The skills associated with complex problem-solving are essential to sustainability. Although research has demonstrated the importance of problem-solving, insufficient resources exist for educators trying to foster problem-solving skills in their students. Sustainability science refers to science viewed through the lens of systems thinking while taking uncertainty and complexity into account. Computer-based models have been used since 1989 to inform problem-solving and sustainability. Models have been proven to facilitate collaboration, empathy, reflection, and awareness of how others think. This research explored how computer-based problem-solving models can be used in sustainability education to improve how students manage complexity. Specifically, the authors investigated whether a relatively simple computer-based scenario could 1) generate a diversity in participant experience and performance, and 2) replicate real-world challenges associated with complex problem-solving identified in existing literature.
In this research, the authors used Genetics Lab, a complex computer-based problem-solving model. In previous literature, complex problems have been characterized by interconnectivity, non-transparency of connections, dynamic components, contradicting goals, and time-pressure, all of which were represented in the Genetics Lab model. The model includes 12 questions within a 35-minute time limit. Each of the 12 questions involves a fictional creature with specific genes and characteristics. Participants investigated how the creatures' genes impact their characteristics and then the participant was instructed to alter the genes to create a specific creature.
This research analyzed data from 47 participants who completed the Genetics Lab model between 2012 to 2015. Two-thirds of participants were undergraduate and master's students at the University of Luxembourg. The remaining third of participants were employed outside of the university. After using the model, Out of the 47 participants, 28 (60%) completed a questionnaire in which they were asked to reflect on their experience. The data from participant performance on the Genetics Lab model and the data from the questionnaires were analyzed using statistics and for themes.
The results confirmed that participants performed and experienced the Genetics Lab model differently, and replicated phenomena reported in real-world complex problem-solving situations. Data from the model showed participants employed various problem-solving strategies. Following how the methods and amount of time used to explore genetic relationships of the fictional creatures differed, the researchers identified two main categories of participants: active and reflective. Reflective participants took more time to investigate the given genetic information, while active participants tended to progress to the next stage of the questions faster.
The emotional experience of the model varied for the participants, which was evident in the questionnaire responses. Most participants (18) reported negative emotions, such as anxiety, insecurity and frustration; a smaller number (5 participants) reported generally positive emotions, such as enthusiasm, interest, and motivation, and the same number (5 participants) indicated a neutral emotional response to the model. These differences demonstrate the concepts of eustress and distress. Participants were faced with the same challenges, but some had a positive reaction to stress (eustress) while some had a negative response (distress). Both positive and negative groups reported that the best way to solve the complex problems was by understanding single components, or by creating sub-goals and smaller steps within the larger problem. The researchers also found that participants made mistakes commonly identified in real-world problem-solving and systems-thinking challenges. For instance, participants failed to identify or account for the variable of time in the system.
This research is limited by the number of participants and that the questionnaires were not linked to specific model participants. This means that the data on the performance of individuals within the Genetic Lab could not be associated with the reflections and emotional responses of participants. This connection would be valuable to make more informed conclusions about the impacts of the model. This research did not provide evidence that the models improve problem-solving skills of participants. The study took place in Luxemburg, a uniquely small and wealthy country; results may vary in other contexts and locations.
Computer-based problem-solving models can add value to sustainability education by creating and illustrating strategies and phenomena relevant to complex problem-solving. Using the models in conjunction with traditional classroom settings is valuable because it allows students to experience and create a tangible connection to the topics discussed. Using a model with enough complexity to generate differences in performance and experience among participants is essential for a rich post-activity discussion, during which participants can discuss alternate problem-solving strategies and responses. The authors also recommend facilitating written self-reflections after the computer-based model because it can stimulate interesting group discussion.
The Bottom Line
Complex problem-solving is critical to achieving sustainability. This study took place in Luxembourg and asked 47 participants to use a computer-based problem-solving scenario (the Genetics Lab), as well as complete a questionnaire. The researchers found that the Genetics Lab created diversity of participant experience and performance and replicated typical for real-world problem-solving challenges. The authors suggest that students can gain a deeper understanding of complex problem-solving skills and potential errors through participating in similar computer-based models, particularly if used in conjunction with lectures, self-reflection, and group discussion. Although this research does not show that computer-based models can improve problem-solving skills, the authors believe these models have great potential to inform problem-solving educational programs.