Video Modelling to Improve Hand Dexterity in Students with Learning Disabilities: Special Education Teachers' Perspectives

Nurbieta Abd Aziz, Masne Kadar, D. Harun, Hanif Farhan Mohd Rasdi
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Abstract

Proficient hand dexterity (HD) may help students with learning disabilities (LDs) perform various hand tasks efficiently. Video modelling (VM) is a strategy that uses a model to improve desired skills. However, the limited study focuses on VM to improve HD among students with LDs. Therefore, this study explores VM implementation in the learning and teaching process to improve the HD of students with LDs. Five Special Education (SE) teachers who taught the Hand Manipulative Component lessons in two different states were purposefully selected and invited to participate in this study. A one-toone interview was conducted with individual SE teachers. The interview sessions were audio-recorded and transcribed verbatim, and the data was analysed through thematic analysis. Rigorous approaches were performed to ensure the findings' trustworthiness, such as triangulation, member checking, and an audit trail. Four key themes emerged: conventional learning and teaching methods, challenges faced by students with LDs in improving HD, and the advantages of implementing VM in learning and teaching to improve HD and develop a video module. This study suggests SE teachers can implement VM in their learning and teaching processes to improve the HD performance of students with LDs. This current study concludes that VM can be a supportive strategy in the teaching process to improve HD performance among students with LDs.
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视频建模提高学习障碍学生的动手能力:特殊教育教师的视角
熟练的手巧(HD)可以帮助有学习障碍(LD)的学生有效地完成各种手部任务。视频建模(VM)是一种使用模型来提高所需技能的策略。然而,有限的研究集中在VM上,以改善LD学生的HD。因此,本研究探讨虚拟机在学习和教学过程中的实施,以提高LD学生的HD。有目的地选择并邀请了五名在两个不同州教授手操纵组件课程的特殊教育(SE)教师参与本研究。对个别SE教师进行了一次单独的访谈。访谈环节进行了录音和逐字转录,并通过专题分析对数据进行了分析。采取了严格的方法来确保调查结果的可信度,如三角测量、成员检查和审计跟踪。出现了四个关键主题:传统的学习和教学方法,LD学生在提高HD方面面临的挑战,以及在学习和教学中实施VM以提高HD和开发视频模块的优势。本研究表明,SE教师可以在学习和教学过程中实施VM,以提高LD学生的HD表现。本研究得出的结论是,虚拟机可以作为教学过程中的一种支持策略,以提高LD学生的HD表现。
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发文量
30
审稿时长
12 weeks
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