Article Gold Open Access 2024

Evaluation of Vocational Education and Training Outcomes Based on Mobile Learning

International Journal of Interactive Mobile Technologies
Journal · Vol. 18 · Issue 19 · pp. 156-170
Abstract

With the rapid advancement of information technology (IT), mobile learning has gradually become a significant approach in vocational education and training. University students utilize mobile applications for learning, which not only enhances the flexibility and effi-ciency of their studies but also promotes the equitable distribution of educational resources. However, effectively evaluating the impact of these applications in vocational education and training remains an urgent issue to be addressed. Current study methods predominantly focus on the analysis of static data, which inadequately captures the dynamic changes in students’ learning behaviors. Additionally, traditional predictive models exhibit low accuracy and poor generalization capabilities when handling high-dimensional, nonlinear time-series data. This study proposes an evaluation method for vocational education and training outcomes based on an improved gated recurrent unit (GRU) model, which comprises three main components: decomposition of university students’ mobile application time-series data based on the variable dependence model (VDM), preprocessing of the mobile application data, and outcome evaluation using the improved GRU model. Incorporation of an attention mech-anism enhances the predictive performance of the model, providing data support and a decision-making basis for educators and developers. © 2024, International Federation of Engineering Education Societies (IFEES). All rights reserved.

Keywords

Author Keywords

Vocational education mobile learning time series attention mechanism gated recurrent unit model (GRU) outcome evaluation

Index Keywords

Apprentices Students Vocational education University students Vocational education and training Federated learning Contrastive Learning Adversarial machine learning Mobile Learning Times series Outcome evaluation Time series Attention mechanisms Gated recurrent unit model Mobile applications Time-series data
Author Affiliations
School of Intelligent Manufacturing, Zibo Vocational Institute, Zibo, China
Funding & Acknowledgements
No funding information
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