Utilizing LSTM and transformer models to analyze and predict potential career paths through student scores

Abstract

Long Short-Term Memory (LSTM) models are a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data, and mitigate the vanishing gradient problem that limits traditional RNNs. LSTMs achieve this by using gates that control the flow of information, allowing memory maintenance over time. In contrast, Transformer models, which rely on self-attention mechanisms rather than recurrence, have revolutionized the field of natural language processing. Transformers enable parallel processing of sequences, making them more efficient and scalable for tasks like language translation and text generation. While LSTMs prove to be effective for certain sequential tasks, Transformers have generally shown greater performance due to their ability to handle longer sequences and capture complex dependencies. The advent of sophisticated machine learning techniques has revolutionized the field of predictive analytics, particularly in the realm of education. This article explores the utilization of Long Short-Term Memory (LSTM) and Transformer models to analyze and predict potential career paths based on student scores. By leveraging these advanced models, educational institutions can better understand student strengths and career suitability, ultimately leading to a more personalized career guidance.