Abstract
In evaluating the learning achievement of programming-thinking skills, the method of using a rubric that describes evaluation items and evaluation stages is widely employed. However, few studies have evaluated the reliability, validity, and consistency of the rubrics themselves. In this study, we introduced a statistical method for evaluating the characteristics of rubrics using the goal question metric (GQM) method. Furthermore, we proposed a method for measuring four evaluation results and characteristics obtained from rubrics developed using this statistical method. Moreover, we showed and confirmed the consistency and validity of the statistical method using the GQM method of the resulting developed rubrics. We show how to verify the consistency and validity of the rubric using the GQM method.
Highlights
Systematic evaluation of the learning achievement of programming-thinking skills for young people is important in programming education
A rubric is a method for evaluating learning achievement that enables a step-by-step evaluation by setting evaluation criteria and evaluation items
RQ1: How do we identify statistical methods to assess the properties of rubrics?
Summary
Systematic evaluation of the learning achievement of programming-thinking skills for young people is important in programming education. Multiple evaluation criteria have been considered as methods for evaluating programming-thinking skills. Rubrics have been proposed as a method for evaluating the learning achievement of programming-thinking skills [1,2,3,4]. A rubric is a method for evaluating learning achievement that enables a step-by-step evaluation by setting evaluation criteria and evaluation items. There are rubrics for simple multiple-choice tests in classes, rubrics for free programming tasks created by students, and rubrics that automate the evaluation itself. Few studies have considered the characteristics of these rubrics themselves and have evaluated and discussed them. We examined the characteristics of a rubric to evaluate achievements in programming education and developed a statistical method to quantitatively evaluate it. We evaluated and analyzed rubrics and the results of the evaluation of programming education in those rubrics
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