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Discovery Model Based on Analogies for Teaching Computer Programming
Mathematics ( IF 2.3 ) Pub Date : 2021-06-11 , DOI: 10.3390/math9121354
Javier Alejandro Jiménez Toledo , César A. Collazos , Manuel Ortega

Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is the use of analogies to support the abstraction process that a student needs to carry for the appropriation of fundamental concepts. This article presents the results of applying a discovery model that allowed for the extraction of patterns, linguistic analysis, textual analytics, and linked data when using analogies for teaching the fundamental concepts of programming by professors in a CS1 in university programs that train software developers. For that reason, a discovery model based on machine learning and text mining was proposed using natural language processing techniques for semantic vector space modeling, distributional semantics, and the generation of synthetic data. The discovery process was carried out using nine supervised learning methods, three unsupervised learning methods, and one semi-supervised learning method involving linguistic analysis techniques, text analytics, and linked data. The main findings showed that professors include keywords, which are part of the technical computer terminology, in the form of verbs in the statement of the analogy and combine them in quantitative contexts with neutral or positive phrases, where numerical examples, cooking recipes, and games were the most used categories. Finally, a structure is proposed for the construction of analogies to teach programming concepts and this was validated by the professors and students.

中文翻译:

基于类比​​的计算机程序设计教学探索模型

在第一门课程 (CS1) 中教授计算机编程的基础知识对教授来说是一项复杂的活动,对他们来说也是一个挑战。现在,在大学里有几种处理 CS1 的教学策略,其中之一是使用类比来支持学生为挪用基本概念而需要进行的抽象过程。本文介绍了应用发现模型的结果,该模型允许在培训软件开发人员的大学课程中使用类比来教授 CS1 教授编程的基本概念时提取模式、语言分析、文本分析和链接数据。是因为,提出了一种基于机器学习和文本挖掘的发现模型,使用自然语言处理技术进行语义向量空间建模、分布式语义和合成数据的生成。发现过程使用九种监督学习方法、三种无监督学习方法和一种涉及语言分析技术、文本分析和链接数据的半监督学习方法进行。主要研究结果表明,教授在类比陈述中以动词形式包含作为计算机技术术语一部分的关键字,并将它们与中性或积极短语在定量上下文中结合起来,其中数字示例、烹饪食谱和游戏是最常用的类别。最后,
更新日期:2021-06-11
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