International business machines corporation (20240105074). ASSISTING REMOTE EDUCATION LEARNERS simplified abstract
Contents
- 1 ASSISTING REMOTE EDUCATION LEARNERS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ASSISTING REMOTE EDUCATION LEARNERS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ASSISTING REMOTE EDUCATION LEARNERS
Organization Name
international business machines corporation
Inventor(s)
Raman Harishankar of Blacklick OH (US)
Stan Kevin Daley of Espanola NM (US)
Shami Gupta of Bidhan Nagar (IN)
Sabyasachi Chatterjee of Bangalore (IN)
Sandipan Sengupta of Kolkata (IN)
ASSISTING REMOTE EDUCATION LEARNERS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240105074 titled 'ASSISTING REMOTE EDUCATION LEARNERS
Simplified Explanation
The patent application describes techniques for assisting remote education learners by analyzing data collected from different environments to identify non-cognitive learners and provide them with relevant educational material.
- Data is collected from an actual base environment, a simulated base environment, and an exam environment for a learner taking an exam.
- The collected data is used to generate behavior patterns for the learner in the simulated base environment and the exam environment.
- If the behavior pattern in the exam environment deviates beyond a threshold from the simulated base environment, the learner is classified as a non-cognitive learner.
- A de-bias technique is applied to the classification to generate a final classification.
- If the final classification is non-cognitive, education material for the subject covered in the exam is selected and played on the learner's computer.
Potential Applications
This technology can be applied in online education platforms to personalize learning experiences for remote learners based on their behavior patterns and performance in exams.
Problems Solved
This technology helps identify non-cognitive learners who may need additional support or resources to improve their learning outcomes in remote education settings.
Benefits
- Personalized learning experiences for remote learners - Early identification of non-cognitive learners for targeted interventions - Improved educational outcomes for learners in online environments
Potential Commercial Applications
The technology can be integrated into existing online learning platforms to enhance the effectiveness of remote education programs.
Possible Prior Art
There may be prior art related to data analysis techniques for personalized learning experiences in online education platforms, but specific examples are not provided in the patent application.
Unanswered Questions
How does the de-bias technique work in the classification process?
The de-bias technique applied to the classification of non-cognitive learners is mentioned in the patent application, but the specific details of how this technique works are not explained.
What types of educational material are selected for non-cognitive learners?
The patent application mentions that education material for a subject covered in the exam is selected for non-cognitive learners, but it does not specify the criteria or types of material that are chosen for these learners.
Original Abstract Submitted
provided are techniques for assisting remote education learners. data is collected for an actual base environment, a simulated base environment, and an exam environment for a learner taking an exam. the collected data is used to generate a first behavior pattern for the learner in the simulated base environment and a second behavior pattern for the learner in the exam environment. in response to the second behavior pattern deviating beyond a threshold from the first behavior pattern, a classification of non-cognitive learners class is determined for the learner. a de-bias technique is applied to the classification to generate a final classification. in response to the final classification being the non-cognitive learners class, education material for a subject covered in the exam is selected, and the educational material is played on a remote learner computer of the learner.