Abstract
Multiple goals balancing is an important but not yet fully validated dimension of complex problem solving (CPS). The present study used process data to explore how solvers clarify goals, set priorities, and balance conflicting goals. We extracted behavioral indicators of goal pursuit from the log data of 3,201 students on the third subtask of the “Ticket” task in the PISA 2012 CPS test. Cluster analysis was used to identify 10 groups that varied in goal pursuit behavior. Logistics and least-squares regression analysis were used to explore how goal pursuit affected task scores and CPS proficiency. The results showed that competent solvers clarified goals and weighed priorities more effectively. They also made trade-offs between conflicting goals. The importance of theoretically-driven log data analysis and coping strategies in the face of multiple goals conflict scenarios was discussed.
Highlights
Science and technology are developing in the current information explosion era
The purpose of the present study is to test and supplement the theoretical discussion of multiple goals balancing in the literature based on the meaningful complex problem solving (CPS) behavior sequences contained in the log data
Results showed that the third “Ticket” subtask score was moderately correlated with CPS proficiency (r = 0.39)
Summary
Science and technology are developing in the current information explosion era. People are facing an increasing number of complex problems in daily life, many of which involving the simultaneous pursuit of multiple goals. Complex problem solving (CPS) becomes common in real life, such as the use of complex technology (e.g., mobile phones, personal computers, and vending machines), the management of complex organizations (e.g., companies and departments), and the prediction of complex environments (e.g., weather and stock prices; Funke, 2003, 2010). Complex problem solving refers to successful interaction with a dynamic task environment, wherein all or some rules in the environment can only be learned by exploring and integrating information (Buchner, 1995). Solvers are asked to manipulate some of the variables to explore effective rules of describing relationships among all variables (knowledge acquisition), and solvers need to use the learned knowledge of rules to achieve specific goals (knowledge application; Funke, 2001)
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