Multi-Task Learning-Based Multi-Dimensional Competency Assessment Model for Vocational Education

Traditional vocational education competency assessment methods usually focus on a single dimension and lack effective connections between tasks, which makes it impossible to fully and accurately reflect students’ comprehensive abilities. In addition, assessment results often lack personalization and dynamic adjustment, and data utilization efficiency is low. This study constructs a multi-dimensional ability assessment model for vocational education based on multitask learning (MTL), aiming to achieve a more comprehensive, accurate and personalized student ability assessment through information sharing between tasks and dynamic adjustment of task weights. During the research process, this paper designs a shared feature extraction layer to extract common features and constructs a task-specific output layer to ensure independent evaluation of each task. At the same time, this paper introduces a dynamic adjustment mechanism of task weights to balance the importance of tasks. Finally, the model’s generalization ability is improved through multi-task collaborative optimization, thus achieving more accurate and personalized student ability assessment. The model constructed in this paper is compared with other models, and the results show that the accuracy of student ability assessment of the model constructed in this paper is higher than that of other control group models under different scales of data, especially when the data scale is 5,000, the assessment accuracy is as high as 0.95. In addition, the model constructed in this paper is also better than other control models in terms of evaluation efficiency. When the data size is 5,000, 10,000 and 20,000, the average single data processing time of the model is 25 milliseconds, 27 milliseconds and 30 milliseconds, respectively, showing great advantages in processing efficiency. The multi-task learning framework proposed in this study can effectively improve the comprehensiveness, accuracy and personalization of vocational education assessment and has strong practical application potential.