Context and Culture Metadata: A Tool for the Internationalization of E-Learning

Jan M. Pawlowski e-Learning Research Center, Korean German Institute of Technology, KGIT, Seoul – South Korea /

Information Systems for Production and Operations Management, University of Duisburg-Essen, Germany jan.pawlowski@icb.uni-due.de

Thomas Richter e-Learning Research Center, Korean German Institute of Technology, KGIT, Seoul – South Korea

richter@kgit.re.kr

Abstract: This paper addresses the problem of adaptation of E-Learning to a given or proposed context. Current learning technology standards are available for various purposes, such as contents, learner profiles or learning ac-tivities, but there are no specifications to describe the context of learning scenarios. Such a description is crucial to identify change-requirements or to compare situations when learning scenarios are re-used. In this paper, we define a specification of context metadata. We show how they can be used to adapt learning scenarios from a given to a new context, in particular to identify change requirements for the internationalization of learning sce-narios.

Introduction

The objective of this paper is to develop a metadata approach representing the context of learning environments to enable the adaptation of existing learning scenarios to a new context. Based on existing standards, our approach pro-vides the basis for comparing, re-using, and adapting scenarios.

Re-using learning scenarios or learning objects seems to be a promising and efficient concept for the development of new educational materials or courses. Even though large scale repositories have been implemented, users do not yet widely use this opportunity. Reasons for this obstacle are the lack of appropriate systems to re-author learning sce-narios and the lack of appropriate recommender systems to identify potential learning scenario candidates. To real-ize those, it is necessary to gather and use information on the context of learning scenarios. There are currently no specifications describing in which environment learning scenarios are or should be used. Therefore, we develop a specification to describe context metadata as a basis for the comparison and adaptation of learning scenarios.

Firstly, we address the issue of standardization. We discuss how standards contribute to the re-use of learning ob-jects, learning scenarios and experiences. Based on this analysis, we identify gaps in existing standards, specifically in the field of context models. Those are necessary to represent the context in which learning scenarios are / can be used. One focus is the description of cultural aspects which are crucial for the internationalization of learning sce-narios. We conclude with a scenario, showing the potentials of our model to internationalize E-Learning scenarios. We derive content changes needed for a successful adaptation from one to another context.

Standards and Adaptation

In the following chapter, we show how learning scenarios can be re-used and how standards support this process.

Re-use

How can we enable the massive re-use of E-Learning? Re-use should not be limited to learning materials / objects, but moreover share and re-use learning scenarios and experiences around them. Different learning technology stan-dards have been developed for this purpose. One goal of standardization is re-use (cf. Littlejohn & Buckingham Shum, 2003). The IEEE Glossary (1990) defines reusability as “the degree to which a software module or other work product can be used in more than one comput-

ing program or software system“. Extending this definition to the fields of learning, education, and training, we con-sider reusability as the degree to which a component, object, or activity can be used in more than one learning sce-nario (Pawlowski, Bick, 2006). Re-use is discussed in various contexts, specifically in the field of software engineering and development (Jacobsen et al., 1997, Pawlowski, Bick, 2006). In the educational context, the discussion focuses on learning objects (cf. Wiley, 2000; Sicilia & García, 2003) and learning activities (cf. Brusilovsky & Nijhawan, 2002; Koper & Man-derveld, 2004; Karampiperis & Sampson, 2005). The variety of aspects on re-use is discussed in Littlejohn & Buck-ingham Shum (2003). Providing only codified knowledge, respectively data (Swan, 2003) is not sufficient to facili-tate re-use. The information might neither be understood nor used (Lugger & Kraus, 2001). The issue of re-use is a basic knowledge management problem (cf. Sridharan & Kinshuk, 2002; Benmahamed et al., 2005) because it in-cludes the facilitation of knowledge identification, knowledge acquisition, knowledge development, knowledge dis-tribution/sharing, knowledge preservation, and knowledge use (Probst & Romhardt, 2000). Instead of only sharing learning materials, it is moreover necessary to share learning activities / processes as well (i.e., learning scenarios). In a given context, it is necessary to know for which new context such activities shall be adapted. The answers on the following questions could help to determine the necessary information for this purpose:

• For which context has the original learning scenario been designed? How similar is this context to the new context and - if necessary - which are the changes needed to manage the adaptation process?

• Which experiences have been made in previous usage scenarios within both contextual environments? To answer the above questions, we analyze existing standards regarding their usefulness.

Learning Technology Standards

Standards have been discussed controversially in the last decade: Demands on cost-reduction, secure investments, and new market potentials are contradictory to the fear of limitations for creative solutions. Standards have been de-veloped and adopted for different contexts and have improved the flexibility and effectiveness of E-Learning. Figure 1 shows a classification for standards in the field of learning, education, and training.

Figure 1: Classification of Learning Technology Standards (cf. Pawlowski, Bick, 2006)

Learning Technology Standards deal with the interoperability of components of learning environments, such as au-thoring systems, learning management systems (LMS), and learning resources and services. Different standards have been developed for the description of content (Learning Object Metadata, LOM, IEEE, 2005), for the interac-tion between LMS and learning objects (Sharable Content Object Reference Model, SCORM, Dodds & Thropp, 2004), for didactical scenarios (IMS Learning Design, Koper, Olivier, & Anderson, 2002; DIN Didactical Object Model, DIN, 2004), and for actor / user modeling (Learner Information Package, LIP, Smythe et al., 2001). These standards provide a basis for the re-use, recombination, and re-contextualization. Two main issues are not yet ad-dressed: • Context Description: One of the main advantages in E-Learning is the possibility to personalize learning proc-

esses. The adaptation of learning environments should be based on the context of the learner. However, there is currently no adequate context specification which can be used by authoring or learning management systems.

• Experiences: Re-use highly depends on its usefulness for actors in a given context. To determine the usefulness it is necessary to see how previous usage scenarios have been performed and how users perceived the usage.

Therefore, it is necessary to find a representation of experiences and performance indicators. A promising ap-proach in this field is the analysis of previous usage scenarios by (Wolpers et al., 2007).

Adaptation Process

Adaptation of E-Learning environments means that learning objects or scenarios are modified for usage in a new context. This adaptation process can differ in the degree of adaptation needs: from minor adaptation (e.g., changing media formats) to a full re-authoring (e.g., translation, adaptation to a different culture) (cf. Gütl et al., 2004; van Rosmalen et al, 2006). The adaptation process consists of five phases (Figure 2):

• Search: In this phase, actors search for useful learning objects, e.g. in a learning object repository or a knowledge base.

• Validate Re-Usability: As a first step, the (intended) context and the new context are compared, e.g. using similarity comparisons and recommender systems (Manouselis, Sampson, 2004). The recommender sys-tems can be improved incorporating previous usage behavior (Wolpers et al., 2007) or experiences (Bick, Pawlowski, 2006).

• Re-Use / Adapt: In this phase, the learning scenario is retrieved and changed. Typical scenarios include re-using scenarios for a new purpose or context (e.g., from Higher Education to corporate training).

• Validate solution: In this phase, it is tested how the changed learning scenario fits the needs of the new con-text.

• Re-Publish: Finally, the new learning scenarios are shared with other users in a repository.

Figure 2: The Adaptation Process In the adaptation process, it is necessary to compare and analyze the context of learning scenarios. Therefore, it is necessary to develop a common language, i.e., a specification to represent the context. This specification can then be used in recommender and adaptation systems.

Context Metadata

The context of E-Learning in our meaning contains every influence factor on learning scenarios which cannot be in-fluenced in the design process. In the following, we will show different levels and consequences of those factors. Our application scenario is an existing course to be adapted for a new context. Depending on the differences be-tween the original and the targeted context, changing needs have to be identified and changes have to be imple-mented before starting a course. In some cases, these differences may be so enormous that an adaptation is impossi-ble. To identify the needs and the adaptation efforts, we need a comprehensive description of the context. In this pa-per, we present simplified descriptions to illustrate our ideas, concepts and models. In our current research, we focus on the definition and prioritization of E-Learning context metadata. As a second issue, we identify solutions for the automated metadata generation and process models to define possible adaptation needs and changes. E-Learning re-lated problems caused through its context are frequently mentioned and described within the literature, e.g.:

• Technical infrastructure (Edmundson, 2006; Heaton, 2001; Kenny, Gunawardana, 2005) • Rights systems (McLean, 2005) • Media user behavior and knowledge: media richness (Wilson, 2002) • Human actors’ culture, knowledge, behavior and acceptance (Zimmermann, 2001; Ahn, 2003; Ho, Ko,

2006; Kenny, Gunawardana, 2005) • Demographical development (Christmann, Badget, 1999) • Religion (Akinyemi, 2003; Ascheri, 2006) • Culture (Hofstede, Hofstede, 2005; Ho, 1976; Wilson, 2002; Cakir, Bichelmeyer, Cagiltay, 2002)

The following figure shows the variety of influences on learning scenarios:

Figure 3: The context of learning scenarios

Figure 3 shows classes of context information (i.e., influence factors): In the middle, the learning scenario is illus-trated with its contextual elements (CE). Those elements directly or indirectly influence a learning scenario. The context blocks (outside the circle) represent typical influence types (impacting the learning scenario). Each context block consists of related sets of various context metadata and related attributes. The data structure description is de-fined to be used within a context metadata database, needed for our adaptation process (Table 1 and Figure 5).

Table 1: Context blocks and related metadata

Context Block Description Sample context elements Culture influence learning scenarios because they

directly influence the human actors life, the curricula, the doctrine and teaching methods (applied to countries, and re-gions)

• language (country) • heroes (country) • learning styles (country) • methods for criticism (country) • required support (country) • …

Rights influence on teaching and learning sce-narios as well as on user rights and rights on contents because of special laws within countries & regions (applied on countries / regions)

• accreditation needs • content related age-constraints • controlled historical views • information constraints • gender specific constraints • …

Human Actor / Role Bearer influence learning scenarios in the role as basic participants: Learners, Authors and Tutors. No individual classification for Learners or Tutors, but general valid at-

• level of needed support (learner) • general learner history (learner) • known learning styles (learner) • interacting habits (learner)

tributes • … • special expertise (author) • cultural background (author) • religion (author) • …

Companies influence learning scenarios concerning in-house trainings because of special needs. Data have to be provided by the companies and are attached to them

• corporate design • internal policy (i.e. QA) • special contents (methods and

lingo may differ from common) • learning history (fixed training

cycle for employees) • …

… … …

Figure 4: Context metadata data-set at the example of “Language” within the Context Block “Culture”

Obviously, those metadata are dynamic. Changes on the attributes of the contextual elements can be expected be-cause of alterations within the environment. Those can influence the content of the context blocks (number and type of context metadata) as well as the attributes of certain context metadata. Therefore, it is necessary to validate and update data from time to time. Changes concerning the contextual elements do not happen at once but usually with a large time delay. An event which might cause impacts on the learning scenario can first just impact the country, some later the learning environment and a long time later the learner himself. Deep political changes as the German reunification (1989), which extremely impacted GDR’s learning environment, are exemplarily for such events. Avoiding inconsistencies in the later database for context metadata, we defined the context blocks to be as inde-pendent and distinctive from each other as possible, so that changes in one block only have minimum influences on the metadata within other blocks. Nevertheless, some relations between the blocks have to be modeled (e.g. in an ontology), since the system has to stay intuitively understandable. As a consequence, extensive checks and updates are necessary. As we have found out, this decision cannot be brought out automatically, because not every single change in the context influences the E-Learning environment (but maybe in combination with others). Seemingly small changes in the environment can, depending on the configuration of attributes, have crucial impacts and obvi-ously big ones may not influence the learning environment at all.

Procedure model: Using context metadata

In the following, we show how the adaptation process can be implemented. We show the processes of adaptation and a technical solution. The determination of metadata and the concrete comparison of contextual environments as part of the adaptation process are designed as two separate processes but may be part of a single application. The da-tabase containing the context metadata is the central information source. Applications shall have (reading) access on this database and automatically get the necessary information within the gathering process. Therefore, standardized data structures (see example in figure 5) and protocols crucially are needed, which can be addressed / used by appli-cations, like authoring or adaptation-process supporting tools. The following procedure model shows how an application can compare the original context and the targeted context by using the database. The database is implied to already be up-to-date. The way how the data reach the database depends on the type, the availability and the impact-radius of the metadata. Some data may be gathered automati-

Context Block: culture Metadata Type: language Related to Contextual Element: country General languages existent: Yes/No Number of Languages: n Geographical separation of regions with different languages: N.A. / Yes / No Language, Region: (language_1, Region_1); (language_2, Region_2); … language_n, region_n) Society type: high context / low context Duration type: static / dynamic Gathering type: manually / automatically / external Source: Fischer Weltalmanach Data Set

cally by referencing other databases (i.e. referencing the German website of “Fischer Weltalmanach” (Fischer, 2006), as supposed within figure 5 for the case of “Language”) or official institutions have to be determined manu-ally or will have to be provided by “interest groups” like companies or societies with special needs. In this field, there are still a lot of open questions and research necessities also concerning general changing needs.

The model has conceptual character and does for example not include specific state aspects: what happens when data are taken out of the database in the time between found out changing needs and fulfilled changes, how the data-base is being filled with data, and others.

Figure 5: The Changing needs evaluation process model

The following steps are necessary to identify changing needs:

1. Define the originating course context and targeted context. 2. Provide the extracted metadata of the originating course to the data-gathering application. 3. Describe the targeted context within the application. 4. In two steps (a, b) the application requests context metadata from the database and gets them as response as

far they are available; otherwise an error-message is given. 5. The datasets are given to the comparison application and the comparison takes place, output is given:

The output is a document containing the differences between origin and targeted context (based on the origin con-text). Additional, if monitored and available through the database, experiences concerning the contexts can be taken into consideration: In any case, a list of recommendations of necessary changes is provided. The recommendation list only shows a minimum number of adjustments but in lack of further experiences (basing on the constellation of different contexts and differing subjects) it has to be checked manually for further necessary changes.

6. Manual evaluation of the result to determine changing needs for the adaptation process.

It is possible that as result of the comparison procedure the needed changes for a successful adaptation process are so significant that realizing this step is not useful. To realize the adaptation the costs to write a new course should be higher than those of the adaptation process. This decision cannot be done automatically because it depends on a lot of additional factors, which are not part of the context metadata database, such as regionally different staff costs. Context metadata attached to learning objects describe their attributes for the whole document but not for every sin-gle paragraph: although it might be useful, it seems impracticable at this point. As a consequence, the list of neces-sary changes could actually only concern a small part of a course while the rest is designed differently.

7. Implement the changing needs.

Comparing different countries is not the only opportunity for comparisons the application should provide. Also countries and companies, societies or interest groups have to be comparable with each other. The precondition to be able to do so is that there are defined datasets available. As we have shown the adaptation process is highly com-plex. While it may be relatively easy manageable between western industrial countries, the process to adapt contents into developing countries or indigenous societies is far more complex and we need more experiences and research. In the following, we demonstrate the adaptation process in a case study.

A Usage Scenario

In our scenario, we show how open content can be re-used to internationalize E-Learning scenarios. Generally, the idea of open content is to provide educational resources to all stakeholders. Open content intends to initiate a dy-namical process: based on an initial resource, content should be used, enriched, improved, and then provided to the community again (see also Bailey, 2005; Cedergren, 2003). This dynamical process can lead to an exponential in-crease in the number of resources (and re-users). Open content does not necessarily mean free resources – as an ex-ample, in the open source community, several business models have been successful, such as shareware concepts or the development of commercial add-ons or consulting services. Therefore, it is necessary to enable re-use as well as advanced scenarios of usage, such as internationalization, re-contextualization, or commercialization.

The scenario “internationalization” can be viewed as a special case of the “contextualization process”. In this case, teachers or service organizations need to translate contents and identify aspects for the cultural adaptation (such as curricula regulations, cultural norms and values, media and presentation aspects, didactical traditions and methods). As a result, the initial content should become available in a multi-lingual, multi-cultural version. This case is of spe-cific interest because “new” resources and contents are created – developers will take “emotional ownership” of the content and will strongly commit to improve and use those resources. Additionally, a wider range of users / learners can be reached improving the target group of the initial content.

Scenario Description

We monitor the process of an open-source course written in Germany designed for students of a virtual university. The course has the subject “database-design” and exercises are attached. It shall now be added to the program of a Digital University in South Korea. Concerning detectable differences there are a lot of related context-blocks. For this example we focus on only two metadata types out of the context block “culture”. The differences between both countries concerning learning behavior are as big as thinkable, so that without further changes the German course would cause misunderstandings in the targeted context and could not successful be im-plemented. While direct criticism in Germany is a usual way, it would cause a face-loss for Korean students. Also the level of needed support during the course is much higher in Korea than in Germany. The course in Germany may not need a tutor except for corrections and critics, but in Korea the students will need to be guided by an authority. The assumptions regarding the differences between both countries are based on Hofstedes’ dimensions Power Dis-tance Index (PDI) and Individualism Index (Hofstede, Hofstede, 2005).

Description of the phases (Search / Validate) using context metadata

1. Origin Course Context: Germany; Targeted Course Context: South Korea; 2. Extract the metadata applied to the German course and give it to the data-gathering application 3. Define the targeted context as South Korea, University; 4a. Request_Dataset_Course_Context; … Receive_Dataset_ Course_Context;

4b. Request_Dataset_SouthKorea; … Receive Dataset_SouthKorea;

5. Datasets now are the input for the comparison process

Available change recommendations:

• No offensive criticism in South Korea, local tutor required • Tutor as person of authority in South Korea needed

Context Block: Culture; Metadata Type: Methods for criticism; Related to Contextual Element: country, students; Direct criticism possible: Yes; Alternative approaches: NA; Region: Germany; … … … … … …

Context Block: Culture; Metadata Type: Level of needed support; Related to Contextual Element: country, students; Tutor required for teaching: no Tutor required for feedback: yes Region: Germany; … … … … … …

Context Block: Culture; Metadata Type: Level of needed support; Related to Contextual Element: country, students; Tutor required for teaching: yes Tutor required for feedback: yes Region: SouthKorea; … … … … … …

Context Block: Culture; Metadata Type: Methods for criticism; Related to Contextual Element: country, students; Direct criticism possible: No; Alternative approaches: show alternative, more perfect solution; Region: SouthKorea; … … … … … …

6. The first difference is the language adaptation from German to Korean (this also includes the cultural influ-

ences into language). Furthermore, differences are identified concerning criticism and tutoring require-ments. Those are only a small fraction and a sample for actually identified differences.

7. Change the feedback form for the practical exercises, hire a local tutor who is not only responsible for cor-rections of the practical exercises but also has to support the students while studying (Further changes, i.e. language translation are needed but those are not focused in the example).

By those recommendations it is possible and economically useful to adapt the course taking culture-specific aspects into account. Nevertheless, related parts of the course, which are concerned by the recommendations, have to be identified manually. At least, depending on the course subject type changing needs may be different.

Conclusion

In our paper, we have shown how context metadata can improve the re-use of learning scenarios. We do not limit the context to an organization. We focused on cultural adaptation (and thus culture metadata). The internationaliza-tion of contents helps us to adapt contents and scenarios for a broad re-use. In most cases, it is not necessary to re-write full courses but only small parts. The automatic identification of general change requirements during the adap-tation process does not only provide a higher probability that the adapted courses will be successful, but also enables us implementing successful courses internationally. As a next step, we will analyze how courses can be adapted and provided for developing countries. Not only the principle of reusability is fulfilled at this point, but also the idea to maintain the origin quality independent from the country and language.

The next step in our research is to find ways how to automatically determine as many context metadata as possible, so that once the database is defined, the manual adaptation efforts can be reduced. Some data cannot be automati-cally collected because they are not defined in the needed extent. We will identify solutions on how to define, gather, and update those data and define an automatic collection process including responsible actors.

For the context metadata, we propose to start the standardization process allowing application developers to produce necessary plug-ins for authoring tools, learning management systems, and comparison applications/recommender systems. The comparison of contexts and extraction of changing needs is not only necessary for the adaptation of open content but also for the re-use of commercial contents. At least having available data concerning the context of E-Learning would bring us a big step further on our way to find common methods to internationalize contents.

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