Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model
US10943497B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 27, 2018 |
| Grant date | Mar 9, 2021 |
| Priority date | — |
| Expiry date | May 16, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG09B5/065
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.