在线学习条件下教育动机的Agent建模

O. Pursky, A. Selivanova, I. Buchatska, T. Dubovyk, Tatiana Tomashevska, Petro Palchuk
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摘要

本文主要研究在线学习条件下学生学习动机特征的智能体建模。在与冠状病毒疫情相关的远程学习中,保持动力发挥着特殊作用。为了分析不同学习条件下的动机特征,本文采用了学生动机的agent模型。在离线和在线学习中测试了该模型的有效性。建模结果显示,在小学和高等教育阶段,离线学习条件下主要动机成分的影响是不同的。最年轻的学生在学习新东西的内心渴望不断得到外部刺激的情况下最有动力学习。在小学阶段,教师和家长的激励作用逐渐减弱,而重要环境(朋友、参考成年人)的消极和积极影响都有所增加。青少年有明确的学习目标。网络学习对这类学生的影响颇有争议。这项研究发现了复杂的趋势——学习动机既有增加,也有急剧下降。第一类人正悄然转向在线学习,广泛利用互联网。第二种要么完全失去学习的动力,要么表现出不稳定的动力,要么急剧下降,然后同样急剧上升。Agent建模技术允许跟踪在何种条件下刺激会改变学生的学习动机。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agent Modeling of Educational Motivation in Online Learning Conditions
This article is devoted to agent modeling the features of student learning motivation in online learning conditions. Maintaining motivation plays a special role during the introduction of distance learning in connection with the coronavirus epidemic. To analyze the features of motivation in different learning conditions agent model of the student motivation has been used. The effectiveness of the model was tested in both offline and online learning. The results of modeling showed that the influence of the main motivational components in the conditions of offline learning varies from primary school to higher education. The youngest students are best motivated to learn in a situation where the inner desire to learn something new is constantly supported by external stimulation. In primary school, the motivating influence of teachers and parents gradually decreases, but both the negative and positive influence of an important environment (friends, reference adults) increases. Adolescents have clearly defined learning goals. The impact of online learning on this category of students is quite controversial. The study has found mixed trends - both an increase in motivation for learning and a sharp decrease. The first group is quietly moving to online learning, making extensive use of the Internet. The second either completely lose motivation to learn or shows unstable motivation, which either sharply decreases, then just as sharply increases. Agent modeling technologies allows tracking the conditions under which stimulation will be change student learning motivation.
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