Study on the Deep Learning Diagnosis and Intervention of Online Courses for Higher Vocational
College Enrollment Students under LearningAnalysis Technology

Study on the Deep Learning Diagnosis and Intervention of Online Courses for Higher Vocational College Enrollment Students under LearningAnalysis Technology

Bozhou Vocational and Technical College

Download:

PDF(0KB)
Export: BibTeX | EndNote (RIS)

Abstract Learning analysis technology is a new technology widely used to promote learning. It can perform quantitative structural analysis on learning data, combine artificial intelligence, expert system and teacher system structure, build a bridge between the data structure and meaning understanding, enrich the application of data information, and effectively carry out analysis and understand- ing. This article firstly describes the connotations, connections, and characteristics of learning analysis technology and deep learn- ing; then puts forward the exploration and research on deep learning strategies for higher vocational expansion students under the background of learning analysis technology and combining deep learning related theories.

Key words： earning analysis technology higher vocational education deep learning diagnosis

Published: 13 August 2019

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	YAN Lei

Cite this article:

YAN Lei. Study on the Deep Learning Diagnosis and Intervention of Online Courses for Higher Vocational College Enrollment Students under LearningAnalysis Technology. Computer & Telecommunication, 2020, 1(6): 30-33.

URL:

https://www.computertelecom.com.cn/EN/ OR https://www.computertelecom.com.cn/EN/Y2020/V1/I6/30

[1]	LI Chun-hui WANG Xiao-ying ZHANG Qing-jie LIU Han-zhuo LIANG Jia-ye GAO Ning-kang. DDoS Attack Detection Method Based on Multi-scale Convolutional Neural Network [J]. 电脑与电信, 2024, 1(6): 35-.
[2]	WANG Jin WANG Rui. Facial Expression Recognition Algorithm Based on Multi-scale Feature Deep Learning[J]. 电脑与电信, 2024, 1(5): 75-.
[3]	NIE Cheng WANG Jie. A Review of Research and Development in Data Analysis Methods [J]. 电脑与电信, 2024, 1(4): 200-25.
[4]	XIN Bo-fu. Aerial Vehicle Detection in Low Light Environment Based on Yolov5[J]. 电脑与电信, 2024, 1(1): 78-83.
[5]	ZHU Dong-ling ZHOU Xiu-ying. The Application of Achievement Degree Evaluation in the Mobile Application Development Course [J]. 电脑与电信, 2023, 1(1-2): 0-4.
[6]	SU Cui-wen CHAI Guo-qiang. Real-time Facial Expression Recognition Based on Facial Feature Detection [J]. 电脑与电信, 2023, 1(1-2): 17-21.
[7]	HE Zong-xi JIANG Ming-zhong XIE Ming-xia PANG Jia-bao CHEN Qiu-yan HU Yi-bo. Design of Driver Fatigue Recognition Algorithm Based on YOLOv8 and Face Key Points Detection[J]. 电脑与电信, 2023, 1(11): 1-6.
[8]	ZHANG Miao-miao CHAI Guo-qiang YU Hai-le XU Hao-xuan. Facial Feature Test and Fatigue Driving Warning Based on Deep Learning [J]. 电脑与电信, 2022, 1(12): 1-.
[9]	ZHU Feng. Million Enrollment Expansion in Higher Vocational Education——Research on Talent Training Strategy for Big Data Technology and Application Specialty[J]. 电脑与电信, 2021, 1(9): 85-89.
[10]	XU Ming-yuan YANG Yan-hong. Design of Baggage Detection System Based on Deep Learning[J]. 电脑与电信, 2021, 1(4): 36-40.
[11]	TANG Di HUANG Si-han. Application of Micro Course in Ecological Teaching Mode of Computer Majorin Higher Vocational Colleges[J]. 电脑与电信, 2021, 1(3): 39-41.
[12]	LUAN Gui-fen. Practice and Exploration of Integrated Teaching Mode of Learning, Trainingand Competition for Core Courses of Computer Network Specialty[J]. 电脑与电信, 2021, 1(3): 72-74.
[13]	LIN Long. Model-based Robust RecognitionAlgorithm for Deep Learning Communication Signals[J]. 电脑与电信, 2021, 1(1): 20-22.
[14]	ZHU Dong-ling. Design and Implementation of“8”-shape Spiral for Software Curriculum Diagnosis and Improvement — —Illustrated by the Case of Java Programming[J]. 电脑与电信, 2020, 1(9): 75-77.
[15]	. Time Window Setting on PredictionAccuracy of Rock Pressure in Fully Mechanized Working Face[J]. 电脑与电信, 2020, 1(4): 14-18.

Viewed

Full text

Abstract

Cited

Shared

Discussed