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arXiv 2402.01655cs.CYcs.AIcs.LG
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在线课程中学生成绩预测的深度学习方法：基于全球视角的挑战

A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

Arizona State University（亚利桑那州立大学）；Lebanese American University（黎巴嫩美国大学）；Zarqa University（扎尔卡大学）；Western University（西安大略大学）；Jadara University（贾达拉大学）

Abdallah Moubayed, MohammadNoor Injadat, Nouh Alhindawi, Ghassan Samara, Sara Abuasal, Raed Alazaidah

备注：
Accepted and presented in 24th International Arab Conference on Information Technology (ACIT'2023)
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AI总结：

本研究提出使用CNN和RNN-LSTM深度学习模型，基于全球三个地区的数据集预测在线课程中期学生成绩，结果显示其在多数数据集上优于传统机器学习模型。

摘要：

使用传统分析方法来分析和评估学生在任何学习环境中的进步既费时又费力。由于教育领域日益重视互联网技术的整合，以及学术机构转向电子学习、混合式或在线学习模式的趋势，学生数量不断增加，这一问题变得更加严峻。因此，学生成绩预测已成为近年来一个活跃的研究领域。为解决这一问题，机器学习和数据挖掘技术应运而生，成为一种可行的解决方案。为此，本研究提出使用深度学习技术（CNN和RNN-LSTM）来预测在线课程交付中期阶段的学生成绩，并使用了从全球三个不同地区收集的三个不同数据集。实验结果表明，深度学习模型表现出良好的性能，在三个数据集中的两个上优于其他经过优化的传统机器学习模型，同时在第三个数据集上也具有可比的性能。

英文摘要：

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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