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

Case Studies01:22

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.
Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...

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

Computational Efficient Approximations of the Concordance Probability in a Big Data Setting.

Big data·2023
查看所有相关文章

相关实验视频

Updated: May 13, 2026

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

5.8K

开放数据,私人学习者:用于学习分析的非识别学生活动和绩效数据集.

Elena Tiukhova1, Dimitri Van Landuyt2, Bart Baesens2,3

  • 1Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium. elena.tiukhova@kuleuven.be.

Scientific data
|February 26, 2026
PubMed
概括

学习分析 (LA) 使用数字痕迹来改善教育. 现在可以使用来自KU Leuven的新型非识别点击流数据集,以支持伦理LA的开发和评估.

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

4.6K
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

5.5K

相关实验视频

Last Updated: May 13, 2026

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

5.8K
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

4.6K
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

5.5K

科学领域:

  • 教育技术的教育技术
  • 数据科学数据科学数据科学
  • 学习分析学习分析

背景情况:

  • 数字学习环境产生了大量的学习者数据.
  • 学习分析 (LA) 利用这些数据来优化教育过程.
  • 在洛杉矶,伦理考虑和数据隐私至关重要.

研究的目的:

  • 为LA研究提供详细,非识别的点击流数据集.
  • 促进 LA 解决方案的开放和透明的开发和评估.
  • 解决教育数据收集中的伦理和隐私问题.

主要方法:

  • 收集了来自KU Leuven两门一年级学士课程的点击流数据.
  • 对数据集进行了严格的非识别过程.
  • 执行了数据集的隐私和实用性验证.

主要成果:

  • 涵盖三学年的全面点击流数据集现在公开提供.
  • 提供了对去识别程序的透明文件.
  • 验证证实了数据集对LA研究的有用性,同时尊重隐私.

结论:

  • 发布的数据集促进了伦理和透明的学习分析研究.
  • 公共可用,验证的数据集对于推动LA领域的发展至关重要.
  • 该资源支持协作和制定强大的LA框架.