اثر انگیزش برچسب زدن کاربران در گسترش فراداده منابع آموزشی دیجیتالی

The emerging Web 2.0 applications have allowed new ways of characterizing digital educational resources, which moves from the expert-based descriptions relying on formal classification systems such as the IEEE Learning Object Metadata (LOM) to a less formal user-based tagging. This alternative way of characterizing digital educational resources is commonly referred to as social tagging, whereas the collection of tags created by the different users individually is referred to as folksonomy. As a result, a number of studies have been reported in the field of Technology-enhanced Learning (TeL) which provide evidence that social tagging has the potential to enlarge metadata descriptions, as well as the formal structured vocabularies with additional terms derived by the resulted folksonomy but more in depth studies are needed regarding this enlargement process. Thus, one issue to investigate further is the possible influence of users’ tagging motivation to the resulted enlarged metadata descriptions. In this paper we aim to investigate this issue by first proposing a methodology that aims to evaluate whether users’ tagging motivation can influence (a) the enlargement of educational resources possible descriptions compared to the anticipated creators’ descriptions and (b) the resulted folksonomy compared with formal structured vocabularies used by the creators of the educational resources and then, apply it to an existing LOR with more than 3,000 science education resources, 434 taggers and 14,707 social tags. Our experiments provided evidence that taggers with a specific type of tagging motivation can produce tags that are significantly different from formal metadata generated by the creators of the educational resources.

Over the past years, several Open Educational Resources (OERs) initiatives have been emerged worldwide towards the provision of open access to digital educational resources, in the form of Learning Objects (LOs) such as: video and audio lectures (podcasts), references and readings, workbooks and textbooks, multimedia animations, simulations, experiments and demonstrations, as well as teachers’ guides and lesson plans (McGreal, 2008). UNESCO (2002) has defined Open Educational Resources (OERs) as the “technology-enabled, open provision of educational resources for consultation, use and adaptation by a community of users for non-commercial purposes”. A key objective of OERs initiatives is to support the process of organizing, classifying and storing digital educational resources and their educational metadata in web-based repositories which are referred to as Learning Object Repositories (LORs) ( Lane and McAndrew, 2010 and McGreal, 2004). LORs are mainly developed to facilitate search, retrieval and access to LOs through their metadata descriptions. Within this context, a popular way for describing digital educational resources is by using a formal and centrally agreed classification system, such as the IEEE Learning Object Metadata (LOM) ( IEEE LTSC, 2002). This implies that either the authors of the educational resources or metadata experts will describe the resources with the use of appropriate metadata authoring tools or that automatic mechanisms will be used to generate the educational metadata values. On the other hand, the emerging Web 2.0 applications have allowed for alternative ways of characterizing digital educational resources, which moves from the expert-based descriptions following formal classification systems to a less formal user-based tagging (Anderson, 2007, Bi et al., 2009 and Derntl et al., 2011). This way of characterizing digital educational resources is referred to as Social Tagging and is defined as the process of adding keywords, also known as tags, to any type of digital resource by the users rather than the creators of the resources ( Heymann et al., 2008 and Marlow et al., 2006). Moreover, the collection of tags created by the different users independently is referred to as Folksonomy ( Golder & Huberman, 2006) and it can constitute an alternative (superset or subset) of the corresponding taxonomy used from the metadata experts. Social tagging of educational resources is an important issue to study since educational resources are not meant to be used only by their creators, but ideally to be re-used in different context and different purposes. Thus, mechanisms to capture the re-contextualisation process are important so that eventually educational resources will not only carry their creators’ anticipated contextual value but other users’ re-contextualisation too. This can enhance both educational resources’ searchability (for various context of use) and their metadata descriptions. In the field of Technology Enhanced Learning (TeL), a number of studies have been reported aiming to investigate this issue, that is: (a) the anticipated added value of social tagging when searching digital educational resources stored in LORs and compare it with the traditional approach of searching based on expert-based formal descriptions following centrally agreed classification systems, such as IEEE LOM and (b) the enlargement of educational resources possible description compared to the anticipated creators’ descriptions following centrally agreed classification systems, such as IEEE LOM (Trant, 2009b and Vuorikari and Ayre, 2009). Additionally, recent studies in the field of social tagging systems suggests that users’ tagging motivation has a direct influence on the properties of resulting tags and folksonomies (Gupta et al., 2010, Korner, 2009 and Korner et al., 2010) but there are not existing studies for investigating this issue in the field of TeL. To this end, in this paper we aim to investigate this issue and we propose a methodology that aims to investigate whether users’ tagging motivation can influence (a) the enlargement of educational resources possible descriptions compared to the anticipated creators’ descriptions and (b) the resulted folksonomy compared with formal structured vocabularies used by the creators of the educational resources. The application of the proposed methodology in an existing LOR, namely the OpenScienceResources Repository (http://www.osrportal.eu/) provided us evidence that taggers with a specific type of tagging motivation can produce tags that are significantly different from formal metadata generated by the creators’ of the educational resources. The paper is structured as follows. Following this introduction, Section 2 discusses the concept of social tagging of educational resources, its expected benefits and provides an overview of related studies that investigate the enlargement of educational resources possible description compared to the anticipated creators’ descriptions. In Section 3, we present our proposed methodology for identifying different types of users’ tagging motivation and investigating their possible influence to the enlargement of metadata descriptions of digital educational resources, as well as to the resulted folksonomy compared to formal structured vocabularies used by the creators of the educational resources. Section 4 provides an overview of the OpenScienceResources (OSR) Repository, which was used for applying our proposed methodology, as well as the social tagging tool used for tagging the educational resources stored in OSR Repository and the dataset that was available at the time of our study. In Section 5, we present the results from the application of our methodology and we discuss our findings. Finally, we present our conclusions and ideas for future work.

The emerging Web 2.0 applications have noticeably influenced the process of characterizing digital educational resources stored in LORs. More precisely, the traditional approach of describing educational resources based on IEEE LOM standard is combined with the social tagging approach, where the generation of metadata is done also by the users of the educational resources and not only by the creators of the resources. There are several expected advantages from this combined approach including enhancement of educational resources searchability, as well as enlargement of their metadata descriptions. Within this context, several studies have investigated the anticipated benefits of social tagging and they have provided evidence about its potential to improve search of educational resources and enlarge their metadata descriptions. Nevertheless, more in-depth studies are needed regarding this enlargement process such as the investigation of users’ tagging motivation to the resulted enlarged metadata descriptions. In this paper, we investigated the influence of different tagging motivations, namely describers and categorizers on the enlargement of the metadata descriptions of digital educational resources. The results of our study performed with the dataset of an existing LOR, namely the OSR Repository showed that: • Describers produce tags that are significantly different from formal metadata, whereas categorizers mainly follow the formal metadata generated by metadata experts or content providers. • Describers outperformed categorizers by contributing 36 more new terms to the structured formal vocabulary used by content providers to characterize science education resources in the OSR Repository. These findings are useful, because they could be a first step for future identification of describers’ contextual value of educational resources, which are different from the creators’ contextual value. Moreover, they could be used in future LORs’ design for automatically calculating tagging motivation and considering describers’ tags for semi-automatically classifying digital educational resources towards improving search and retrieval of digital educational resources. Future work will focus on: (a) deeper quantitative analysis to the results of our study, so as to identify the possible effect of describers’ and categorizers’ social tags to the enlargement of metadata descriptions for digital educational resources with different granularity levels and different formats and (b) deeper qualitative analysis to the enlarged metadata descriptions produced by describers, which have been proved significantly different from formal metadata. This is important because we will be able to identify the types of educational resources that receive users’ tags, which are different from formal metadata and finally we will be able to identify different contexts of use (based on describers’ tags) for specific educational resources. Finally, the results of our study and especially the discrimination of the users as categorizers and describers could be used for future studies that could aim to (a) investigate the benefits of searching based on categorizers’ or describers’ tags compared to searching based on expert-based formal descriptions and (b) analyze clusters of tags that are produced by describers and categorizers towards identifying smaller user groups, who share a commonly-understood meaning of tags and potentially common interests.