TY - GEN
T1 - The Development of Multivariable Causality Strategy
T2 - 24th International Conference on Artificial Intelligence in Education, AIED 2023
AU - Saba, Janan
AU - Kapur, Manu
AU - Roll, Ido
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Interactive simulation
KW - Multivariable causality strategy
KW - exploratory learning
UR - http://www.scopus.com/inward/record.url?scp=85164930882&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-36272-9_4
DO - https://doi.org/10.1007/978-3-031-36272-9_4
M3 - منشور من مؤتمر
SN - 9783031362712
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 53
BT - Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
A2 - Wang, Ning
A2 - Rebolledo-Mendez, Genaro
A2 - Matsuda, Noboru
A2 - Santos, Olga C.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 July 2023 through 7 July 2023
ER -