. Therefore, the

Agent will offer different learning information under different learning fields.

Figure 4 An example of recommendation learning

Learning profiles are like ‘Tree structure ‘. In fact, they link and store with node

in the database, for example, in the situation of studying ‘Economics’, learners can

study ‘Macroeconomics’ first and then study ’Microeconomics’ or on the contrary,

study ‘Microeconomics’ first and then study ’Macroeconomics‘. The Agent will find

out the next course from the database. The connection situations of “node” of the

learning profiles in database are as follows:

Math

Commercial Mathematics Project Mathematics

StatisticsAccountingEconomics Linear Algebra

Dispersed Mathemati

cs

Calculus

MicroeconomicsMacroeconomics IntegrationDifferentiationDescriptive Statistics

Inferential Statistics

Figure 5. An example of learning profiles

Agent will recommend the following correlated curriculum in the suitable study

course field, that is to say, you will have {Accounting, Economics, Statistics} choices

in ‘commercial mathematics’, have {Macroeconomics, Microeconomics} in ‘

Economics’. There’s no restraint in studying ‘Macroeconomics’ or ’ Microeconomics’

so it will present the route of the above. The learner can study ’Macroeconomics’ after

studying ‘Microeconomics’ or study ‘Macroeconomics’ and then study

‘Microeconomics’. There is no certain order. If in ' Statistics ', there is the only one

learning profile—learn ’Descriptive Statistics ‘ first and then study ‘Inferential

Statistics’ so Agent may only offer one select. As show in Figure 5.

In different cases, different study courses will have different learning profiles.

These learning profiles will be in accordance with the learner's experience of the past

to offer future learners correct learning profiles and let the learners may study more

effectively and faster.

4.CONCLUSION AND FUTURE DEVELOPMENT

We are interested in recommending beneficial learning activities in nice on-line

learning and take shortcuts pattern in raising learning resource course materials to

users 。Recommend customers to buy goods systems at the e-learning system with e-

commerce, find out the relation between courses through Web mining to provide the

recommendation to learners. Moreover, use the connection between products in e-

commerce to raise purchasing ability. And this research finds out the best learning

profile in the fields of different learning courses. It offers the recommendation

learning profile to learners in e-learning system and lead learners to study the correct

study courses. These learning rules come from previous experience of learners, leave

the best experience for the future learners by screening. Having good study routes is

still not enough because the learning profile that is analyzed by system might not

Economics→MicroeconomicsEconomics→MacroeconomicsMacroeconomics→MicroeconomicsMicroeconomics→MacroeconomicsStatistics→Descriptive Statistics→Inferential StatisticsCalculus→Differentiation

Two choices of courses

Can choose the learning sequence—early or late

Only one single learning profile

suitable for every learner exactly, the recommending learning profiles must be

evaluated. The result of evaluation leaves the recommending learning profiles which

are suitable for learners and make recommending learning profiles could be more

complete.

In this paper, learners can fast find out the contents in numerous e-learning

database, but not waste a lot of time on looking for the wanted courses in the network

and don’t get best results of learning. This recommendation system pick and fetch the

past learner’s experience to recommend more effective learning profiles for the future

learner. It enables learners to find the knowledge that they want to learn and offer

other relevant learning profiles for their reference.

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