TEXT № 4. Learner Modeling on the Semantic WebTASK: Read and translate the following text. Learn the vocabulary by heart. Learners are assessed by several systems during their lifelong learning. Those systems can maintain fragments of information about a learner derived from his learning performance and/or assessment in that particular system. Customization services would perform better if they would be able to exchange as many relevant fragments of information about the learner as possible. This paper presents the conceptualization and implementation of a framework which provides a common base for the exchange of learner profiles between several sources. The exchange representation of learner profiles is based on standards. An API is designed and implemented to create/export and manipulate such learner profiles. The API is implemented for two cases, as a Java API and as web services with synchronized model exchange between multiple sources. Application cases of the API are discussed shortly as well. Furthermore, the process of importing learner models from foreign systems is analyzed. Possible conflicts are discussed and conflict handling in different types of learner systems is described. Each user adapted service or application needs a user profile to perform the adaptation accordingly. In the area of education, several approaches have been proposed to collect information about users such as preferences, following clicking behavior to collect likes and dislikes, and questionnaires asking for specific information to assess learner features (e.g. tests, learner assessment dialogs, and preference forms). In addition, several tools have been designed to improve learner models by open active learner modeling. The variety of use cases are supported by such tools like maintaining and comparing the student's own and the system's believes about his knowledge, multiple choice questionnaires, collaborative peer assessment in discussions, and dialogues with interactive topic maps. These systems can be seen as services to improve user or learner models in open environments. Different users may prefer a different style of evaluation and thus may want to choose one or more of them which are the most suitable for them to evaluate their profiles. To benefit from such heterogeneous services, an interoperable learner profile and an infrastructure to support its exchange should be provided. The following questions arise: how to represent the learner profile, how to access the learner profile, and how to provide an extensible API to process heterogeneous profiles. The remainder of the paper is structured as follows: Section 2 discusses standard based representations of learner profiles, their instantiation, and mappings from internal data models. Section 3 discusses howT the models can be accessed by means of a Java API, web services, querying infrastructure for RDF, and application cases which have been implemented. The import process of learner models is explained in Section 4. Finally, section 5 provides a summary and an outline of possible further work. In order to be able to exchange a learner profile between e-Learning and learner assessment systems, we need to provide explicit information about what is going to be exchanged, which values of the specific subject are considered and how the information is bound to a learner. Learner profile standards and open specifications provide us with a representation for subjects of exchange, e.g. learner performance, portfolio, preferences, learning style, certificates, evaluations, and assessment. Domain ontologies provide us with exchangeable/sharable models of domains. Such ontologies can model either the domain which will be overlaid in the learner profile, learner competencies/skills, or can model stereotype structures.
Accessing the Learner Profile. Access through Java API. We build a Java API which is structured according to the learner profile fragments mentioned above. The API is meant to be used to retrieve, insert, and update the learner profiles stored in the structures described above. The API defines a class and properties for each class from the RDFS tru the learner model. The interface provides access functions for getting, deleting and updating a model of the fragment. It provides further functions to derive additional information or to process more complex manipulations over referenced types as well. The API is implemented for the RDF representation (instances of the RDFS described above). The API is easily extensible by providing further specializations if additional extensions and interface implementations for local repositories and data models are needed. Access through API as Web Services. The second implementation is provided through web services where several clients can access one model which is persistent on one server. The server holds the main model, i.e. the data of a learner profile gathered from several sources, and handles all requests from the clients. Each client is uniquely identified at the server and can be used by a browsing or assessment system. Furthermore, a client can be used by other learning systems which want to make use of the learner profiles or which want to contribute to them. The model can be accessed directly by invoking functions of a web service or in a synchronized replicated way; i.e. each client has its own repository which is synchronized with the main server every time a change occurs. The web services framework can be used in a distributed way as well (several servers exchanging learner models between each other). (5477) Vocabulary: to assess - оценивать heterogeneous services – разнородные услуги conceptualization – представление, понимание implementation – осуществление framework – рамка, структура API (Application Programming Interface) – программный интерфейс приложения learner profiles – данные об ученике handling – обработка to evaluate – оценивать сustomization - изготовление browsing system – система просмотра deleting - удаление to replicate - повторять to occur – возникать invoking function – функция запроса extensible – растяжимый to update – улучшать application – приложение variety – разнообразие relevant fragment – важный фрагмент
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