A Deep Learning Driven Framework for Teaching Strategy based on Student Behavior Analysis for Recommending Strategies

The digital technologies in education usage now days has resulted in many boost ups of Data driven approaches toward enhancing teaching effectiveness. It is an advanced system that uses deep learning based techniques to analyze students’ behavior and derives insights from Students’ Academic Performance Dataset. A multitude of behavioral indicators from attention, engagement, participation to academic outcomes are processed by the system and recommended personalized teaching strategies based on those patterns. It does so using advanced deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to discover intricate relationships between data, and provides actionable insights for identifying ways to enhance instructional practices. Not only does it include predictive capabilities, but it can actually predict future potential learning challenges so that teachers can take early proactive measures rather than reactively. This leads to learning environment more adaptive to the needs of the learners, both as individuals and groups. We show experimental results on how its deep learning models outperform traditional machine learning methods in detecting behavior patterns and coming up with effective teaching strategies. The proposed model can thus redesign personalized education and enhance the performance of students by enabling data driven, adaptive teaching practices.