为非技术学生简化编程:一种解释学方法。

IF 2.8 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kunstliche Intelligenz Pub Date : 2022-01-01 Epub Date: 2022-01-17 DOI:10.1007/s13218-021-00748-0
Andrea Valente, Emanuela Marchetti
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引用次数: 0

摘要

本文研究了面向非技术大学生的编程简化问题。本文概述了典型的简化策略,根据我们的发现,针对非技术学生的CT课程通常针对来自不同院系的学习者,提供通用和基础知识,而不是与他们的专业专门相关。在这项研究中,我们提出了一种解释学方法来简化编程,我们的目标是澄清编程的问题解决方面,解决特定于他们学习的计算问题,并利用学习者对他们作为用户所经历的数字媒体的预理解。我们的理论方法的实际对应是一个极简的Python多媒体库,称为Medialib,我们设计它使不懂技术的大学生能够用简短易读的代码创建视觉媒体和游戏。我们在两个实证案例研究中讨论了Medialib的使用:与日本福冈九州大学的合作,以及为南丹麦大学媒体研究学生提供的编码模块。此外,我们使用概念机器来尝试比较编程学习工具的简单性,并根据我们的主张,即Medialib对学习者来说比其他流行的方法“更简单”。其主要贡献是一种结合了解释学螺旋和概念机器的解释学方法来简化特定环境下的编程。该方法由Medialib库这个工具支持;这两个案例研究提供了如何在CT课程的初学者中使用该方法和工具的示例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Simplifying Programming for Non-technical Students: A Hermeneutic Approach.

Simplifying Programming for Non-technical Students: A Hermeneutic Approach.

Simplifying Programming for Non-technical Students: A Hermeneutic Approach.

Simplifying Programming for Non-technical Students: A Hermeneutic Approach.

This paper investigates the simplification of programming for non-technical university students. Typical simplification strategies are outlined, and according to our findings CT courses for non-technical students typically address learners from different faculties, providing generic and basic knowledge, not specifically related to their major. In this study, we propose instead a hermeneutic approach to simplify programming, in which we aim at clarifying the problem-solving aspect of programming, addressing computational problems that are specific to their studies and leveraging on learners' preunderstanding of the digital media they have experienced as users. The practical counterpart of our theoretical approach is a minimalistic Python multimedia library, called Medialib, that we designed to enable university students with a non-technical profile to create visual media and games with short and readable code. We discuss the use of Medialib in two empirical case studies: a collaboration with the university of Kyushu in Fukuoka, Japan, and a coding module for Media Studies students at the University of Southern Denmark. Furthermore, we use Notional Machines to attempt a comparison of the simplicity of learning tools for programming, and to ground our claim that Medialib is "simpler" for learners than other popular approaches. The main contribution is a hermeneutic approach to the simplification of programming for specific contexts that combines the hermeneutic spiral and notional machines. The approach is supported by a tool, the Medialib library; the two case studies provide examples of how the approach and tool can be deployed in beginners in CT courses.

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来源期刊
Kunstliche Intelligenz
Kunstliche Intelligenz COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
8.60
自引率
3.40%
发文量
32
期刊介绍: Artificial Intelligence has successfully established itself as a scientific discipline in research and education and has become an integral part of Computer Science with an interdisciplinary character. AI deals with both the development of information processing systems that deliver “intelligent” services and with the modeling of human cognitive skills with the help of information processing systems. Research, development and applications in the field of AI pursue the general goal of creating processes for taking in and processing information that more closely resemble human problem-solving behavior, and to subsequently use those processes to derive methods that enhance and qualitatively improve conventional information processing systems. KI – Künstliche Intelligenz is the official journal of the division for artificial intelligence within the ''Gesellschaft für Informatik e.V.'' (GI) – the German Informatics Society – with contributions from the entire field of artificial intelligence. The journal presents fundamentals and tools, their use and adaptation for scientific purposes, and applications that are implemented using AI methods – and thus provides readers with the latest developments in and well-founded background information on all relevant aspects of artificial intelligence. A highly reputed team of editors from both university and industry will ensure the scientific quality of the articles.The journal provides all members of the AI community with quick access to current topics in the field, while also promoting vital interdisciplinary interchange, it will as well serve as a media of communication between the members of the division and the parent society. The journal is published in English. Content published in this journal is peer reviewed (Double Blind).
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