Mental model development and reasoning about a causal system in a computer-based inquiry environment

We live in a world of complex relationships. Discovering truths in this complexity requires an ability to isolate causality amidst infinite associations. Underlying human use of advanced investigative skills are cohesive mental representations of external causal systems. Development of “dynamic” and “additive effect” mental models lead to use of better inquiry strategies and knowledge acquisition. This study investigated whether: (1) mechanistic information about system components leads to generation of a mental simulation of how and why factors are or are not causal (dynamic mental model), (2) demonstration mechanism that more than one factor can contribute to an outcome (additive effect mental model), and (3) development of mental models lead to advances in reasoning.; The first experiment tested whether providing information about mechanistic qualities of factors within a system during reasoning activity increased dynamic mental depictions of how and why factors vary together. Two middle-school groups were compared on mental model development and reasoning. Results show mechanistic information stimulates development of more complex and dynamic mental models, leading to better knowledge acquisition and slightly better inquiry strategies, but not greater prediction accuracy.; The second experiment replicates the first, and in addition investigates whether providing visualization of system components in Geographic Information System (GIS) map layers encourages understanding that more than one factor can contribute to the same outcome (additive effect mental model), leading to more advanced reasoning. Four middle-school groups were compared on mental model development and reasoning: (1) one group was provided mechanistic information, (2) another group was provided GIS demonstration, (3) a third group was provided mechanistic information and GIS demonstration, (4) a fourth group was provided neither treatment. Results reveal GIS demonstration stimulates development of additive effect mental models, inducing better inquiry strategies, more accurate prediction and better knowledge acquisition. Mechanistic information impacts development of mental model complexity and dynamism, leading to better inquiries and better knowledge acquisition, but not greater accuracy in prediction.