基于深度学习的计算机专业个性化教学资源推荐方法
Recommended method of personalized teaching resources for computer majors based on deep learning
谢泽长 1刘宗远2
作者信息
- 1. 河南机电职业学院 电气工程学院,河南 郑州 450000
- 2. 河南机电职业学院 信息中心,河南 郑州 450000
- 折叠
摘要
常规推荐方法推荐形式较为单一,导致其推荐效果不佳.为解决这一问题,文章基于深度学习理论,设计计算机专业个性化教学资源推荐方法.该方法首先深入了解高校学生学习习惯、兴趣偏好、学习目标与教学进度,完成推荐需求分析与教学资源推荐标签的生成;然后,以此为基础,基于深度学习理论,构建教学资源推荐模型,基于梯度下降算法完成模型训练,实现教学资源的精准推荐;最后,应用对比实验验证所提方法的先进性.测试结果表明:该设计方法最终得出的推荐频次均值较高,高于20 次,优于对比方法,推荐效果更能满足实际需求.
Abstract
The conventional recommendation method is relatively simple,which leads to its poor recommendation effect.In order to solve this problem,based on the deep learning theory,the recommendation method of personalized teaching resources for computer major.This method first understands the learning habits,interest preferences,learning objectives and teaching progress,completes the recommendation requirement analysis and the generation of teaching resource recommendation labels.Then,based on deep learning theory,it constructs the teaching resource recommendation model,and completes the model training based on gradient descent algorithm to realize the accurate recommendation of teaching resource.Finally,it applies comparative experiment to verify the advanced nature of the proposed method.The test results show that the average recommendation frequency of the design method is higher,higher than 20 times,which is better than the comparison method,and the recommendation effect can better meet the actual needs.
关键词
深度学习/计算机专业/教学资源/推荐方法Key words
deep learning/computer major/teaching resources/recommendation methods引用本文复制引用
出版年
2024
NETL
NSTL
万方数据