学生学风险的预测建模:来自韩国远程大学的实用见解
Eui-Yeong Seo1, Jaemo Yang2, Ji-Eun Lee3
1Education Innovation Center, Hanyang Cyber University, 220 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea.
Heliyon
|June 4, 2024
概括
这项研究开发了一个实用的学生学预测模型,用于使用机器学习进行远程教育. 最近的数据和特定的学生指标有效地识别了有风险的学习者,从而实现了有针对性的支持干预.
科学领域:
- 教育技术的教育技术
- 教育中的数据科学教育中的数据科学
- 高等教育研究 高等教育研究
背景情况:
- 远程教育有助于终身学习,但面临着很高的学生流失.
- 识别和减轻导致学生学的因素对于在线大学至关重要.
- 现有的预测模型在现实教育环境中往往缺乏实际应用.
研究的目的:
- 开发一个实用和准确的预测模型,用于远程教育中的学生学.
- 分析大量现实世界的数据,以确定学生退学的主要指标.
- 为了比较不同数据子集和机器学习算法的预测性能.
主要方法:
- 在2018-2022年间,韩国一所在线大学对144,540个实例进行了分析.
- 整合人口,学术和在线学习管理系统 (LMS) 的日志数据.
- 逐步向后消除的应用和光梯度增强机 (LGBM) 和后勤回归模型的比较.
主要成果:
- 关键的退学指标包括年龄,居住区,职业,GPA和LMS参与度指标.
- 最近的数据 (过去四个学期) 证明了稳定预测模型培训的有效性.
- 光梯度增强机 (LGBM) 算法显示出更高的预测准确性 (ROC-AUC),而后勤回归则提供了更好的解释性.
- 性别特定的分析显示,男性和女性学生的学影响因素不同.
结论:
- 结合先进算法 (LGBM) 和可解释模型 (逻辑回归) 的混合方法对韩国高等教育中学预测有效.
- 准确的预测模型可以开发有针对性的学生支持策略,以减少退学.
- 利用纵向和最近的数据,以及人口和行为指标,提高了退学风险评估的准确性.
相关概念视频
01:08Outliers and Influential Points
4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
02:20Correlations
32.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.8K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:25Multiple Regression
3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
01:11Residuals and Least-Squares Property
7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
01:29Reliability and Validity
12.7K
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.
12.7K