Undergraduate Research Database Recommendation System (URDRS)
University of California System: University of California, Merced
posted on 04/15/2011
The research projects conducted by undergraduate students are frequently driven by the need to fulfill the requirements of specific assignments within the framework of formal course curricular. It is desirable to have an internet-accessible software tool that supports students to discover and access library and other information resources to complete scholarly research projects related to their coursework. The library information retrieval system is not designed to recommend broad publication databases, rather than specific documents, in response to user queries. However, the undergraduate researchers are often unfamiliar with the expert vocabulary of the subject they are researching or the organization of scholarly literature. Therefore, it is a challenge for them to effectively retrieve the most relevant scholarly information.
This invention is generally useful for the undergraduate students to conduct course-based research. URDRS can be integrated into a web site, catalog, course management system, and other web-accessible resource.
The novel software system enables the student researchers to effectively retrieve broad and relevant information by simply querying the course information. It also enhances the opportunity for the students to expand their basic information literary.
Researchers at the University of California, Merced have developed an easy-to-use, internet-based software tool, Undergraduate Research Database Recommendation System (URDRS), that uses a locally managed knowledge base coupled with the machine learning methods to increase the success of undergraduate students as they attempt to discover and access information resources needed to complete scholarly research projects related to their coursework.
URDRS supports course-based research, prompting the user to volunteer information about the education context of the current query, potentially including course-identity information and even specific course-assignment information. Ranked results are to be provided in a manner that facilitates subsequent access to the recommended databases. On the back end, knowledge base are formed with data from campus course catalog, syllabi, faulty web pages, course reading lists, etc. It can be updated and modified easily, and allows the instructors and librarians to add content and limits to further enhance student success.
The system is expected to generate and store anonymized query transaction records in a separate database, which can be further analyzed using machine learning methods to improve and expand upon the recommendations made by the system. The system is scalable and can save librarians significant time for repetitive reference, instruction, development of online tutorials and web site tinkering.
File Number: 21480
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This innovation currently is not available for online licensing. Please contact David Cepoi at University of California System: University of California, Merced for more information.request more info
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