Analogous Content Selection Mechanism Using Device Profile and Content Profile for U-Learning Environments

Yoonsoo Lee, Byoungchol Chang, Hyunoh Doh, Kyuhyun Park, Jaehyuk Cha1

Department of Electronic Computer Engineering, Hanyang University, Korea

{eclipse, bcchang, lunar0m, mai99, chajh}@hanyang.ac.kr

1 Corresponding Author

Abstract

There are a lot of learning contents, that have the same contexts which are targeted specific devices, under circumstances in which diverse devices support a ‘U-Learning environment’. But the current methods selecting adequate contents have little flexibility.

So, this paper we propose an analogy matched contents selection(AMCS) method which can find similar contents even though there are no exactly matched features of a learner’s device. Also, our research shows that our technique is at most two times more efficient than existing techniques.

1. Introduction

For the ‘U-Learning’ to occur, we need the system which supports ‘Device Awareness’ and ‘Contents Adaptation’. ‘Contents Adaptation’ is divided into two methods. The first one is the ‘Contents Selection’ method. The other is the ‘Contents Transformation’ method.

There has been much research concerning those methods on the process. For example, ‘Websphere Transcoding Publisher[1]’ which is based on annotation from IBM, ‘Function-based Object Model (FOM)[2] using ‘Content understanding’ which attempts an author’s intention from Microsoft and ‘Cuypers [3]’ which creates the adapted contents by reassembling chunk from CWI.

In the case of ‘Contents Transformation’, a situation that needs lots of additional information or loses its original meaning because of imperfection of information while it is reassembling might occur. Because of these problems, there have been many researches into the ‘Contents Selection’. In the case of ‘Contents Selection’, three main methods are required. First, the way to describe a distinctive feature of a device that a learner would make use of is needed.

Secondly, the function to describe characteristics of contents that shows the targeted devices. The final one, that is how to find the adequate contents which are suitable for each device is needed as well.

In this paper, we propose a device profile to describe the diverse characteristics of the device and content profile to describe the features of a targeted learner’s device. Furthermore, we suggest the mechanism is used to select adequate contents using these profiles 2. Device Profile and Content Profile 2.1. Device Profile

There has been a variety of standards from various

organizations such as ‘Delivery Context[4]’ from ‘Ubiquitous Web Applications Working Group (UWAWG)’ in ‘W3C’, ‘UAProf[5]’ from ‘OMA’ and ‘Universal Profiling Schema for content negotiation (USP) [6]’ from ‘INRIA’.

We use the ‘UAProf’ to represent the features of devices. However, it has a shortages of vocabulary needed to describe the amount of information of ‘U-Learning’ devices.

In this paper, we suggest 32 additional attributes extending to basic words which number about 100. Table 1 shows part of the added attributes.

Table 1. Part of additional UAProf attributes

Attribute Resolution Rule

Type Sample Value

SpeakerEnable Locked Boolean “Yes”,”No” MICEnable Locked Boolean “Yes”,”No”

Codec Append Literal(bag) “xvid” FlashEnable Override Boolean “Yes”,”No”

… … … …

2.2. Content Profile

2009 Ninth IEEE International Conference on Advanced Learning Technologies

978-0-7695-3711-5/09 $25.00 © 2009 IEEE

DOI 10.1109/ICALT.2009.176

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Existing meta-data for contents only describe context. For instance, ‘IEEE LOM[7]’, contains no information concerning targeted devices. Therefore, the need for standards describing the features of contents for matching the appropriate devices would arise.

We propose a new concept ‘Analogy Rate of Attribute (ARA)’ to describe the features of devices formally. Every attribute for content has the ARA values and the ARA values are from 0 to 1 in proportion to how well the author’s intention is expressed. This value is registered by a content’s author when the content is created, and then used for contents selection.

Attributes of devices are sorted into two categories according to their feature of attributes’ value. The first one has “Interpolated ARA(IARA) values” that have continuous and ranges features such as the screen size of devices. In this paper, we assume that the value of screen size is continuous even though it has been used as being discrete. In actuality diverse devices have a variable screen size between discrete values. Attributes like screen size are considered as IARA values. The other one has “Unconnected ARA (UARA) values” which have discrete value, 0 or 1.

Figure 1. Sample IARA graph Figure 2. Sample UARA graph

Figure 1 shows the ARA value of ‘Network-

Bandwidth’ which is one of the IARA values. Each line represents variance of ARA values according to the attributes’ value of devices.

In the case of UARA, it has three types of values. One is a Boolean type which is ‘true’ or ‘false’. Another is literal type such as ‘Pentium III’. The other is enumeration type like ‘text/html', 'image/gif'.

Figure 2 exhibits attributes for ‘CcppAccept-Language’.

Figure 3 shows the classified attributes.

Figure 3.Classified attributes by features of values

3. Mechanism 3.1. Device Awareness

The most common way of Device Awareness is using ‘HTTP Request Header’. If this method is used on the Web, there is no need to install additional programs such as Active X or Java Applet.

One drawback of this method is that it cannot find the similarity between same materials if they have different attributes’ names. This problem can be solved by the way that modifies the header information. However, the problem still exists that Http Request Header does not have enough space to contain the modified information.

Our method basically makes use of device awareness techniques which apply ‘Request Header’. In addition, it identifies the devices as long as it has their own profiles which describe devices’ features. 3.2. Contents Selection

The simplest way for content selection is to employ the Exact Matched Contents Selection(EMCS). EMCS means that it only finds the content that has the exact values for attributes.

But, it cannot find any contents if there is no exact value for specific attributes. That is, it works properly only when there are precisely the same values of attributes. Hence, we propose the mechanism that can retrieve

contents which are close those wanted, even if there is no result from the EMCS method. The proposed mechanism has an analogy matched contents selection (AMCS) metric that the ARA value is applied to, in order to search the approximate to outcome of the EMCS. AMCS method adopts linear interpolation to calculate unknown ARA values.

We define the ‘adequate contents’ as the content that has the highest weighted average of the ARA value.

In Equation 1, VoA stands for the value of analogous, w represents the weight for each attribute and x stands for the ARA values of attributes.

( )(1) 1)w(0 1 ≤≤

×=∑=

n

xwVoA

n

iii

In addition, there are critical attributes which affect the operation of devices. Consequently, if such attributes do not satisfy the requirement, set the ARA value of them as - ∞.

4. Implementation and Testing

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We implemented a system to test our mechanism. Figure 4 is system architecture.

Figure 4. System Architecture

We performed the experiment with 30 contents and 10 different devices.

In this paper, we adopt the ‘precision’, ‘recall’ and ‘First-nP’ as our evaluation criteria. The ‘precision’ and ‘recall’ are defined by Equations 2 and 3.

(2) #

# Precision contentsfoundallof

contentsfoundcorrectofP =

(3) # # RecallcontentsadequateallofcontentsfoundcorrectofR =

The measurement that we use, ‘First-nP[8]’, rates the service based on the percentage of results within the first ‘n’ returned that were relevant or useful. Equation 4 is ‘First-3P’ to estimate the performance.

{ }[ ] (4) 10)3, min(#360

3PFirst×−−

∑=−resultof

orderretrievedbyscore

Figures 5, 6, 7 show the results from the experiment. It is a comparison of EMCS and AMCS.

Figure 5. The comparison of the Precision

Figure 6. The comparison of the Recall

Figure 7. The comparison of the First-3 Precision

As the above figures show, the AMCS method shows the better performance in most cases.

5. Conclusion

In this paper, we proposed the ‘Content profile’ which is the meta-data of content describing the device-requirement. Also, the AMCS method was suggested to overcome the weakness of the EMCS. Then, we proved that our AMCS method showed better efficiency throughout the experiment. As ‘Information and Telecommunication Technology’ is being dramatically developed, diverse devices are also being introduced. It is a highly time consuming and cost inefficient task to create contents for all devices. It is expected that our mechanism would contribute to the development of ‘U-Learning’ environment.

6. References [1] IBM - WebSphere Transcoding Publisher. http://www-01.ibm.com/software/pervasive/transcoding_publisher/ [2] Chen, J.L., Zhou, B.Y., Shi, J., Zhang, H.J. and Wu, Q.F. Function-based Object Model Towards Website Adaptation. Proc. of WWW-10, Hong Kong, May 1-5, 2001. [3] J. van Ossenbruggen, F. Cornelissen, J. Geurts, L. Rutledge, and L. Hardman. Cuypers : a semi-automatic hypermedia generation system.Technical Report INS-R0025, CWI, Amsterdam (NL), 2000. [4] Gimson, R., et. al., "Device Independence Principles", W3C Working Draft, http://www.w3.org/TR/di-princ/, September 2001. [5] Open Mobile Alliance(OMA) User Agent Profile V2.0. http://www.openmobilealliance.org/Technical/release_program/uap_v2_0.aspx [6] Lemlouma T. and Layaïda N. Universal Profiling for Content Negotiation and Adaptation in Heterogeneous Environments. W3C Workshop on Delivery Context. W3C/INRIA Sophia-Antipolis, ARAnce, 4-5 March 2002. [7] Wayne Hodgins et al. Draft Standard for Learning Object Metadata. http://ltsc.ieee.org/wg12 /files/LOM_1484_12_ 1_v1_Final_Draft.pdf. IEEE 1484.12.1-2002, 2002. [8] H. Leighton and J. Srivastava. First 20 precision among world wide web search services. Journal of the American Society for Information Science, 50(10):870–881, 1999.

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