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一种用于数据驱动的远程学习推荐的多标准决策方法。

A multi-criteria decision-making (MCDM) approach for data-driven distance learning recommendations.

作者信息

Alshamsi Aysha Meshaal, El-Kassabi Hadeel, Serhani Mohamed Adel, Bouhaddioui Chafik

机构信息

Department of Information Systems and Security, College of Information Technology, UAEU, Al-Ain, UAE.

Department of Computer Science and Software Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC Canada.

出版信息

Educ Inf Technol (Dordr). 2023 Jan 26:1-38. doi: 10.1007/s10639-023-11589-9.

DOI:10.1007/s10639-023-11589-9
PMID:36718426
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9878493/
Abstract

Distance learning has been adopted as an alternative learning strategy to the face-to-face teaching methodology. It has been largely implemented by many governments worldwide due to the spread of the COVID-19 pandemic and the implication in enforcing lockdown and social distancing. In emergency situations distance learning is referred to as Emergency Remote Teaching (ERT). Due to this dynamic, sudden shift, and scaling demand in distance learning, many challenges have been accentuated. These include technological adoption, student commitments, parent involvement, and teacher extra burden management, changes in the organization methodology, in addition to government development of new guidelines and regulations to assess, manage, and control the outcomes of distance learning. The objective of this paper is to analyze the alternatives of distance learning and discuss how these alternatives reflect on student academic performance and retention in distance learning education. We first, examine how different stakeholders make use of distance learning to achieve the learning objectives. Then, we evaluate various alternatives and criteria that influence distance learning, we study the correlation between them and extract the best alternatives. The model we propose is a multi-criteria decision-making model that assigns various scores of weights to alternatives, then the best-scored alternative is passed through a recommendation model. Finally, our system proposes customized recommendations to students, and teachers which will lead to enhancing student academic performance. We believe that this study will serve the education system and provides valuable insights and understanding of the use of distance learning and its effectiveness.

摘要

远程学习已被用作面对面教学方法的替代学习策略。由于新冠疫情的蔓延以及实施封锁和社交距离措施的影响,全球许多政府都在大力推行远程学习。在紧急情况下,远程学习被称为应急远程教学(ERT)。由于这种动态、突然的转变以及远程学习需求的扩大,许多挑战被进一步凸显出来。这些挑战包括技术应用、学生的学习投入、家长的参与以及教师额外负担的管理、组织方法的变化,此外还有政府制定新的指导方针和法规来评估、管理和控制远程学习的效果。本文的目的是分析远程学习的替代方案,并讨论这些替代方案如何影响学生在远程学习教育中的学业成绩和留存率。我们首先研究不同利益相关者如何利用远程学习来实现学习目标。然后,我们评估影响远程学习的各种替代方案和标准,研究它们之间的相关性并找出最佳替代方案。我们提出的模型是一个多标准决策模型,为替代方案分配各种权重分数,然后将得分最高的替代方案通过推荐模型。最后,我们的系统向学生和教师提出定制化建议,这将有助于提高学生的学业成绩。我们相信这项研究将为教育系统提供帮助,并为远程学习的使用及其有效性提供有价值的见解和理解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/6f90d7c0ae98/10639_2023_11589_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/2e6624d160a6/10639_2023_11589_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/c3d618065c6e/10639_2023_11589_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/3cc7b3ad6a66/10639_2023_11589_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/6cab8b422d61/10639_2023_11589_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/57ed32eb694b/10639_2023_11589_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/6f90d7c0ae98/10639_2023_11589_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/2e6624d160a6/10639_2023_11589_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/c3d618065c6e/10639_2023_11589_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/3cc7b3ad6a66/10639_2023_11589_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/6cab8b422d61/10639_2023_11589_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/57ed32eb694b/10639_2023_11589_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c250/9878493/6f90d7c0ae98/10639_2023_11589_Fig6_HTML.jpg

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