Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Reliability and Validity01:29

Reliability and Validity

12.8K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Agricultural sustainability monitoring in arid regions using hybrid deep learning and Landsat 8 imagery in Najran City, Saudi Arabia.

Scientific reports·2026
Same author

A hybrid improved binary GWO-PSO with random forest (IBGWO-PSO-RF) based intrusion detection model for large-scale attacks in IoT environment.

Scientific reports·2026
Same author

Hybrid deep learning and optimization-based land use and land cover classification for advancing sustainable agriculture in Najran city, Saudi Arabia.

Scientific reports·2025
Same author

An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning.

Scientific reports·2025
Same author

Analyzing histopathological images using fused CNN features based on the geometric active contour method for early diagnosis of lung and colon cancer.

Discover oncology·2025
Same author

GAN-AVI: facial expression translator in Twitter avatar analogous to tweet sentiments.

Scientific reports·2025

相关实验视频

Updated: Jul 26, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K

早期检测学生的学位水平的学术表现,使用教育数据挖掘.

Areej Fatemah Meghji1, Naeem Ahmed Mahoto1, Yousef Asiri2

  • 1Department of Software Engineering, Mehran University of Engineering and Technology Jamshoro, Hyderabad, Jamshoro, Pakistan.

PeerJ. Computer science
|June 22, 2023
PubMed
概括

教育数据挖掘使用分类来预测学生的表现. 这项研究提出了一个根据学术水平对学生进行细分的框架,有助于制定教育政策,以改善教育成果.

关键词:
分类 分类 分类 分类.数据挖掘是一种数据挖掘.决策树 决策树是一个决策树.教育数据挖掘教育数据挖掘教学政策的教育政策.学生表现预测 学生表现预测

更多相关视频

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.6K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.4K

相关实验视频

Last Updated: Jul 26, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

3.9K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.6K
Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.4K

科学领域:

  • 教育数据挖掘教育数据挖掘
  • 高等教育的分析.

背景情况:

  • 大学产生大量的学生数据,对于理解学习行为至关重要.
  • 教育数据挖掘 (EDM) 提供了从这些数据中提取有价值的见解的方法.

研究的目的:

  • 用EDM分类来分析学生数据,以预测学业成绩.
  • 提出一个学生细分框架来确定绩效水平.
  • 支持教育政策的制定,以提高教育质量.

主要方法:

  • 从教育数据挖掘中应用的分类技术.
  • 分析了291名大学生的数据.
  • 开发了学生学业绩水平的细分框架.

主要成果:

  • 该分类模型有效地预测了学生的表现.
  • 细分框架成功地识别了不同学术水平的学生.
  • 最初两年的早期课程数据证明足以进行分类.

结论:

  • 拟议的EDM框架是有效的预测和细分学生的表现.
  • 这种方法可以为教学策略提供信息,以减少学术失败并促进学生的成功.
  • 早期的学术指标可以用于及时干预.