# Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2304.11739 · HTML: https://mlsanity.com/paper/2304.11739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 58 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems](https://mlsanity.com/paper/2304.09821/) (2023)
- [HOPE: Human-Centric Off-Policy Evaluation for E-Learning and Healthcare](https://mlsanity.com/paper/2302.09212/) (2023)
- [Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving](https://mlsanity.com/paper/2208.04696/) (2022)
- [Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning](https://mlsanity.com/paper/2303.13541/) (2023)
- [Assessing Competency Using Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning](https://mlsanity.com/paper/2303.14609/) (2023)
- [Preparing Unprepared Students For Future Learning](https://mlsanity.com/paper/2303.11960/) (2023)
- [The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems](https://mlsanity.com/paper/2303.11965/) (2023)
- [Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer](https://mlsanity.com/paper/2303.12223/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Generalized Apprenticeship Learning Framework for Capturing Evolving Student Pedagogical Strategies](https://mlsanity.com/paper/2602.20527/) (2026)

## Abstract (arXiv)

In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.