A group of scientists from FIZ CHEMIE and the Freie Universität Berlin (FU) are researching into facilities and technologies to support future self-study on the ChemgaPedia education platform using dynamically-generated learning recommendations. The aim of the research is an e-learning education environment that suggests learning content along a didactically-structured learning path, taking into account the individual educational requirements of the user.
Richard Huber, Head of Marketing & Communication at FIZ CHEMIE and a member of the development group explained, "The system guides students in the e-learning environments just like a teacher. The suggestions will guide the student along a completely individual learning path, dynamically generated just for this person". The prototype of the ChemgaPedia Recommender will be presented in early December at the Online Educa conference exhibition in Berlin (1. - 3.12.10, Hotel InterContinental).
Dynamically-generated recommendations are known from online stores that can tell you what other buyers of the product you have just purchased also bought, and from Google when making a spelling mistake: "Did you mean…". The research team, including Alexandru-Aurelian Todor, Sebastian Krebs, Ralf Heese and Professor Adrian Paschke from the Institute of Computer Science at the FU, is using comparable technologies to develop the learning-trajectory generator for ChemgaPedia. These are described as semantic-data and linked-data technologies.
The conditions required for being able to generate useful personalized recommendations are differentiated user data on the one hand and suitable specialist ontologies on the other. Ontologies transform human knowledge, contexts and specialist dependencies into a special, machine-processable format. The RDF schema used for ChemgaPedia allows specialized software to recognize relationships between information, and thus can capture contexts for the computer.
The developers of the learning-path generator for ChemgaPedia are semantically preparing the metadata of the ChemgaPedia learning units, their complete texts, data from user monitoring, as well as links and chemical entities in order to build the necessary ontologies, and these are then compared with existing chemistry ontologies. A user interface has been built upon this extensive network of connected information, which provides the student with expanded recommendations on the subject and learning suggestions through the linking that already exists in ChemgaPedia.
The suggested learning steps and learning paths are derived from the subject currently being worked on and user behavior, as well as a comparison and analysis of the content of existing specialist chemistry ontologies (DB-Pedia, PubChem and similar), without the respective user and his/her personal profile being known. However, in the future a facility should also be provided for generating personalized learning paths that suit individual levels of education, such as the syllabus for the third semester. In order to be able to technically map this function, personal details on prior knowledge, field of study, term and similar facts are required. The scientists hope to be able to collect this data in future on a voluntary basis. In this way, the learning path generator for e-learning environments could be further developed to become a personal learning assistant.
With 18,000 pages, 25,000 media objects and 350,000 users per month, ChemgaPedia is the most extensive and most used scientific education platform in the German-speaking area. It can be used without the need for personal registration. The learning contents cover the complete bandwidth of specialist chemical knowledge needed when studying for a Bachelor´s degree; related subjects from pharmaceutics and life sciences are also included.
For additional InformationFIZ CHEMIE
All statements in this press release which are not of a historical character refer to the future in the sense of U.S. security law. The predictive statements are assumptions which are based on the current state of information and consequently are subject to particular uncertainty factors. Events which actually occur can deviate considerably from those predicted due to many factors, for example as a result of changes in technology, product development or production, market acceptance, costs or prices for products of FIZ CHEMIE and dependence on alliances and partners, approval processes, competition, intellectual property or patent protection and copyrights
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