A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning
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.
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References
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