Towards an Adaptive Education through a Machine Learning Recommendation System

Ossama H. Embarak
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引用次数: 5

Abstract

Educational institutions have a tremendous burden of handling students with low academic performance (At-risk students). Many approaches support this group of pupils, such as psychological therapy, a proper timetable for vulnerable pupils, recall, personal training, mock-tests, direct private education, or success centres. However, these methods are not enough to solve the issue since other factors influence the learner’s success, which could be their family difficulties, cognitive style, prior performance, and college foundation level. This paper explores machine learning models for predicting at-risk students and then build a recommendation platform to provide direct system-based coaching to such students to remediate the fragmentation in their knowledge and skills. We use a dataset of 554 students from a computer program; the study aims to break down the curricula into a set of crumbs of knowledge and skills used to measure learners’ progress during their study. We used various machine learning algorithms, a decision tree with an accuracy of 82.68% (positive class: Good Standing), and high coverage of 87.69% (positive class: Good Standing). We completed the model optimization and used the ROC comparison to compare the classifier models. A remediation algorithm is used to support at-risk cases, leading to a sharp decline in the at-risk rate. The study finds that the current applied approaches to handle at-risk students exaggerate the problem, and students should not be treated in bulk.
通过机器学习推荐系统实现适应性教育
教育机构在处理学习成绩差的学生(高危学生)方面负担很大。许多方法支持这一群体的学生,如心理治疗、为弱势学生制定适当的时间表、回忆、个人培训、模拟测试、直接私人教育或成功中心。然而,这些方法并不足以解决问题,因为影响学习者成功的还有其他因素,可能是他们的家庭困难、认知风格、以前的表现和大学基础水平。本文探索了机器学习模型,用于预测有风险的学生,然后建立一个推荐平台,为这些学生提供直接的基于系统的指导,以弥补他们在知识和技能上的碎片化。我们使用来自计算机程序的554名学生的数据集;这项研究旨在将课程分解为一系列知识和技能的碎片,用于衡量学习者在学习过程中的进步。我们使用了各种机器学习算法,决策树的准确率为82.68%(积极类:良好信誉),高覆盖率为87.69%(积极类:良好信誉)。我们完成了模型优化,并使用ROC比较来比较分类器模型。一种补救算法用于支持风险案例,导致风险率急剧下降。研究发现,目前用于处理有风险学生的方法夸大了问题,学生不应该被大量治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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