The Development of Multivariable Causality Strategy: Instruction or Simulation First?

Janan Saba, Manu Kapur, Ido Roll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
EditorsNing Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031362712
StatePublished - 2023
Event24th International Conference on Artificial Intelligence in Education, AIED 2023 - Tokyo, Japan
Duration: 3 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13916 LNAI


Conference24th International Conference on Artificial Intelligence in Education, AIED 2023


  • Interactive simulation
  • Multivariable causality strategy
  • exploratory learning

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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