理解基于不同年龄类别的在线学习

Danielle Morin, Hamed Safaee Fard, R. Saadé
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引用次数: 7

摘要

[这篇论文被修订并发表在2019年出版的《信息技术教育杂志:研究》第18卷]目的/目的:了解不同年龄组学生在在线环境中学习的准备情况。背景:由于越来越多的成人/成熟学生在工作的同时继续接受高等教育,今天的在线学习者在年龄上是多样化的。对学习者年龄对其在线学习体验影响的了解是有限的。研究方法:采用调查方法。该研究使用了1900个调查样本。进行相关分析。贡献:该研究的贡献在于增加了该领域有限的知识体系,并为在线学习准备调查的维度增加了额外的维度,如有用性、倾向、焦虑和态度。研究发现:年龄较大的学生在计算机熟练程度和学习技能方面比年龄较小的学生更有信心。他们更有动力,表现出更好的态度,也不那么焦虑。对从业者的建议:从业者应该考虑允许学生根据他们的年龄配置学习方法的偏好。这些偏好应该与在线学习准备调查(OLRS)的维度联系起来。对研究人员的建议:需要在在线学习环境中使用OLRS进行更多的实证研究。OLRS因素是强的,可以预测学生的准备和表现。这些都是人工智能在技术中介学习工具支持下的机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding Online Learning Based on Different Age Categories
[This Proceedings paper was revised and published in the 2019 issue of Journal of Information Technology Education: Research, Volume 18] Aim/Purpose: To understand readiness of students for learning in online environments across different age groups. Background: Online learners today are diverse in age due to increasing adult/mature students who continue their higher education while they are working. Understanding the influence of the learners’ age on their online learning experience is limited. Methodology: A survey methodology approach was followed. A sample of one thousand nine hundred and twenty surveys were used. Correlation analysis was performed. Contribution: The study contributes by adding to the limited body of knowledge in this area and adds to the dimensions of the Online Learning Readiness Survey additional dimensions such as usefulness, tendency, anxiety, and attitudes. Findings: Older students have more confidence than younger ones in computer proficiency and learning skills. They are more motivated, show better attitudes and are less anxious. Recommendations for Practitioners: Practitioners should consider preferences that allow students to configure the learning approach to their age. These preferences should be tied to the dimensions of the online learning readiness survey (OLRS). Recommendations for Researchers: More empirical research is required using OLRS for online learning environments. OLRS factors are strong and can predict student readiness and performance. These are opportunities for artificial intelligence in the support of technology-mediated tools for learning.
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