Growth and fixed mindset classification using NLP approach - Topkoc, Ege (Open Science Journal, 2024)
Theories regarding the growth and fixed mindsets have emerged in the last couple of decades. It focuses on how peoples’ brains think and handle problems. People with a fixed mindset tend to feel they cannot improve or overcome difficult situations. On the other hand, people with a growth mindset tend to focus on the process and believe they can improve no matter where they started. We hypothesized that these mindsets can also be detected from text. Our goal was to design an NLP framework to classify sentences as growth or fixed mindsets. We used data generated by a Large Language Model (LLM) for our dataset: around 2000 sentences. We discovered a relationship between the sentiment of the sentence and the mindset type. Our model was a merged model which extracted features from words using word embeddings and used manually extracted features such as sentiment scores. A bidirectional Long Short-Term Memory (LSTM) was used to provide more context to both the beginning as well as the end of sentence. The final model had an F1 score of 0.99. The model can be improved by using a greater dataset, preferably created by humans instead of an AI.