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#5670. Personalized task difficulty adaptation based on reinforcement learning
| May 2027 | publication date |
| Proposal available till | 28-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Education |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5670.1   | Contract №5670.2 | Contract №5670.3 | Contract №5670.4 |
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Traditionally, the task difficulty level is often determined by domain experts based on some hand-crafted rules. However, with the adoption of Massive Open Online Courses (MOOCs), it has become harder to manually personalize task difficulty as the system designers are faced with a very large question bank and a user base of individuals with diverse backgrounds and ability levels. This research focuses on developing a data-driven method to adaptively adjust difficulty levels in order to maintain a target user performance level over a series of tasks whose difficulty level is highly variable among different individuals. Specifically, the issue of difficulty adaptation was formulated as a reinforcement learning problem. To ensure responsiveness of the interactive systems, a novel bootstrapped policy gradient (BPG) framework was developed, which can incorporate prior knowledge of difficulty ranking into policy gradient to enhance sample efficiency. To obtain high-quality prior information on difficulty ranking, a clustering-based approach was proposed which can learn a personalized difficulty ranking to capture users’ individual differences.
Keywords:
Clustering; Difficulty Adaptation; Intelligent Tutoring System; Reinforcement Learning
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
Traditionally, the task difficulty level is often determined by domain experts based on some hand-crafted rules. However, with the adoption of Massive Open Online Courses (MOOCs), it has become harder to manually personalize task difficulty as the system designers are faced with a very large question bank and a user base of individuals with diverse backgrounds and ability levels. This research focuses on developing a data-driven method to adaptively adjust difficulty levels in order to maintain a target user performance level over a series of tasks whose difficulty level is highly variable among different individuals. Specifically, the issue of difficulty adaptation was formulated as a reinforcement learning problem. To ensure responsiveness of the interactive systems, a novel bootstrapped policy gradient (BPG) framework was developed, which can incorporate prior knowledge of difficulty ranking into policy gradient to enhance sample efficiency. To obtain high-quality prior information on difficulty ranking, a clustering-based approach was proposed which can learn a personalized difficulty ranking to capture users’ individual differences.
Keywords:
Clustering; Difficulty Adaptation; Intelligent Tutoring System; Reinforcement Learning
Contacts :
