Co-Learning Model for E-Learning Hint Propensity
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Solution Overview
Problem
Conventional knowledge tracing models fail to accurately predict a learner's propensity to use hints, leading to inefficient learning outcomes as they do not account for the learner's understanding and the substance of the question, and they do not consider the complete sequence of prior interactions.
Innovation Solution
A co-learning model that jointly models knowledge tracing and hint-taking propensity, accepting a question and the learner's current knowledge state to predict probabilities of answering correctly and using a hint, allowing for personalized e-learning plans and updating the knowledge state based on interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the tabling method is used to predict hint-taking probability based on aggregate behaviors of other learners, then the prediction can be made using historical data, but it fails to account for the particular learner's understanding and question substance, leading to poor prediction accuracy
Solution Approach 1:
The patent combines knowledge tracing and hint-taking propensity prediction into a single unified deep learning model. The model jointly processes learner interaction sequences and question representations to simultaneously predict both knowledge state and hint-taking probability, allowing the system to leverage shared features and improve overall prediction accuracy while avoiding the limitations of separate models
Solution Approach 2:
The patent transforms the input representation from simple binary sequences (whether a hint was taken) to rich contextual embeddings that include question difficulty, category, topic, and complete interaction history. This parameter transformation allows the model to capture the substance of questions and learner understanding, significantly improving prediction accuracy
2Reliability
If hints are provided to learners who need them, then learning effectiveness improves, but learners may become dependent on hints and not attempt questions independently, reducing learning efficiency
Solution Approach 1:
The model predicts the probability of hint-taking and uses this information to selectively provide hints only when the learner is likely to need them. By providing hints partially (not always) and strategically based on predicted propensity, the system maintains learning effectiveness while preventing over-reliance on hints, thus preserving learning efficiency
Solution Approach 2:
The system continuously updates the learner's knowledge state and hint-taking propensity based on interaction feedback. This dynamic feedback loop allows the system to adapt hint provision in real-time, providing hints when they are most beneficial and withholding them when the learner is making progress, thereby optimizing both effectiveness and efficiency
3Measurement precision
If the complete sequence of learner interactions is considered, then prediction accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The model processes and encodes the complete interaction sequence in advance to create a compressed representation of the learner's knowledge state and hint-taking propensity. By performing this encoding beforehand, the system captures all relevant information from the complete sequence while reducing the computational burden during actual prediction, thus improving accuracy without excessive computation time during inference
Data Source
AI summary
Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.