教育中的大数据:学生风险案例研究

IF 1.5 0 ENGINEERING, MULTIDISCIPLINARY Engineering, Technology & Applied Science Research Pub Date : 2023-10-13 DOI:10.48084/etasr.6190
Ahmed B. Altamimi
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引用次数: 0

摘要

本文分析了在特定教育数据集和特定环境中预测学生失败的各种机器学习算法。本文根据学生的成绩、课程难度水平和GPA来预测学生的不及格,这与大多数文献中提供的研究不同,这些研究的重点是周围环境。其主要目的是早期发现有学业表现不佳风险的学生,并实施具体的干预措施,以提高他们的学业成绩。使用11种不同的机器学习(ML)算法来分析数据集。这些数据经过预处理,特征被设计成有效地捕获可能影响学生学习成绩的基本信息。模型选择和评估是一个细致的过程,用于比较算法在准确性、精密度、召回率、f分数、特异性和平衡准确性等指标方面的性能。我们的研究结果表明,不同算法的性能存在显著差异,人工神经网络(ann)和卷积神经网络(cnn)表现出最高的整体性能,紧随其后的是梯度增强分类器(GBC)、神经模糊和随机森林(RF)。其他算法表现出不同的性能水平,其中循环神经网络(RNNs)在召回和f得分方面表现出最弱的结果。教育机构可以利用从这项研究中获得的见解来做出数据驱动的决策,并设计有针对性的干预措施,帮助有风险的学生在学业上取得成功。此外,本文提出的方法可以推广并应用于其他教育数据集,以达到类似的预测目的。
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Big Data in Education: Students at Risk as a Case Study
This paper analyzes various machine learning algorithms to predict student failure in a specific educational dataset and a specific environment. The paper handles the prediction of student failure given the students' grades, course difficulty level, and GPA, differing from most of the provided studies in the literature, where focus is given to the surrounding environment. The main aim is to early detect students at risk of academic underperformance and implement specific interventions to enhance their academic outcomes. A diverse set of eleven Machine Learning (ML) algorithms was used to analyze the dataset. The data went through preprocessing, and features were engineered to effectively capture essential information that may impact students' academic performance. A meticulous process for model selection and evaluation was utilized to compare the algorithms' performance with regard to metrics such as accuracy, precision, recall, F-score, specificity, and balanced accuracy. Our results demonstrate significant variability in the performance of the different algorithms, with Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs) showing the highest overall performance, followed closely by Gradient Boosting Classifier (GBC), Neuro-Fuzzy, and Random Forest (RF). The other algorithms exhibit varying performance levels, with the Recurrent Neural Networks (RNNs) showing the weakest results in recall and F-score. Educational institutions can use the insight gained from this study to make data-driven decisions and design targeted interventions to help students at risk succeed academically. Furthermore, the methodology presented in this paper can be generalized and applied to other educational datasets for similar predictive purposes.
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来源期刊
Engineering, Technology & Applied Science Research
Engineering, Technology & Applied Science Research ENGINEERING, MULTIDISCIPLINARY-
CiteScore
3.00
自引率
46.70%
发文量
222
审稿时长
11 weeks
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