利用有形材料实现教育编程环境

Toshiyasu Kato, Y. Kambayashi, Koji Oda
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引用次数: 0

摘要

最近,人们开始关注有形教材的教育。编程教育领域也不例外。教育研究者发现,有形的教材可以提高学生的逻辑思维能力。因为有形的教材让学生用手触摸,用眼睛看到,加深了他们的理解。换句话说,他们吸引学生的所有五种感官。编程语言独立学习材料在世界各地的一些教育机构中使用。使用某些类型图形的编程环境是常用的,但是由于所安装的计算机的能力和它们的小屏幕,它们限制了用户的操作。为了克服这个问题,我们开发了一套切实可行的编程教育教材。该系统使用户可以使用其有形的材料来学习编程,既可以进行群体教育,也可以进行个人学习。本研究的目的是解决编程学习者普遍存在的一个常见问题。那就是让教师知道学习者在课堂上的编程情况。教师很难收集数据,了解每个学生取得了什么进步,是什么障碍导致了他们的延迟。我们的系统利用有形教材的优势,为教师提供这些数据。在本文中,我们报告了我们在开发编程教育有形材料方面的经验。由于我们的目标是支持高等教育机构的编程课程,我们的目标是如何开发和改进个人编程方法。这项研究是在有限的时间内支持编程课程的新尝试。使用这种有形教材的学生应该安排一套程序卡。每张卡片上都有一个与操作流程相匹配的二维码。在完成二维码的排列后,学生使用平板电脑的相机拍摄这些卡片的照片。这一系列QR码构建了一个Python程序,并在设备上执行。教师可以远程监控每个学生的进度,因为每个QR码都有注释,可以告知教师正在执行的程序元素,以便教师可以收集有关单个学生构建的程序的详细信息。教师可以根据收集到的信息将学生分为几类,然后为学生设置相应的学习点。收集到的信息包括学生在课程中失败的地方,以及他们如何试图纠正错误。有些修正是正确的,有些则不是。然后,教师将学生进一步分类,使教师能够认识到每个学生的弱点,并给予他或她仔细的指导。为了证明我们的有形材料对编程教育的有效性,我们对大学生进行了数值实验。我们观察到他们中80%的人提高了他们的逻辑思维能力。在未来的工作中,我们将对教师进行更强的支持功能,使教师能够分析学生的常见错误并正确引导学生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Implementation of Educational Programming Environment Using Tangible Materials
Recently, people started to pay attention on the education using tangible teaching materials. The field of programming education is not an exception. Researchers in education have found that tangible teaching materials improve students' logical thinking ability. Because the tangible teaching materials make students deepen their understanding by touching them with their hands and seeing them with their eyes. In other words, they appeal to students' all five senses. Programming language independent learning materials are used in several educational institutions around the world. Programming environments using some kinds of graphics are commonly used, but they restrict their users' operations due to the abilities of installed computers and their small screens. To overcome this problem, we have developed a tangible teaching material for programming education. This system makes it possible for the users to learn programming by using its tangible materials for either group education or individual learning. The purpose of this research is to solve one of the common problems widely seen among programming learners. That is letting the instructors know the learners' programming situations in classes. It is difficult for instructors to collect data about what progress each student makes and what obstacle makes them be delay. Our system takes advantages of tangible teaching materials and provides such data for instructors. In this paper, we report our experiences about the development of a tangible material for programming education. Since our goal is to support programming classes in higher educational institutes, our target is how to develop and improve individual programming methods. This research is a new attempt to support programming classes with limited time.The student using this tangible teaching materials is supposed to arrange a set of programming cards. Each card has a QR code that matches an operation procedure. Upon completion of the arrangement of QR codes, the student takes a photo of these cards using the camera of a tablet. The series of QR codes construct a Python program, and it is executed on the device. The instructor can monitor each student's progress remotely, since each QR code has annotation that informs the instructor what program element it is executing so that the instructor can collect detailed information about the program individual student constructing. The instructor can classify students into several categories based on the collected information, then set study points for the students corresponding to the categories. The collected information contains where the students failed in their programs and how they tried to correct the errors. Some corrections are right, and some are not. Then the instructor further classifies students in the same categories so that the instructor can recognize each student's weak points and give him or her careful guidance. To demonstrate the effectiveness of our tangible materials for programming education, we have conduct numerical experiments over college students. We have observed 80% of them improve their logical thinking ability. As a future work, we will implement stronger support features for instructors so that the instructor can analyze students' common errors and rightly guide them.
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