Fear Emotion Decelerates Outcomes of Climate Change Education

Emotion mediating educational outcomes has been recognized for decades. However, empirical experiments to test these predictions, particularly for climate change education, which is often mixed with various emotions, are rare. In this study, we conducted a two-week climate change education program with specific video clips designed to induce fear or hope in students to explore how emotions affect educational outcomes. The study involved 1,730 students from nine middle schools in three coastal cities (Xiamen, Shenzhen, and Ningbo) in China. The results demonstrated that emotional video clips are a successful stimulus for the target emotion. In the hope treatment group, emotion did not significantly affect the educational outcomes, as indicated by the limited change in students’ climate change involvement, self-efficiency, and mitigation behavior. However, in the fear treatment group, emotion significantly decelerated students’ change in mitigation behavior compared to the lecture-only group. These decelerated behaviors are mostly located at the behavior change of low carbon life. Based on the mediation analysis, both the hope group and lecture-only group had direct effects on climate change mitigation behavior, while the fear group only had an indirect effect on climate change behavior, mediated primarily by climate change involvement. The study thus highlighted that both negative and positive emotions should not indiscriminately used in climate change education programs to safeguard significant educational outcomes.

Applying the Affective Aware Pseudo Association Method to Enhance the Top-N Recommendations Distribution to Users in Group Emotion Recommender Systems

Recommender Systems are a subclass of information retrieval systems, or more succinctly, a class of information filtering systems that seeks to predict how close is the match of the user’s preference to a recommended item. A common approach for making recommendations for a user group is to extend Personalized Recommender Systems’ capability. This approach gives the impression that group recommendations are retrofits of the Personalized Recommender Systems. Moreover, such an approach not taken the dynamics of group emotion and individual emotion into the consideration in making top-N recommendations. Recommending items to a group of two or more users has certainly raised unique challenges in group behaviors that influence group decision-making that researchers only partially understand. This study applies the Affective Aware Pseudo Association Method in studying group formation and dynamics in group decision making. The method shows its adaptability to group's moods change when making recommendations.