Enhancing social problem solving in children with autism and normal children through computer-assisted instruction
Enhancing social problem solving in children with autism and normal children through computer-assisted instruction
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DOI：
10.1023/a:1010660502130
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发表时间：
2001-08-01
影响因子：
3.9
通讯作者：
Nakhoda-Sapuan, S
中科院分区：
文献类型：
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作者：
Bernard-Opitz, V;Sriram, N;Nakhoda-Sapuan, S
自闭症儿童在解决社会问题和为问题想出多种解决方案方面有困难。然而，他们对图片和动画等视觉线索的反应相对熟练。电脑上显示了八个不同的社会问题，以及可能的解决方案的选择，以及产生替代解决方案的选项。8名学龄前自闭症儿童和8名匹配的正常儿童进行了10次训练，其间穿插6次探查。在所有的课程中，孩子们都被要求为动画问题场景提供解决方案。与探究式课程不同，在培训课程中，问题的解决方案首先由培训师详细解释。随后用解的动态动画说明了这些解释。虽然自闭症儿童产生的替代解决方案明显少于正常的同龄人，但观察到自闭症组在整个探索过程中稳步增加。新想法出现的频率与自闭症的诊断类别直接相关。研究结果表明，在计算机界面的帮助下，自闭症儿童和他们的正常同龄人可以学习解决问题的策略。需要更多的研究来确定这种计算机辅助教学是否能推广到现实生活中未经训练的问题情境。
Children with autism have difficulty in solving social problems and in generating multiple solutions to problems. They are, however, relatively skilled in responding to visual cues such as pictures and animations. Eight distinct social problems were presented on a computer, along with a choice of possible solutions, and an option to produce alternative solutions. Eight preschool children with autism and eight matched normal children went through 10 training sessions interleaved with 6 probe sessions. Children were asked to provide solutions to animated problem scenes in all the sessions. Unlike the probe sessions, in the training sessions problem solutions were first explained thoroughly by the trainer. Subsequently these explanations were illustrated using dynamic animations of the solutions. Although children with autism produced significantly fewer alternative solutions compared to their normal peers, a steady increase across probe sessions was observed for the autistic group. The frequency of new ideas was directly predicted by the diagnostic category of autism. Results suggest young children with autism and their normal peers can be taught problem-solving strategies with the aid of computer interfaces. More research is required to establish whether such computer-assisted instruction will generalize to nontrained problem situations in real-life contexts.