Adaptive Learning Question System for Personalized Content Delivery
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Solution Overview
Problem
Conventional online learning systems fail to adapt the form of questions based on individual learners' question solving types, leading to suboptimal learning experiences.
Innovation Solution
An apparatus and method that determine a learner's question solving type by analyzing their interaction with questions, including eye movement tracking and answer evaluation, to dynamically adjust the output form of questions on a learning screen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standardized questions are provided to all learners, then the system complexity is low and implementation is easy, but the learning effectiveness and understanding degree are insufficient
Solution Approach 1:
The system performs preliminary classification of learners into different question-solving types (visual, auditory, reading-writing) before delivering questions. This preliminary action enables the system to prepare and deliver appropriately formatted questions in advance, resolving the contradiction by establishing adaptability through pre-defined categories without requiring complex real-time analysis infrastructure
Solution Approach 2:
The system introduces an intermediary classification mechanism that mediates between standardized question content and individual learner needs. This intermediary layer translates uniform question material into multiple presentation formats based on learner type, achieving adaptability without requiring fundamental changes to the question bank or complex customization logic
2Adaptability or versatility
If the same question is presented in a single format, then the implementation is simple, but learners with different solving types cannot concentrate effectively and understanding is reduced
Solution Approach 1:
The system segments question delivery into distinct format types (visual presentation, auditory presentation, reading-writing presentation) corresponding to different learner types. Each question can be delivered in one of several segmented formats, allowing learners to concentrate effectively on their preferred modality while maintaining operational simplicity through clear format categorization
Solution Approach 2:
The system applies local quality by tailoring the presentation format of each question to match the specific learner's solving type. Visual learners receive visually-emphasized questions, auditory learners receive audio-based questions, and reading-writing learners receive text-based questions with writing tasks, thereby enhancing concentration and understanding without requiring complete system redesign
3Productivity
If conventional online learning provides standardized questions without considering solving types, then the system is easy to operate, but learning effectiveness and answer accuracy are limited
Solution Approach 1:
The system implements self-service by automatically analyzing learner responses and eye movement data to identify question-solving types, then autonomously updating the question delivery format without requiring manual intervention. This self-service mechanism improves learning effectiveness through personalized delivery while containing complexity through automated decision-making algorithms
Solution Approach 2:
The system incorporates feedback loops where learner performance data and eye movement patterns are continuously analyzed to refine question-type matching. This feedback mechanism progressively improves learning effectiveness and answer accuracy by adjusting question formats based on actual learner responses, while managing complexity through iterative optimization rather than complex upfront design
Data Source
AI summary
Disclosed are an apparatus and method for providing questions for learning, which can determine the question solving type of a learner during a question solving process, and change the output form of a question based on the question solving type. The apparatus and method may output a learning screen on which the text and problem of a question are outputted at different time points according to the question solving type of a learner, continuously determine the question solving type of the learner through eye tracking, and update the question solving type of the learner.