Development of training programs and evaluation methods for question intelligence

Fumihito Ikeda
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Abstract

Asking questions is key to advancing knowledge and asking the creative questions is especially important. Being able to properly frame and focus on the creative questions is an important skill but this isn’t something that is taught or evaluated within educational systems. Professor Fumihito Ikeda, Brain Science Research and Education Center, Institution for the Advancement of Higher Education, Hokkaido University, Japan, believes that nurturing children’s development of creative questioning skills at school would be beneficial for the leaders of tomorrow. He and his team are developing test questions and training programmes for schools that generate questions focused on three types of logical reasoning: inductive, deductive and abductive. Ikeda’s goal is to develop and train mainly primary and high school students in question-intelligence. A test question using the three types of reasoning was created and tried on around 200 high school students. The researchers utilised, a deep neural network (DNN), a form of machine learning, to assist in the automatic classification and evaluation of questions.Ikeda plans to create an inquiry learning programme that will improve the quality of scientific questions while asking different types of logic in order to collect more appropriate questions for DNN to learn. He will use his partnerships with the Japan Society for Science Education and the Japan Creativity Society to roll these programmes out to students at educational institutions.
制定问题智力的培训计划和评估方法
提出问题是提升知识的关键,提出创造性的问题尤其重要。能够正确地构建并专注于创造性问题是一项重要的技能,但这不是教育系统中教授或评估的东西。日本北海道大学高等教育发展研究所脑科学研究与教育中心的池田文仁教授认为,在学校培养儿童创造性提问技能的发展将有利于未来的领导者。他和他的团队正在为学校开发测试问题和培训项目,这些问题集中于三种类型的逻辑推理:归纳、演绎和溯因。池田的目标主要是开发和训练中小学生的问题智能。研究人员设计了一个使用这三种推理方式的测试问题,并在大约200名高中生中进行了测试。研究人员利用深度神经网络(DNN),一种机器学习的形式,来协助问题的自动分类和评估。池田计划创建一个探究性学习程序,在提出不同类型逻辑的同时提高科学问题的质量,以便收集更合适的问题供DNN学习。他将利用与日本科学教育协会和日本创意协会的合作,向教育机构的学生推广这些项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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