Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

A MASHUP-BASED ADAPTIVE LEARNING SYSTEM

YI-HSING CHANG., YEN-YI CHEN2

Department of Information Management, Southern Taiwan University

E-MAIL:yhchang@mail.stut.edu.tw m9990211@stut.edu.tw

Abstract: This study aims to build, using Felder and Silverman's

Learning Style Theory and Mashup technology, an adaptive

learning system to help students improve their learning effect. In this system, Felder and Silverman's Learning Style Theory is used to gain an understanding of students' learning styles to

enable them to engage in adaptive learning according to their

respective learning styles. Additionally, this learning system also allows learners to use a Mashup search engine to search for related supplementary teaching materials to achieve better learning results. After its completion, the learning system was used to conduct an experiment on the freshmen of two computer programming classes in the university's Information

Management Department to compare the difference in students'

learning effect. Moreover, a questionnaire was designed based on Technology Acceptance Model to carry out qualitative and quantitative analyses. The results showed that compared with

the control group, students in the experiment group made more significant improvement in their academic performance and all of them had a positive evaluation for the learning system.

Keywords: learning style, adaptive learning, Mashup, programming

language

1. Introduction

With the rapid development of the Internet and information technology, e-Learning has now become an important way for people to obtain knowledge. Also, people can improve their learning effect through computers and the Internet. According to Wikipedia [1], definition, e-Learning is

a way of learning using online technology, in which information technology and multimedia are used to establish a learning model to break through traditional classes' limitations in time and location and make it possible for learners to learn at times that are convenient for them. In the e-Learning nowadays, LMS (Learning Management System), which is a set of leaning management system, has been widely used as an intermediary platform to set up, store, maintain, follow up and manage the information of both learners and teachers as well as their respective learning and teaching processes. In this study, Felder and Silverman's

978-1-4673-1487-9/12/$31.00 ©2012 IEEE

Learning Style Theory is combined with LMS to construct an adaptive learning management system, in which a Mashup search engine is used to allow learners to choose, via system's learning style classification module, courses that are most suitable to them, thereby to further enhance their learning efficiency and achieve better learning results.

2. Literature review

2.1 Learning style

In terms of learning style, there are different definitions.

For example, Honey and Mumford [2] defined learning style as a person's best way of learning decided by his or her own attitude and behavior; Conner [3] noted that learning style is a person's preferred way to obtain knowledge; Graf, et al. [4] suggested that students' preferred learning methods can be derived from their related learning statistics using a regularity-based method; Peterson, et al. [5] contended that a person's best way of learning is closely associated with the content of his learning and the learning environment at the time. The learning style theory put forth by Felder and Silverman [6] divides students' learning styles into four aspects, each of which contains two learning styles (see Table 1 ).

TABLE 1: THE CHARACTERISTICS OF EACH LEARNING STYLE

Learning aspect Learning style Process Active, Reflect

Input Visual, Verbal

Perception Sensitive, Intuitive

Understanding Sequential Global

A study conducted by Zualkernan et al. [7] showed that the application of learning style to the exchanges in the learning of different cultures can be very helpful. Wen and Wang [8] pointed out that the determination of students' e-Learning style can help the provision of adaptive learning based on learners' needs and desires. In a study by Yao, et al.[9], learning style was further applied to medical

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Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

experiments. From the above, it is known that the application of learn style can greatly improve people's learning effect. Consequently, in this study the Learning Style Theory put forth by Felder and Silverman was adopted in the learning system to help boost learners' learning effect.

2.2. Adaptive learning

Alexandros and Susanne [10] mentioned that a good learning system environment should be able to monitor users' behaviors, judge their needs and preferences and provide appropriate learning content based on users' information. Brusilovsky [11] came up with a method and skills to establish an adaptive learning environment. He thought that a good system should be able to meet the following requirements: being a hypermedia system; equipped with learner modules; a hypermedia model with adaptive learning functions. Therefore, the adaptive technologies required for the construction of an adaptive learning Website can be divided into two main categories as shown in Figure. l.

Adaptive multimedia .....--- presentation

Figure. 1: The illustration of adaptive hypermedia technologies

In terms of application, a more complete adaptive learning system was presented by Tao, et al. [12] to provide learners with the path and map model for adaptive learning knowledge items as well as learning preferences for users. Shipin and Jianping [13] once mentioned that interactive Web pages and coordinated teaching methods should be included in today's adaptive learning to help people in their learning process. Jovanovic et al. [14] proposed an ontology-based online adaptive data-sharing mechanism, via which users can share learning resources based on the correlations between learners' attributes and an ontological tree structure. A study by Takano and Li [15] suggested that an adaptive learning system, in which 3D dynamic graphics are used to help learners understand the content of a book, can also increase users' interest in learning. Besides, Matar [16] came up with an Adaptive Learning Object Repository Structure to enable students from different universities to use adaptive learning systems of other schools. In addition to the links of adaptive annotation and adaptive hiding as shown in Fig. 1, other adaptive hypermedia technologies are also included in the

learning system in this study.

3. System structure

The purpose of this study is to build a learning style-based adaptive learning management system, in which a three-layered framework concept is adopted: presentation layer, service layer and data layer (as shown in Figure. 2).

Presentation Layer

Desktop aUI II Mobile

Device aUI

--Scrvicc--L-a-y-c-.-.. ---------------------------1[---

ServiceAPI

i-------------------A-ppficati<-)r;-S-e-r:vice------------------1

I [ Teachi ng I\A3.nagenent Pgent J I

Mapt i ve Pgent

I\A3.shup search Agent L ______________________________________________________________________

,

___________________ :ft ___________ ___________ Jt _____________________________ _ Data Layer ..JJ..- Jj..

:--i}-a-taba-se--- -----: : ---------------s-o-c-fili-

;.;ctw-o-;..ks

--i

"� ,Ii

"�"�,i �==;=�=;===t'1

Social Network

Systenl Data

c _ ________________________________________________ ! L_L __ -----=----------"

c=J Applicati()ll (1IJI � Datahase

Intclligent Agents Modulc � Web Service Data

Figure. 2: System structure

3.l. Presentation layer

GUI (Graphical User Interface) is used for the presentation layer to allow users to operate the system by moving a mouse pointer or via other pointing devices on Windows, icons, buttons and so on. As a result, users can engage in online learning by simply logging on to the Website. At present, the system can support Web page surfmg and other mobile devices such as smart phones.

3.2. Service layer

The service layer includes three modules: teaching management agent, adaptive agent and Mashup search agent (see the following explanation).

(1) Teaching management agent: This module is designed to divide teaching materials into different categories to provide learners with the most appropriate teaching content according to learners' information provided by adaptive agent.

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Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

(2) Adaptive agent: This module is designed for the provision of adaptive learning. It is equipped with a learning style radar chart to help identify users' learning preferences, direct learners to interactive mechanisms such as course-related interactive discussion boards learning forums blogs, etc. and help learners to quickl; understand th� courses' pre-class guidance and after-class quizzes. Moreover, the supplementary information searched out via the Mashup search engine can be presented in an adaptive manner, and the teaching content of the adaptive courses can also be displayed using a knowledge map

(3) Mashup search agent: This module is designed for users to engage in Mashup search to fmd additional information based on adaptive sorting results provided by the above two agents so that learners can gain a better understanding of the course content according to their interests.

3.3. Data layer

Data layer contains three databases: content database, user database and Web service data.

(1) Content database: This database is designed to store basic teaching materials and adaptive teaching materials, including images, texts, graphs, multimedia and so on.

(2) User database: This database is designed to store users' logging-in information such as users' personal information to verify if the user has the right to view the course content. Additionally, it is also used to keep records related to learners' test processes, discussion boards opened by each user, forums, etc.

(3) Web Service data: This database is designed to store

links to adaptive supplementary information searched out via Mashup search function.

4. System design

4.1 System workflow

Figure. 3 is the system's workflow. The system can judge users' learning styles according to their test results to allow learners to understand their own learning preferences. After that, the system can provide corresponding teaching content based on learners' preferences to help them achieve better learning effect. Users can also use Mashup search to find more supplementary information.

Figure. 3: System flowchart

4.2 Adaptive classification

This section is to explain on how to use adaptive classification to effectively improve users' learning effect. After a user logs in, the system will require him/her to fill in a questionnaire designed based on the learning style theory put forth by Felder and Silverman before using ILS (Index of Learning Styles) method to grade the filled questionnaire to further judge the user's learning style. However, it was found in this classification process that this kind of input method failed to distinguish learners of oral type from those of other types. This means that the design of the system is not suitable for oral type learners because they have difficulty understanding the course content by listening only to

system's explanation without the help of pictures. As a result, a decision was made to remove oral type from the adaptive classification items. Fig. 2 shows the learning strategy of adaptive classification.

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Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

TABLE 2: THE LEARNING STRATEGY OF ADAPTIVE CLASSIFICATION

Learning Learning Learning aspect style strategy

(tools/teaching materials)

Active Internet Process forum, etc.

Reflect Wiki, Weblog and et al.

I Visual Pictures and Input Tables , Video

Perception Sensitive Cases Leading

Intuitive Practice After School

Understanding Sequential Related learning

objectives and Key projects

Global Knowledge Map

According to the above description on learning styles, learners can clearly understand their learning aspects. After that, the system will record learners' learning styles, and the adaptive agent will guide them to the related teaching Web pages for adaptive learning

4.3 Module design

� =� dbase�.

Mashup Search Mashup Agent Scurch Web Service

Figure. 4: module design

Figure. 4 shows the module design of the adaptive agent, teaching management agent and Mashup search agent.

(I) Adaptive agent

After learning style classification, the adaptive agent will provide the learner with adaptive course content according to the results of learning style classification. Meanwhile, the agent will also check the records contained in the user database to adjust course content and consult test results to understand the leamer's learning situation and his progress. If the learner doesn't perform well or lag behind in the learning process, the Mashup search engine can be used to look for appropriate supplementary materials. After that, the system will guide the learner to discussion boards for the answers to questions he/she has encountered. In this way, learners can solve their problems through discussions with other learners or via teaching assistants' help.

(2) Teaching management agent This agent is responsible for the classification of

teaching materials into different categories based on their attributes. After knowing which kind of information is needed by the leamer, the adaptive agent will call teaching

management agent to find out the most appropriate teaching materials according to the leamer's information provided by the adaptive agent, and report back to the adaptive agent so that the adaptive agent can send the course content to the learner.

(3) Mashup search agent After a learner has finished a adaptive learning session

but wishes to have more supplementary information, he/she can use the adaptive agent to call up the Mashup search agent to look for more supplementary information according to

his/her learning style. The Web Service Data module will then record all the links to the adaptive supplementary information for the leamer's direct review next time.

4.4 Mashup Module

The Mashup Data Model, is illustrated in Figure 5. The concept is the teaching materials are composed of the following three types of materials: text, graphics, and video. The different types of teaching materials are searched and retrieved from their corresponding social network platforms. Thus, the learners can effectively learn by the social network resources.

Figure 5: Mashup Module

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Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

5. Experiment

5.1 Experiment steps

(1) Subjects

The experiment was conducted on the freshmen in the Visual Basic programming classes in the Information Management Depart of Southern Taiwan University of Technology. It involved two classes - one as experiment group and the other as control group. The experiment lasted five weeks. Each week had three class sessions. Totally, there were 15 experimental class sessions.

(2) Experiment method In the beginning, both groups of students were made to

attend normal e-Learning classes. After the mid-period, students of experiment group were made to participate in adaptive learning, in which an adaptive teaching Website for Visual Basic programming was used to assist students in their learning. After the experiment, a comparison was made to see if there was any difference in students' learning effect between the two groups.

(3) Post-experiment questionnaire on learning effect After the semester ended, students of the experiment

group were asked to fill in a questionnaire related to their learning effect to understand if the adaptive learning could help improve their learning.

(4) Data analysis Statistic analysis software SPSSI7.0 was used to

analyze the retrieved questionnaires to see if the goal of this study had been attained

5.2 Experiment results

TABLE 3. THE DIFFERENCE BETWEEN THE TWO GROUPS IN THE RESULTS OF THE LEARNING EFFECT PRE-TEST

Experiment group

Control group

Independent-Simples T Test

Number Mean SD T-value P-value

58 67.52 15.180 .062 .951

58 67.34 14.800

The difference between the two groups in the results of the learning effect pre-test Independent-sample t-test was conducted on the learning effect pre-test results of students from both experiment and control groups. The pre-test results were based on the examinations administered in the mid term. Table 3 shows the results of the analysis, in which t-value is .062 and p-value is .951 (> .05). The results suggest that there is no significant difference between the two groups in the results of the learning effect pre-test.

TABLE4. THE DIFFERENCE BETWEEN THE TWO GROUPS IN THE RESULTS OF THE LEARNING EFFECT POST-TEST

Independent-Simples T Test

Number Mean SD T-value P-value Experiment

group Control group

58

58

78.12

71.91

10.053 2.896 .005

12.863

The difference between the two groups in the results of the learning effect post-test Independent-sample t-test was conducted on the two groups' results of the learning effect post-test. The post-test results were based on the examinations given at the end of the semester. Table 4 shows the results of the analysis, in which t-value is 2.896 and p-value is .005 « .05). This suggests that the two groups had both made significant progress in their tests but compared with the control group, the improvement made by the students in the experiment group were much more remarkable.

6. Conclusions

The experiment found that the Mashup-based adaptive learning system can substantially boost learners' learning effect. In another word, learning style classification indeed can help learners gain a better understanding of their own learning aspects and preferences. Compared with traditional one-way teaching methods, adaptive learning can more effectively increase learners' interest in learning and allow them to learn in their own preferred manners. Moreover, Mashup search can help users find more useful knowledge. Currently, the learning materials provided by the system are mostly in form of Web pages, which are easy to understand but not interesting enough. Accordingly, it is suggested that in the future more interesting learning concepts should be incorporated in this kind of learning system to make the learning process of computer programming more like playing games to further boost students' interest in learning

Acknowledgements

This study is supported in part by the National Science Council of Republic of China under the contract number NSC99-2511-S-218-008-MY2

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Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15-17 July, 2012

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