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Transcript of 2006bai6314.doc.doc.doc.doc
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
[email protected] & [email protected]
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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