Abstract
Understanding phenomena by exploring complex interactions between variables is a challenging task for students of all ages. While the use of simulations to support exploratory learning of complex phenomena is common, students still struggle to make sense of interactive relationships between factors. Here we study the applicability of Problem Solving before Instruction (PS-I) approach to this context. In PS-I, learners are given complex tasks that help them make sense of the domain, prior to receiving instruction on the target concepts. While PS-I has been shown to be effective to teach complex topics, it is yet to show benefits for learning general inquiry skills. Thus, we tested the effect of exploring with simulations before instruction (as opposed to afterward) on the development of a multivariable causality strategy (MVC-strategy). Undergraduate students (N = 71) completed two exploration tasks using simulation about virus transmission. Students completed Task1 either before (Exploration-first condition) or after (Instruction-first condition) instruction related to multivariable causality and completed Task2 at the end of the intervention. Following, they completed transfer Task3 with a simulation on the topic of Predator-Prey relationships. Results showed that Instruction-first improved students’ Efficiency of MVC-strategy on Task1. However, these gaps were gone by Task2. Interestingly, Exploration-first had higher efficiency of MVC-strategy on transfer Task3. These results show that while Exploration-first did not promote performance on the learning activity, it has in fact improved learning on the transfer task, consistent with the PS-I literature. This is the first time that PS-I is found effective in teaching students better exploration strategies.
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Saba, J., Kapur, M., Roll, I. (2023). The Development of Multivariable Causality Strategy: Instruction or Simulation First?. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_4
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