Year: 2024
Type: article
Abstract: Information retrieval aims to find the most important data for specific queries. The challenge is retrieving relevant data efficiently due to the large search area. Existing solutions lead to unnecessary processing costs. Additionally, identifying the main focus of the query is crucial for targeted retrieval. Current methods struggle to address these issues effectively. To overcome these challenges, we have proposed a goal-question-indicator (GQI) approach for personalized learning inquiry (PLA). This approach allows for efficient retrieval of variable-sized data with reduced processing requirements. We have also presented the open learning analytics platform's (Open-LAP) pointer motor segment, which helps end users specify goals, generates discussion topics, and provides self-characterizing pointers.
Institution: University of Engineering & Management
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $150
OpenAlex ID: W4410064407