# Preparing Unprepared Students For Future Learning

arXiv: https://arxiv.org/abs/2303.11960 · HTML: https://mlsanity.com/paper/2303.11960/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Leveraging Deep Reinforcement Learning for Metacognitive Interventions across Intelligent Tutoring Systems](https://mlsanity.com/paper/2304.09821/) (2023)
- [Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement](https://mlsanity.com/paper/2405.06218/) (2024)
- [Bridging Declarative, Procedural, and Conditional Metacognitive Knowledge Gap Using Deep Reinforcement Learning](https://mlsanity.com/paper/2304.11739/) (2023)

## Abstract (arXiv)

Based on strategy-awareness (knowing which problem-solving strategy to use) and time-awareness (knowing when to use it), students are categorized into Rote (neither type of awareness), Dabbler (strategy-aware only) or Selective (both types of awareness). It was shown that Selective is often significantly more prepared for future learning than Rote and Dabbler (Abdelshiheed et al., 2020). In this work, we explore the impact of explicit strategy instruction on Rote and Dabbler students across two domains: logic and probability. During the logic instruction, our logic tutor handles both Forward-Chaining (FC) and Backward-Chaining (BC) strategies, with FC being the default; the Experimental condition is taught how to use BC via worked examples and when to use it via prompts. Six weeks later, all students are trained on a probability tutor that supports BC only. Our results show that Experimental significantly outperforms Control in both domains, and Experimental Rote catches up with Selective.