Related Experiment Video
Updated: Oct 8, 2025
13:44
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
Published on: December 9, 2022
3.8K
Impact Assessment of Big Data on Higher Education Management Based on Time-Varying Clustering Sampling Algorithm
1School of Preschool Education, Zibo Normal College, Zibo 255130, China.
Computational Intelligence and Neuroscience
|December 24, 2021
Summary
Big data significantly impacts higher education management. A time-varying lens algorithm (TLA) effectively analyzes educational needs, enhancing management capabilities.
Area of Science:
- Educational Technology
- Data Science in Education
- Higher Education Management
Background:
- The increasing influence of the data era on higher education.
- The need to evaluate big data's impact on educational management.
- The intersection of social economy development and educational attention.
Purpose of the Study:
- To evaluate the impact of big data on higher education management.
- To analyze business needs within education management, thinking, and practice.
- To improve educational abilities using big data insights.
Main Methods:
- Introduction of a time-varying lens algorithm (TLA).
- Analysis of business processes in education management, thinking, and practice.
- Learning and mining relevant technologies for educational improvement.
Main Results:
- The time-varying lens algorithm (TLA) proved effective in analysis.
- Demonstrated the utility of big data in assisting higher education management.
- Simulation results validated the effectiveness of the time-varying clustering sampling algorithm.
Conclusions:
- Big data, analyzed via TLA, can effectively assist higher education management.
- The proposed algorithm enhances the understanding of educational business needs.
- Continuous development in data science offers new avenues for educational improvement.
Related Concept Videos
01:20Cluster Sampling Method
13.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.1K
01:09Random Sampling Method
12.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
12.8K
01:23Sampling Plans
318
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
318
01:16Stratified Sampling Method
13.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
To choose a stratified sample, divide the population into groups called strata and then take a...
13.2K
00:55Convenience Sampling Method
9.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
9.9K
01:25Choosing Between z and t Distribution
3.2K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.2K