A Hybrid SETO-GBDT Model for Efficient Information Literacy System Evaluation

Abstract

Information literacy (IL) is essential for vocational education talents to thrive in the modern information age. Traditional assessment methods often lack quantitative precision and systematic evaluation models, making it difficult to accurately measure IL levels. This paper aims to develop a robust, data-driven model to assess information literacy in vocational education talents. The goal is to improve the accuracy and efficiency of IL evaluations by combining machine learning techniques with optimization algorithms. The proposed method integrates the Stock Exchange Trading Optimization (SETO) algorithm with the Gradient Boosting Decision Tree (GBDT) to construct the SETO-GBDT model. This model optimizes parameters such as the number of decision trees and tree depth. A comprehensive evaluation index system for IL is built, focusing on learning attitude, process, effect, and practice. The SETO-GBDT model was trained and tested using real-world data on IL indicators. The SETO-GBDT model outperformed traditional models such as Decision Tree, Random Forest, and GBDT optimized by other algorithms like SCA and SELO. Specifically, it achieved an RMSE of 0.13, an R2 of 0.98, and reduced evaluation time to 0.092 s, demonstrating superior accuracy and efficiency. The research concludes that the SETO-GBDT model offers a significant improvement in evaluating IL for vocational education talents. The model’s high accuracy and reduced evaluation time make it an effective tool for assessing and enhancing information literacy, aligning with the educational goals of developing well-rounded, information-savvy professionals.