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Expert Systems with Applications 35 (2008) 2113–2122

Expert Systemswith Applications

Automatic and interactive e-Learning auxiliary materialgeneration utilizing particle swarm optimization

Tien-Chi Huang a, Yueh-Min Huang a,*, Shu-Chen Cheng b

a Department of Engineering Science, National Cheng Kung University, Taiwan No. 1, Ta-Hsueh Road, Tainan 701, Taiwan, ROCb Department of Computer Science and Information Engineering, Southern Taiwan University of Technology

No. 1, Nantai Street, Yung-Kang City, Tainan 710, Taiwan, ROC

Abstract

The purpose of this research was to utilize a PSO-based algorithm, serial blog article composition particle swarm optimization(SBACPSO) algorithm, to automatically and intelligently generate auxiliary materials. Contrary to previous fixed content auxiliary mate-rials, the proposed auxiliary materials, which consist of blogs posted by learners, provide more interactive and cooperative characteristicsfor the learning process. With a few blog features such as comments, trackbacks, difficulty levels, and association degree related to aspecific topic, the best combination of blog articles is produced as an auxiliary material. The generated auxiliary materials from a realcourse are presented in a system demonstration. The experimental results and satisfaction analysis also indicate that the proposed algo-rithm can achieve the expected convergence, with participants being satisfied with interaction, assistance, usability, and flexibility.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Serial blog articles composition particle swarm optimization; Auxiliary material; e-Learning; RSS

1. Introduction

The ultimate goal of an e-Learning system is not only toprovide diversified and standardized learning or educa-tional materials with computer-assisted technologies, butalso to offer efficient and effective learning for every kindof learner. In order to achieve this goal, a lot of researcheshave been done in this field. For instance, a personalized e-Learning system proposed by Huang, Huang, and Chen(2007) uses computerized adaptive testing (CAT) and agenetic algorithm to construct a learning path for each lear-ner. In the learning path, the system provides each learnerwith individual course materials that assist his/her learningmore effectively. Liu and Yang (2005) proposed an adap-tive learning system, which is combined with education the-ories, strengthens individual learning material generation.

0957-4174/$ - see front matter � 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.eswa.2007.09.039

* Corresponding author. Tel.: +886 6 2757575x63336; fax: +886 62766549.

E-mail addresses: kylin@easylearn.org (T.-C. Huang), huang@mail.ncku.edu.tw (Y.-M. Huang), kittyc@mail.stut.edu.tw (S.-C. Cheng).

Although the studies have mainly focused on individualcourseware material, a lot of time and effort have beenspent on individualization for each learner. Furthermore,it should be noted that there have been few attempts toestablish a direct relationship between a specified courseand the related auxiliary materials.

In order to tackle these problems, our earlier work pro-posed a learning management system which applies a stan-dardized course generation process (SCGP) that utilizesdynamic fuzzy Petri nets (DFPN) to design the learningmap of a curriculum (Huang et al., 2008). The processnot only makes the designed courses conform to theSCORM standard (2004), but also automatically offersauxiliary materials for each specified course. However,fixed content auxiliary materials were adopted in the study;consequently, factors such as adaptation and interactionreceived less emphasis. Therefore, rather than looking fora one-size-fits-all model, adaptation and interaction of aux-iliary materials need to be strengthened. An innovativeapproach that uses serial blog article composition withPSO (SBACPSO) algorithm is proposed to optimize the

2114 T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122

selection of blog articles to compose serial auxiliarymaterials.

The particle swarm optimization (PSO) algorithm wasintroduced to find near optimal solutions (Kennedy &Eberhart, 1995). Inspired by the flocking behavior of birds,PSO has been successfully applied to test sheet design (Yin,Hwang, Chang, Hwang, & Chan, 2006), game learning(Messerschmidt & Engelbrecht, 2004), data clustering,image analysis (Wachowiak, Smolikova, Zheng, Zurada,& Elmaghraby, 2004), and NN training (Bergh & Engelbr-echt, 2000; Eberhart & Hu, 1999; Engelbrecht & Ismail,1999. Yin et al. (2006) suggested that a PSO-based algo-rithm is suitable for problems in composing optimal serialitems from large item banks. In this study, Yin’s techniqueis applied to use a PSO-based algorithm to automaticallyand intelligently compose blog articles which are postedby learners as auxiliary materials possessing interactionand adaptation. Multiple criteria are used to determinehow to distribute blog articles to each auxiliary material.For example, the number of comments, the degree of theblog articles’ difficulty, the expected ratio of unit topics,and the amount of trackbacks along with each blog articleare considered. Moreover, the practicality of the proposedmethodology is demonstrated using an implemented sys-tem, analytical experiments, and satisfaction evaluations.

The rest of this paper is structured as follows: in Section2, related research, including the existing e-Learning sys-tems and blog-based techniques in the learning field, ispresented. Section 3 shows the proposed algorithm, SBAC-PSO, and an illustrative example is given to explain theprocess of the algorithm. The system demonstration, ana-lytical experiments, and satisfaction evaluations are pre-sented in Section 4. Finally, a brief conclusion is given inSection 5.

2. Related works

In recent years, various education or learning systemshave been proposed in the field of e-Learning. However,those that focused on quintessential textual learning arealready out of date. The state-of-the-art learning systemsusually take multimedia information such as audio, video,and animation into account. Two kinds of web-basedlearning system models were presented by Akama, Osumi,& Makoshi (2002): the Patterned Frame Model and theSynchronized Presentation Model. The two models takeadvantage of multimedia content to present learning pat-terns in an e-Learning environment. Cybulski & Linden(2000) developed a multimedia assisted teaching environ-ment (MATE) which uses interactive multimedia to offerhigh-quality learning assistance. From the learner’s per-spective, MATE complements traditional teaching, whichis associated with lectures, tutorials, and practical sessions.Additionally, a web-based real-time presentation systemhas been proposed for electronic learning materials (Desh-pande & Hwang, 2001). This system also uses an extensionof the well-known bi-level image encoding algorithms

which allows video frames to be clearly recognized at alow bit-rate encoding.

Although the above-mentioned research all focused ondeveloping multimedia learning content, they did not pro-vide specified auxiliary learning materials for learning assis-tance. To overcome this problem, we have proposed astandardized course generation process that uses dynamicfuzzy Petri nets to enhance course planning (Huang, Chen,Huang, Jeng, & Kuo, 2008) based on the earlier developede-Learning system (Jeng, Huang, & Kuo, 2005). Moreover,not only are multimedia learning materials for teachingand learning offered, but also specified auxiliary materialsare dynamically determined for designated courses. Never-theless, an auxiliary material defined in that study might beanother fixed learning resource; consequently, the learnercannot participate in a discussion with other learners,which results in the system being insufficient with respectto interactivity. In view of this, a crucial element in web2.0, blogging, is applied to facilitate knowledge sharingand interactive discussion.

A blog is usually viewed as a website that collects per-sonal published information. Generally speaking, informa-tion is published periodically according to the editor’spurposes. By means of blog postings, learners can docu-ment their learning experiences or knowledge and sharethem. Hence, in recent years, blog-based systems have beenapplied in educational or learning settings (Divitini, Haug-alokken, & Morken, 2005). An implemented blogging sys-tem has been proven to be feasible in an internationaldistance course (Lin et al., 2006). It can foster learners’ par-ticipation and develop their e-Learning experiences. Dron(2003) adopted structural and methodological techniquesto tackle the blended delivery problem, which leads to lear-ner anxiety and inefficient learning. Additionally, manycorporations, such as Microsoft and Sun Microsystems,also use blogging technologies as internal knowledge man-agement tools with which employees share research infor-mation and ideas with each other (Gordon, 2006). A blogoffers an environment where decentralized authorship canbe realized, and creates a more feasible environment inwhich learners can be stimulated to make more reflectionsand comments (Nardi, Schiano, Gumbrecht, & Swartz,2005). Notwithstanding the achievement of communicationand information sharing of blogs, a lot of disorganizedblog articles exist in the blogosphere. Using an appropriatedesign and guiding strategies, the proposed method leadsto blog articles that are organized and useful auxiliarymaterials in the e-Learning field.

3. Serial blog article composition

3.1. An overview of the proposed system

In this subsection, the proposed system that automat-ically and intelligently produces the interactive auxiliarymaterials needed for learners is introduced. Fig. 1 showsthe abstract architecture of the system. In this architec-

Fig. 1. The abstract architecture of the proposed system.

T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122 2115

ture, the role of bloggers can be teachers, learners, orcourse designers who post blog articles to offer assistanceor add to a discussion. All blog articles posted would becollected into a database called blog knowledge base,and the proposed SBACPSO algorithm draws theselected blog articles to generate the specific RSS feedswhich are provided to LRMS by a designed RSS aggre-gator. Next, LRMS takes these RSS feeds to sort theauxiliary materials ordered according to the related top-ics within a course. In essence, the auxiliary materialsoffered by LRMS can be shown as several RSS feeds.Afterwards, learners are able to subscribe to the RSSfeeds from LRMS and directly obtain the auxiliary mate-rials. As soon as the learner’s subscribed feeds have newcontent, the RSS reader dynamically retrieves that con-tent and presents it to the user. By the manner, learnersdo not need to manually check whether the auxiliarymaterials have new content.

3.2. Model design for serial blog article composition

In order to compose blog articles for auxiliary materials,a few factors such as the difficulty of each blog article, theassociation degree between blog articles and a specifiedtopic, the number of comments of blog articles, the numberof trackbacks related to a blog article, and the course topicscontained within blog articles, where course topics indicatesome key terms in a specified course such as ‘‘Stack’’,‘‘Array’’, or ‘‘Linked List’’, are considered.

K auxiliary materials are assumed in a specified course.To compose blog articles as the auxiliary material k,1 6 k 6 K, the following variables are used in the proposedmodel:

• di, 1 6 i 6 N: the difficulty of ith blog article, where N isnumber of blog articles in the blog knowledge base.

• rij, 1 6 i 6 N, 1 6 j 6M: the association degree betweenith blog article and the topic j, where M is number oftopics in a course.

• ci, 1 6 i 6 N: the number of comments of ith blogarticle.

• ti, 1 6 i 6 N: the number of trackbacks of ith blogarticle.

• L: lower bound on the expected comments for each blogarticle.

• U: upper bound on the expected comments for each blogarticle.

• xik, 1 6 i 6 N and 1 6 k 6 K: the decision variable is setto 1 if blog bi is in auxiliary material k; otherwise, it is setto 0.

• hj, 1 6 j 6M: the lower bound on the expected relevanceof the topic j.

• C(x): a membership function mapping the number ofcomments x into a degree.

• T(x): a function, which has a sigmoid form, maps track-back count x into a score.

The following equations show the formal definition ofthe proposed model:

O1 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXK

k¼1

PNi¼1dixikPN

i¼1xik

� D

����������2

vuut ð1Þ

O2 ¼XK

k¼1

XM

j¼1hj �

XN

i¼1rijxik

� �ð2Þ

O3 ¼1PK

k¼1

PMj¼1

PNi¼1rijxikT ðtiÞ

ð3Þ

L 6XN

i¼1CðciÞxik 6 U ; 1 6 k 6 K ð4Þ

Eq. (1) calculates the difference between the average diffi-culty degree for each auxiliary material and the targetdifficulty degree D given by the lecturer. Herein, combina-tional blog articles should be selected such that the averagedifficulty degree of each auxiliary material is close to thetarget difficulty degree. In Eq. (2), the total relevance ofthe selected blog articles in each auxiliary material is firstlycalculated (i.e.

PNi¼1rijxik), and then the expected relevance

of each topic is used to calculate the relevance difference of

2116 T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122

each topic for each auxiliary material. The more relevancebetween blog articles and topics there is, the less the calcu-lated relevance difference is. In Eq. (3), the trackback countof each blog article is used to calculate a score whose valueincreases with the trackback count. Constrain (4) indicatesthat the total comment count for each auxiliary materialshould be restricted in a specified range which is deter-mined by the course designers. The membership functionC(x) defined by Chen, Huang, & Chu (2005) maps a com-ment count x into a degree which is defined in the openinterval (0, 1), as depicted in Fig. 2. x1 and x2 are two con-trol values which, respectively, indicate the lower andupper bounds of the comment count. If the comment countof a blog article is less than x1, students may not get inter-ested in the article and it might not get commented enthu-siastically. On the other hand, if the comment count of ablog article is larger than x2, the article can attract numer-ous discussions and it should get a higher degree:

CðxÞ ¼0; x < x1

x�x1

x2�x1; x1 6 x 6 x2

1; x > x2

8><>:

In addition, T ðxÞ ¼ 10:1þ5e�x

� �is designed to evaluate the

trackback count of a blog article as shown in Fig. 3. Whenthe trackback count x of a blog article is small, a slight

1.0

Degreee

CommentCountt

1x 2x

Fig. 2. A membership function.

0 2 4 6 8 100

1

2

3

4

5

6

7

8

9

10

Trackback Count

Score xe−+ 51.0

1

x

Fig. 3. A sigmoid form function maps the trackback count to a score.

score is given to this blog. The score of a blog with a highertrackback count should be larger than one with a lowertrackback count. The purpose of using a sigmoid formfunction is to make a curve such that the score can signif-icantly increase as the trackback count increases.

3.3. SBACPSO (serial blog article composition with PSO)

algorithm

In this subsection, the SBACPSO algorithm process isdescribed. The algorithm consists of four steps to find qual-ity approximate solutions. The first step is initial swarmgeneration, which describes how parameters, along withblog articles, are encoded into a vector. The second stepis the kernel of the PSO-based algorithm, which is thedesign of fitness function. In the third step, pbest for eachparticle and gbest are calculated using the designed fitnessfunction. The velocities and particles’ position are updatedin the fourth step. Finally, the reinforcement strategy of theauxiliary materials is discussed in the last step.

Input: N blog articles b1, b2, . . ., bN, M topics c1,c2, . . ., cM, the expected relevance of topic j, hj, the targetarticle difficulty D, the number of required auxiliary mate-rials, K, and the lower and upper bounds, L and U, respec-tively, of the comment count.

Output: gbest is the best solution which is a combina-tional set of blogs.

Step 1. Initial swarm generation:

The particle is a candidate solution of evaluatingthe combination of blogs, which is represented byan NK-dimensional vector, [x11, x21, . . ., xN1,x12x22, . . ., x1K, x2K, . . ., xNK]. As mentionedabove, xik is a binary value; if the blog bi is con-tained in the auxiliary material k, xik is set to 1;otherwise, it is set to 0.

Step 2. Fitness function design:

In the proposed model, O1, O2, and O3 are threeobjective functions that need to be minimized.Additionally, the selected particles should meetconstrain (4). Therefore, if constrain (4) is violated,a penalty needs to be considered in the design of fit-ness function. The penalty term is shown below:

P ¼XK

k¼1min 0;U �

XN

i¼1

CðciÞxik

!����������

þ minð0;XN

i¼1

CðciÞxik � LÞ�����

�����!

ð5Þ

If the comment count does not fall into the speci-fied range, which is less than the lower bound orlarger than the upper bound, the penalty term willbe summed in the fitness function. Ultimately, thefitness function can be represented as the followingequation:

Minimize F ðxÞ ¼ O1 þ w1O2 þ O3 þ w2P ð6Þ

Table 1Given input parameters and values

T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122 2117

where w1 and w2 are weights for Eq. (2) and thepenalty term for falling into the interval (0, 1).

N M h1 h2 h3 D K L U

8 3 1 0.5 0.8 0.5 3 0.1 3.0

Step 3 . Combination of social and cognition models for

velocity updating:

Since a PSO-based algorithm possesses character-istics that are similar to the coordination andbehavior consistence of a biological colony in arealistic environment, each particle needs to ownthe best experience itself and the best global solu-tion. Two models are utilized to describe thesetwo solutions; the social model and the cognitionmodel. In the social model, the ith particle moveswith a velocity vi, which is a function of the bestsolution found by the particle itself (i.e. calledpbesti). Moreover, the best solution (i.e. calledgbest) found among all particles should be deliv-ered to each particle, which means each particlecognizes selfhood in a colony. The PSO-basedalgorithm combines these two models to presentthe velocity modification of each particle in thewhole swarm. The velocity of the ith particle atiteration t is represented by the following:

viðtÞ ¼ viðt � 1Þ þ u1l1ðpbesti � yiðt � 1ÞÞþ u2l2ðgbest � yiðt � 1ÞÞ ð7Þ

where u1 and u2 are acceleration constants and l1

and l2 are uniformly distributed random numberswhich fall into an open interval (0, 1). yi(t � 1)indicates the position of the ith particle at thet � 1 iteration.

Table 2The relevance between eight blogs and three topics

Step 4. Update of positions:

In this step, the position of each particle needs tobe updated to find a better solution. The updateof positions simply depends on the velocity of eachparticle, which is shown as the following equation:

Topic 1 Topic 2 Topic 3

yiðt þ 1Þ ¼ yiðtÞ þ viðtÞ ð8Þ b1 1 0.5 0b2 0 0.3 1b3 0.5 0.9 0.2b4 0.7 0.2 0.7b5 0.2 0 0.9b6 0.6 0.8 0.8b7 0 0.1 0.7b8 0.4 0.6 0

Table 3The number of comments and trackbacks and difficulty level of eight blogs

Comments C_degree Trackbacks T_degree Difficulty

b1 12 0.022 4 5.22 0.3

Step 5. Reinforcement of the auxiliary materials:

In order to enrich the content in auxiliary materialsfor a course, a periodic reinforcement processneeds to occur. The reinforcement strategy is thatexisting blogs in an auxiliary material are kept per-manently, and the proposed algorithm executes aslong as the amount of new blog articles in the blogknowledge base achieves a certain number which isdefined by the course designer. Clearly, the existingblogs should not be considered in the subsequentexecution and new blogs reinforce the auxiliarymaterials.

b2 50 0.44 3 2.87 0.4b3 27 0.19 5 7.48 0.2b4 33 0.26 0 0.2 0.6b5 103 1.0 25 9.99 0.5b6 90 0.89 20 9.99 0.4b7 23 0.14 0 0.2 0.6b8 43 0.37 5 7.48 0.9

3.4. An illustrative example

This subsection provides an example to illustrate theSBACPSO algorithm. First, a few given input parametersare shown in Table 1

Assume that three auxiliary materials (=K) associatedwith target difficulty 0.5 (=D) need to be generated fromeight blog articles (=N) which are related to three topics.The lower bounds of the expected relevance of these threetopics are h1, h2, and h3, respectively. The weights for Eq.(2) and the penalty term are both set to 0.01. Table 2 showsthe associated relevance between eight blogs and threetopics.

Table 3 represents the number of comments and track-backs for each blog as well as the difficulty level of eachblog. In the aforementioned description, the number ofcomments and trackbacks needs to be transformed intodegrees by the membership function C(x) and the sig-moid-formed function T(x), respectively. In this example,the lower (x1) and upper (x2) bounds in the membershipfunction are 10 and 100. Therefore, the number of com-ments and trackbacks can be transformed into propor-tional degrees, represented as C_degree and T_degree,respectively, in Table 3. In order to reward a blog whichis referred to, a sigmoid-formed function, T(x), is particu-larly used to transform the number of trackbacks for eachblog to represent the score. Obviously, the scoring methodis controlled by the designed sigmoid curve which isdepicted in Fig. 3.

Step 1. Initial swarm generation:

Two particles are used as a swarm in the example.In the first generation, the first particle selects blogarticles b1, b4, b5 for the first auxiliary materials, b5,

2118 T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122

b6, b7 and b8 for the second one, and b1, b7 for thethird one. In regard to the second particle, the firstauxiliary material includes b2, b6, b7 and the secondauxiliary material consists of b3, b4, b7 and b8, andthe third auxiliary material is composed of b1, b2,b4, b5 and b6.

Step 2. Fitness value computation:

The fitness value of each particle can be calculatedaccording to the fitness function, F ðxÞ ¼ O1þw1O2 þ O3 þ w2P , as shown below:

1st Generation

1stauxiliarymaterial

2ndauxiliarymaterial

3rdauxiliarymaterial

F(x)

1st particle 10011000 00001111 10000010 0.0852nd particle 01000110 00110011 11011100 0.012

Apparently, the second particle attains a smallerfitness value than the first particle. Hence, the sec-ond particle is considered as gbest. Furthermore,each particle takes itself as after the firstgeneration.

Step 3. Velocity and position updating:

In this step, the two particles need to be updated sothat they can proceed to the next generation. Theupdate information includes the position andvelocity of each particle. Since the second particleis taken as gbest, the position and velocity of thefirst particle should refer to the position of the sec-ond particle. The update rules are based on Eqs. (7)and (8). In essence, the first particle will movetoward the second particle in the second genera-tion. Furthermore, the second particle will main-tain the same velocity since it is gbest. Theposition will be changed by adjusting a very smallnumber of bits, which can be seen in the followingsecond generation:

2nd Generation

1stauxiliarymaterial

2ndauxiliarymaterial

3rdauxiliarymaterial

F(x)

1st particle 01010000 00110111 11001000 0.0432nd particle 01000100 00111010 10011001 0.072

Obviously, the combination of the first particle isclose to that of the second one, and the combina-tion of the second particle becomes[010001000011101010011001].

Step 4. Auxiliary material generation and reinforcement:

The above-mentioned steps successively proceeduntil a user-defined generation number is reached.Finally, the best combination in each auxiliarymaterial will be determined, and then these auxil-

iary materials can be provided for learning. Sincethe computation load will increase as the numberof blogs increases, the proposed algorithm needto periodically execute according to the load onthe computation server. Subsequently, the rein-forcement of auxiliary materials will be achieved.

4. Evaluations and discussion

4.1. System demonstration

The system is mainly designed for learners to provideauxiliary materials. In addition, it offers other convenientfunctionalities to allow users to acquire intrinsic andextrinsic learning information during the learning process.After logging in to the personal learning system, the learnercan begin to post blogs, interact with others, and acquirethe auxiliary materials. Fig. 4 illustrates all the informationrelated to the course ‘‘Data Structure’’ which has beengiven by Prof. Huang. The right side of the figure presentsthe latest blog articles posted by learners and the auxiliarymaterials that were generated by the proposed PSO-basedapproach for each topic within the course. Since the pro-vided auxiliary materials are made as RSS feeds, if a lear-ner clicks one of the auxiliary material links, the materialis extracted by the designed RSS parser and shown in thecenter area. Obviously, the material related to ‘‘Stack &Queue’’ consists of the selected blogs, as shown in Fig. 4.Moreover, each blog can be viewed in detail by clickingon the ‘‘More’’ link.

Apart from the course information and auxiliary mate-rials presented for users, an RSS aggregator was designedto allow learners to collect their own needed auxiliarymaterials and external knowledge or information. If a lear-ner wants to add an auxiliary material to his/her own col-lection, he/she can click the icon, , next to each topic’sname. After clicking the ‘‘Personal RSS Aggregator’’ link,the learner can see the information collected by him/her-self, as shown in Fig. 5.

The RSS aggregator allows learners to add RSS feedsinto his/her own collection. These feeds can be not onlyfrom the auxiliary material feeds as mentioned above, butalso from web sites which provide RSS feeds. If a learneradds a feed by inputting the name and address of the feed,the feed will be distributed to the group which is under thehierarchy ‘External’; otherwise, the feed comes from auxil-iary materials. The subscribed feeds are presented as a treestructure, as shown on the left side of the figure. The learnercan examine the contents of a feed, which are blogs in thiscase. Each blog can be parsed from a XML structure andpresented to the learners. If a learner finds a blog usefuland wants to include it in his/her own collection, he/she justpresses the ‘‘Tag’’ shown beside each blog. Afterwards, theblog is added into the collection as easily as adding a bookmark. In this manner, the selected blogs can be kept over aperiod of time, without having to search for them.

Fig. 4. The course information and provided auxiliary materials.

Fig. 5. Personal collective information.

T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122 2119

4.2. Evaluations

4.2.1. Analytical experiments

Analytical experiments consisted of two parts: the firstexperiment was conducted to observe whether the fitnessvalue decreases and converges as the generation numberincreases. The second one analyzed the change of fitness

values which are set in different generation numbers. Inthe second experiment, determining an appropriate swarm-size and generation number for the reinforcement of auxil-iary materials was also a goal.

In the first experiment, 20 particles were chosen and theterminated generation number was set to 100. In addition,the number of blogs for this experiment was chosen to be

Table 4Questions for each aspect

Aspect Subject Question

Interaction Lecturer Do you feel the system can increase thediscussion frequency with your students?

Learner Do you think this system can increase theinteraction inclination with the teacher or otherlearners?

Assistance Lecturer Do you think the system can offer the suggestions

2120 T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122

300. The results representing the fitness values acquiredfrom gbest throughout the generations are given inFig. 6. Apparently, the fitness value decreases as the num-ber of generations increases, ultimately moving towardconvergence which accords with our original anticipation.

In the second experiment, 20 particles were used to ana-lyze the change of fitness values which are located in a dif-ferent number of items from two different generations, asshown in Fig. 7. When the terminated generation numberwas set to 15, the fitness values tended towards valuesunder 0.1, with the number of blogs more than 800. Onthe other hand, the fitness values were under 0.05, withthe number of blogs is more than 350 with 100 generations.The results offer an estimation basis for determining thecriteria for proceeding with reinforcement of auxiliarymaterials. In order to leverage the computation load onthe server, when the number of blogs increases to 800 ina shorter period of time, the reinforcement process startswith 15 generations, while the number of blogs increasesto 350 in a longer period of time, the reinforcement processchooses 100 generations.

4.2.2. Satisfaction evaluation and overall benefit

In order to evaluate the improvement and validity of thedesigned system, the targeted subjects for satisfaction eval-

Fig. 6. Convergence process of 300 blogs with 20 particles.

Fig. 7. The decreasing behaviors of fitness values among differentgenerations.

uation were equivalent to the experiment ones who wereselected in (Huang et al., 2008). The targeted subjectsencompass 90 lecturers and 400 learners who have everused the SCGP platform. The measurements consist offive aspects, which are ‘‘Interaction’’, ‘‘Assistance’’,‘‘Usability’’, ‘‘Flexibility’’, and ‘‘Improvement’’. Eachaspect employs a five-point scale to measure the validityof the proposed system. Table 4 shows the questions foreach aspect. The ‘‘Improvement’’ aspect was evaluatedcomparing with previous results about above four aspects.

Figs. 8 and 9 are the results of teacher reflection andlearner perception, respectively. It can be observed thatmore than 90% of lecturers and learners agree that the aux-iliary materials provided by the system can lead them tointeract with others. In the assistance aspect, 85% of learn-ers agree that the auxiliary materials can render essential

during course design?Learner Do the provided auxiliary materials render

essential assistance for your learning process?

Usability Lecturer Were the generated auxiliary materials consistentwith the topic of the course?

Learner Were the provided auxiliary materials useful inyour specific topic learning?

Flexibility Learner Compared with the auxiliary materials you haveused on the previous platform, did the auxiliarymaterials provided in the current system rendermore flexibility?

Lecturer

On a scale of 1–5 rate: (1 = very disagree 2 = disagree, 3 = moderate,4 = agree, 5 = strongly agree).

Fig. 8. The evaluation of the teacher reflection.

Fig. 10. The improvement of the generated auxiliary materials.

Fig. 9. The evaluation of learner perception.

T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122 2121

assistance for their learning. However, only 65% of lectur-ers accept that the materials would offer suggestions for acourse design, because some lecturers think that the coursedesign needs to depend on their professional knowledge,rather than only basing it on the discussed information.It should be noted that 25% of lecturers and learners thinkthat the auxiliary materials were not associated with thespecific topics. Apart from some of learners stating thatthe posted blogs were not closely related to the topicswhich they were learning, a few lecturers also felt thatnot all auxiliary materials were compatible with the learn-ing topics. A possible reason for this is that the auxiliarymaterials generated by the PSO-based algorithm areapproximate solutions, thus they may not match the topicscompletely. As for the evaluation of flexibility aspect, 70%of lecturers and 87% learners agree that the provided aux-iliary materials are more flexible than the previous ones.

Based on the above-mentioned four aspects, the overallbenefit for participants compared to the auxiliary materialsin SCGP was investigated. The results are shown in Fig. 10.

Seventy-six percent of participants think that the pro-vided auxiliary materials contribute more interactiveopportunities. These results were expected since a discus-sion process occurs when they post blogs or write com-ments. Many of the learners deem that these materialscan help nurture the spirit of teamwork and an understand-ing of the roles of other professionals. Additionally, theresults of the assistance satisfaction were mixed. Fifty-two percent of respondents agree the proposed materialsoffer more assistance than earlier ones, while others pointout that the auxiliary materials have no significantimprovement in terms of assistance. The improvement ofusability and flexibility were about 82% and 86%, respec-tively. Most learners acknowledge that the system providesa more convenient way to acquire auxiliary materials sincethey just subscribe to an RSS feed in terms of a relatedtopic. Moreover, since the selected blogs can be added intothe RSS feed dynamically, most learners and lecturersacknowledge the improvement in flexibility. Overall, theresults have been very positive.

4.2.3. Response feedback

In order to design more pedagogical learning materials,a few opinions about the proposed auxiliary materials weregathered from lecturers and learners, respectively, and arelisted as follows:

Teachers’ perspective

1. The generated auxiliary materials make lectures moreinteractive and interesting.

2. The design process of auxiliary materials accords withproblem-based and cooperative learning.

3. Lecturers can participate in the discussion among learn-ers during the material design process.

4. Lecturers can adjust the designed learning materialsdepending on what learners find difficult.

5. Sometimes the main focus of the lecture seems to be onauxiliary materials rather than on course content.

Students’ perspective

1. I like contributing opinions or suggestions to otherlearners to solve their problems.

2. It gives me an idea of how I am doing in relation to therest of the class.

3. During the material design process, many problems andmisunderstandings could be identified and resolved.

4. These auxiliary materials indeed make up for the defi-ciency of knowledge taught in the class.

5. I am so glad that my posted blogs were selected as theauxiliary materials.

5. Conclusions

In this paper, a PSO-based algorithm, SBACPSO, whichcollects a few didactic and pedagogical blog articlesto make interaction-centered auxiliary materials, was

2122 T.-C. Huang et al. / Expert Systems with Applications 35 (2008) 2113–2122

developed. The algorithm was also deployed on an existinge-Learning platform. In the proposed approach, the char-acterization of a blog is utilized to analyze its quality, suchas the number of comments and trackbacks. In addition,two characterizations which encompass the difficulty levelof a blog and the association degree along with a specifictopic were added. The blogs selected are packaged in aRSS feed associated with a related topic, and then LRMSprovides these feeds as auxiliary materials. An illustrativeexample has been given to explain the operation processof the proposed algorithm. Even though many differentstudies have attempted to provide various materials, fewof them utilized intelligent and interactive approaches togenerate the auxiliary materials. With the assistance ofSBACPSO, lecturers and learners can participate in thedesign process of the materials, significantly improvinginteraction and flexibility. The system demonstrationshowed the functionality and utilization of a system whichdeployed the proposed algorithm. The system successfullyoffers varied auxiliary materials relevant to the various top-ics in different courses.

The evaluations consisted of three parts: analyticalexperiments, satisfaction evaluations, and response feed-back. Experiments were conducted for both comparativeperformance and convergence velocity of the proposedalgorithm. The results revealed that a better executionefficiency could be acquired in our approach and thequality of the solution increased as the number of gener-ations adopted increased. In the second part of the eval-uation, the satisfaction of the participants who used theprovided auxiliary materials was analyzed. The fouraspects evaluated were interaction, assistance, usability,and flexibility. Clearly, the results indicate that the gen-erated auxiliary materials have a positive effect on theparticipants’ teaching and learning. In terms of improve-ments, the results were as expected. A few commentsfrom lecturers’ reflection and learners’ perception werealso listed. The comments from some lecturers indicatethat the proposed approach offers educational methodssimilar to problem-based and cooperative learning meth-ods during the design process of the auxiliary materials.Most learners also felt that they obtained more learningand discussion opportunities with the lecturers and otherlearners. In the future, our system will be improvedtoward the provision of problem-based learning toaddress the inadequacies of traditional instructionalapproaches. Also, information retrieval techniques willbe used on blogs to offer learners more precise and effi-cient learning materials.

Acknowledgement

The authors would like to thank the National ScienceCouncil of the Republic of China for financially supportingthis research under Contract No. NSC 95-2221-E-006-307.

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