DEEPMIND: A Deep Learning Model with RNN and Attention for Personalized Mental Health Education Effectiveness Assessment and Content Optimization
Abstract
Mental health education plays a critical role in improving psychological well-being and awareness, particularly in educational and organizational settings. Evaluating its effectiveness and optimizing the content delivery can significantly enhance engagement and learning outcomes. Traditional assessment methods often rely on manual surveys and static analytics, which are limited in personalization and fail to capture complex behavioral patterns. These approaches lack adaptability and struggle to provide real-time feedback for content improvement. To address these limitations, this paper proposes a novel deep learning-based framework called DEEPMIND (Deep Engagement and Effectiveness Prediction Model Integrating Neural Decisioning). DEEPMIND utilizes recurrent neural networks (RNNs) and attention mechanisms to analyze learner interaction data and sentiment patterns for assessing educational effectiveness. The proposed method dynamically evaluates learner responses, behavioral signals, and content features to recommend personalized content optimizations. By integrating natural language processing (NLP) and deep feature extraction, DEEPMIND continuously improves content relevance and user engagement. Experimental results demonstrate that DEEPMIND outperforms baseline models in accuracy, adaptability, and content enhancement, leading to a 25% increase in learner retention and a 30% improvement in mental health knowledge acquisition. The model has been trained and tested using the Sentiment Analysis for Mental Health dataset, which comprises 10,000 labeled samples. It separated the training and testing sets into five groups and utilized an 80:20 ratio. DEEPMIND has performed significantly better than baseline algorithms in predicting engagement (91.2%), detecting sentiment (89.4%), and adapting (87.6%).References
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