Bloom's taxonomy: A beneficial tool for learning and assessing students’ competency levels in computer programming using empirical analysis
Bloom's taxonomy: A beneficial tool for learning and assessing students’ competency levels in computer programming using empirical analysis
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布鲁姆分类法：使用实证分析学习和评估学生计算机编程能力水平的有益工具
DOI：
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发表时间：
2020
影响因子：
2.9
通讯作者：
Farrukh Saleem
中科院分区：
文献类型：
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作者：
Z. Ullah;Adidah Lajis;M. Jamjoom;A. Altalhi;Farrukh Saleem
以前的研究表明，大多数计算机科学专业的学生，尤其是新手，缺乏编程能力。这种不足的原因是大多数学生缺乏背景知识，编程的第一次经验，以及用特定语法语言编写程序的新环境等。由于这些原因，每年的失败率都很高。一些研究人员已经使用了学习分类法;在这方面，Bloom的分类法已被广泛用于评估和学习编程。此外，布卢姆的分类已被用作准备评估问题的规模，并在此基础上量化的能力水平。相比之下，本研究提出了一种新的编程评估方法，其中实现的能力水平的学生映射到各自的认知水平的布鲁姆的分类直接从书面代码没有事先映射的问题。能力水平的计算映射到各自的认知水平是基于一些主要的标准从以前的研究中使用的理论。此外，本研究强调了结构程序设计课程的基本主题：选择，重复和模块化。数据收集进行了从213名学生使用实证检验，进一步分析，通过结构方程模型。结果表明，布卢姆的分类法是一个有益的工具，学习和评估编程。
Previous research on computer programming advocates that most computer science students, especially novices, lack programming competencies. The reasons given for this inadequacy is that most students lack the background knowledge, first experience of programming, and a new environment of writing programs in a syntax specific language, and so forth. Due to these reasons, the failure rate is high every year. Several researchers have used learning taxonomies; in that, Bloom's taxonomy has been widely used for assessment and learning of programming. Moreover, Bloom's taxonomy has been used as a scale for preparing the assessment questions, and the competency level was quantified based on that. In contrast, this study proposes a novel approach of programming assessment, in which the achieved competency level of a student is mapped to the respective cognitive levels of Bloom's taxonomy directly from the written code with no prior mapping of questions. The computation of the competency level in terms of mapping to the respective cognitive level is based on some principal criteria extricated from theories used in previous studies. Furthermore, this study emphasizes the basic topics of the structure programming course: Selection, repetition, and modular. The data collection was carried out from 213 students using an empirical test that is further analyzed through Structural Equation Modeling. The results show that Bloom's taxonomy is a beneficial tool for learning and assessing programming.