基于改进BP神经网络的高职学生学习能力评价模型构建

Construction of Higher Vocational Students’ Learning Ability Evaluation Model Based on Improved BP Neural Network

促进施教者精准教学和学习者高效学习是智慧教育需解决的核心问题,而学生学习能力是准确把握学生认知特征、精准制定学习策略的重要依据。特此,探索建立学生学习能力评价BP神经网络模型,以提高评价的客观性和精确性。神经网络结构确定后,针对标准BP神经网络算法存在的不足,引进了L-M算法对神经网络训练算法进行了相应改进。最后,利用MATLAB工具对该模型进行训练和测试,经验证所建立的BP神经网络学习能力评价模型具有极强的鲁棒性和精确性,完全可以满足高职应用技术类专业学生学习能力的评价,可为智慧教育环境下研究解决类似问题提供参考。

Promoting precise teaching by teachers and efficient learning by learners is the core problem to be solved in intelligent education, and students’ learning ability is an important reference for accurately grasping students’ cognitive characteristics and accurately formulating learning strategies. Therefore, we explore the establishment of BP neural network model for students’ learning ability evaluation, so as to improve the objectivity and accuracy of evaluation.After the neural network structure is determined, aiming at the shortcomings of the standard BP neural network algorithm, L-M algorithm is introduced to improve the neural network training algorithm.Finally, the model is trained and tested with MATLAB tools. It is verified that the established BP neural network learning ability evaluation model has strong robustness and accuracy. It can fully meet the evaluation of learning ability of students majoring in Applied Technology in higher vocational colleges, and provide a reference for studying and solving similar problems in the environment of intelligent education.