In this work, we investigate how two factors, metacognitiveskills and motivation, would impact student learning acrossdomains. More specifically, our primary goal is to identify thecritical, yet robust, interaction patterns of these two factors thatwould contribute to students’ performance in learning logicfirst and then their performance on a subsequent new domain,probability. We are concerned with two types of metacognitiveskills: strategy-awareness and time-awareness, that is, whichproblem-solving strategy to use and when to use it. Our datawere collected from 495 participants across three consecutivesemesters, and our results show that the only students who con-sistently outperform their peers across both domains are thosewho are not only highly motivated but also strategy-aware andtime-aware.

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.