More, May Not the Better: Insights from Applying Deep Reinforcement Learning for Pedagogical Policy Induction
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| Title: | More, May Not the Better: Insights from Applying Deep Reinforcement Learning for Pedagogical Policy Induction |
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| Language: | English |
| Authors: | Gyuhun Jung, Markel Sanz Ausin, Tiffany Barnes, Min Chi |
| Source: | International Educational Data Mining Society. 2024. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 11 |
| Publication Date: | 2024 |
| Sponsoring Agency: | National Science Foundation (NSF) |
| Contract Number: | 1726550 1651909 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Intelligent Tutoring Systems, Problem Solving, Artificial Intelligence, Teaching Methods, Decision Making, Undergraduate Students, Computer Science Education |
| Abstract: | We presented two empirical studies to assess the efficacy of two Deep Reinforcement Learning (DRL) frameworks on two distinct Intelligent Tutoring Systems (ITSs) to exploring the impact of Worked Example (WE) and Problem Solving (PS) on student learning. The first study was conducted on a probability tutor where we applied a classic DRL to induce policies using the training data collected from the "same tutor." The second one was conducted on a logic tutor by leveraging a Multi-Task DRL framework to induce a Unified-DRL (U-DRL) policy from two related training datasets collected from the probability and logic tutors. Overall our results found that in the first study, the DRL policy significantly out-performs the Expert policy but no significant difference was found between the two policies on the number of PS and WE received. For the second study, while no significant difference between U-DRL and the Expert policy across various learning performance, the U-DRL students received significantly more PS and less WE than the latter. In short, our findings shows that 1) the efficacy of DRL policies is not necessarily enhanced when trained with multiple task-related datasets compared to a single source dataset; 2) the effectiveness lies not in "how much" PS and WE exposure students receive, but rather in "how and when" they are delivered. [For the complete proceedings, see ED675485.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED675660 |
| Database: | ERIC |
| Abstract: | We presented two empirical studies to assess the efficacy of two Deep Reinforcement Learning (DRL) frameworks on two distinct Intelligent Tutoring Systems (ITSs) to exploring the impact of Worked Example (WE) and Problem Solving (PS) on student learning. The first study was conducted on a probability tutor where we applied a classic DRL to induce policies using the training data collected from the "same tutor." The second one was conducted on a logic tutor by leveraging a Multi-Task DRL framework to induce a Unified-DRL (U-DRL) policy from two related training datasets collected from the probability and logic tutors. Overall our results found that in the first study, the DRL policy significantly out-performs the Expert policy but no significant difference was found between the two policies on the number of PS and WE received. For the second study, while no significant difference between U-DRL and the Expert policy across various learning performance, the U-DRL students received significantly more PS and less WE than the latter. In short, our findings shows that 1) the efficacy of DRL policies is not necessarily enhanced when trained with multiple task-related datasets compared to a single source dataset; 2) the effectiveness lies not in "how much" PS and WE exposure students receive, but rather in "how and when" they are delivered. [For the complete proceedings, see ED675485.] |
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