Personality-Enhanced Social Recommendations in SAMI: Exploring the Role of Personality Detection in Matchmaking
This work addresses social isolation in online learning environments by incrementally improving matchmaking through personality detection.
The paper tackled the problem of limited social connection in online courses by enhancing a matchmaking system (SAMI) with personality detection, using GPT's zero-shot capability to infer Big-Five traits from forum posts and showing its efficacy in benchmarks, with initial integration suggesting personality can complement existing matching factors.
Social connection is a vital part of learning, yet online course environments present barriers to the organic formation of social groups. SAMI offers one solution by facilitating student connections, but its effectiveness is constrained by an incomplete Theory of Mind, limiting its ability to create an effective mental model of a student. One facet of this is its inability to intuit personality, which may influence the relevance of its recommendations. To explore this, we propose a personality detection model utilizing GPTs zero-shot capability to infer Big-Five personality traits from forum introduction posts, often encouraged in online courses. We benchmark its performance against established models, demonstrating its efficacy in this task. Furthermore, we integrate this model into SAMIs entity-based matchmaking system, enabling personality-informed social recommendations. Initial integration suggests personality traits can complement existing matching factors, though additional evaluation is required to determine their full impact on student engagement and match quality.