The Impact of Artificial Intelligence-Based Virtual Laboratories on Developing Students’ Motivation Towards Learning Mathematics

Haitham Qawaqneh, F. Ahmad, Ali Ratib Alawamreh
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Abstract

This research investigates the impact of virtual laboratories (VLabs) based on artificial intelligence (AI) on developing students’ motivation toward learning mathematics. A semiexperimental approach is used to achieve the research objectives. The research sample, consisting of 80students from the seventh grade, is selected by the purposeful sampling approach. The research sample is randomly distributed into three groups: two experimental groups and one control group. The first group of 26 students is taught using the AI-based VLabs, while the second group of 27 students is taught using a VLab based on 3D visual imaging, and the third group, a control group consisting of 27 students, is taught using the traditional approach. The research instrument, a questionnaire for learning motivation, was designed after ensuring its validity and reliability. The findings of the motivation questionnaire indicate that students in the first experimental group have more motivation to learn mathematics than students in the second experimental group and the control group. The results also show that the students in the second experimental group have more motivation to learn mathematics than the students in the control group. Given the said findings, the research recommends using virtual laboratories based on artificial intelligence and all its applications in the learning process due to their impact on students’ mathematics learning.
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基于人工智能的虚拟实验室对培养学生数学学习动机的影响
本研究调查了基于人工智能的虚拟实验室(VLabs)对培养学生学习数学动机的影响。采用半实验方法来实现研究目标。本研究以七年级80名学生为研究对象,采用有目的的抽样方法。研究样本随机分为三组:两个实验组和一个对照组。第一组26名学生使用基于人工智能的VLab进行教学,而第二组27名学生使用了基于3D视觉成像的VLab,第三组是由27名学生组成的对照组,使用传统方法进行教学。在确保其有效性和可靠性后,设计了一份学习动机问卷。动机问卷调查结果表明,第一实验组的学生比第二实验组和对照组的学生更有动机学习数学。结果还表明,第二实验组的学生比对照组的学生更有动力学习数学。鉴于上述发现,该研究建议使用基于人工智能的虚拟实验室及其在学习过程中的所有应用,因为它们会影响学生的数学学习。
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来源期刊
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0.00%
发文量
352
审稿时长
12 weeks
期刊介绍: This interdisciplinary journal focuses on the exchange of relevant trends and research results and presents practical experiences gained while developing and testing elements of technology enhanced learning. It bridges the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Fields of interest include, but are not limited to: -Software / Distributed Systems -Knowledge Management -Semantic Web -MashUp Technologies -Platforms and Content Authoring -New Learning Models and Applications -Pedagogical and Psychological Issues -Trust / Security -Internet Applications -Networked Tools -Mobile / wireless -Electronics -Visualisation -Bio- / Neuroinformatics -Language /Speech -Collaboration Tools / Collaborative Networks
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